short article about technology in education

REALIZING THE PROMISE:

Leading up to the 75th anniversary of the UN General Assembly, this “Realizing the promise: How can education technology improve learning for all?” publication kicks off the Center for Universal Education’s first playbook in a series to help improve education around the world.

It is intended as an evidence-based tool for ministries of education, particularly in low- and middle-income countries, to adopt and more successfully invest in education technology.

While there is no single education initiative that will achieve the same results everywhere—as school systems differ in learners and educators, as well as in the availability and quality of materials and technologies—an important first step is understanding how technology is used given specific local contexts and needs.

The surveys in this playbook are designed to be adapted to collect this information from educators, learners, and school leaders and guide decisionmakers in expanding the use of technology.  

Introduction

While technology has disrupted most sectors of the economy and changed how we communicate, access information, work, and even play, its impact on schools, teaching, and learning has been much more limited. We believe that this limited impact is primarily due to technology being been used to replace analog tools, without much consideration given to playing to technology’s comparative advantages. These comparative advantages, relative to traditional “chalk-and-talk” classroom instruction, include helping to scale up standardized instruction, facilitate differentiated instruction, expand opportunities for practice, and increase student engagement. When schools use technology to enhance the work of educators and to improve the quality and quantity of educational content, learners will thrive.

Further, COVID-19 has laid bare that, in today’s environment where pandemics and the effects of climate change are likely to occur, schools cannot always provide in-person education—making the case for investing in education technology.

Here we argue for a simple yet surprisingly rare approach to education technology that seeks to:

• Understand the needs, infrastructure, and capacity of a school system—the diagnosis;
• Survey the best available evidence on interventions that match those conditions—the evidence; and
• Closely monitor the results of innovations before they are scaled up—the prognosis.

RELATED CONTENT

Podcast: How education technology can improve learning for all students

To make ed tech work, set clear goals, review the evidence, and pilot before you scale

The framework.

Our approach builds on a simple yet intuitive theoretical framework created two decades ago by two of the most prominent education researchers in the United States, David K. Cohen and Deborah Loewenberg Ball. They argue that what matters most to improve learning is the interactions among educators and learners around educational materials. We believe that the failed school-improvement efforts in the U.S. that motivated Cohen and Ball’s framework resemble the ed-tech reforms in much of the developing world to date in the lack of clarity improving the interactions between educators, learners, and the educational material. We build on their framework by adding parents as key agents that mediate the relationships between learners and educators and the material (Figure 1).

Figure 1: The instructional core

Adapted from Cohen and Ball (1999)

As the figure above suggests, ed-tech interventions can affect the instructional core in a myriad of ways. Yet, just because technology can do something, it does not mean it should. School systems in developing countries differ along many dimensions and each system is likely to have different needs for ed-tech interventions, as well as different infrastructure and capacity to enact such interventions.

The diagnosis:

How can school systems assess their needs and preparedness.

A useful first step for any school system to determine whether it should invest in education technology is to diagnose its:

• Specific needs to improve student learning (e.g., raising the average level of achievement, remediating gaps among low performers, and challenging high performers to develop higher-order skills);
• Infrastructure to adopt technology-enabled solutions (e.g., electricity connection, availability of space and outlets, stock of computers, and Internet connectivity at school and at learners’ homes); and
• Capacity to integrate technology in the instructional process (e.g., learners’ and educators’ level of familiarity and comfort with hardware and software, their beliefs about the level of usefulness of technology for learning purposes, and their current uses of such technology).

Before engaging in any new data collection exercise, school systems should take full advantage of existing administrative data that could shed light on these three main questions. This could be in the form of internal evaluations but also international learner assessments, such as the Program for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), and/or the Progress in International Literacy Study (PIRLS), and the Teaching and Learning International Study (TALIS). But if school systems lack information on their preparedness for ed-tech reforms or if they seek to complement existing data with a richer set of indicators, we developed a set of surveys for learners, educators, and school leaders. Download the full report to see how we map out the main aspects covered by these surveys, in hopes of highlighting how they could be used to inform decisions around the adoption of ed-tech interventions.

The evidence:

How can school systems identify promising ed-tech interventions.

There is no single “ed-tech” initiative that will achieve the same results everywhere, simply because school systems differ in learners and educators, as well as in the availability and quality of materials and technologies. Instead, to realize the potential of education technology to accelerate student learning, decisionmakers should focus on four potential uses of technology that play to its comparative advantages and complement the work of educators to accelerate student learning (Figure 2). These comparative advantages include:

• Scaling up quality instruction, such as through prerecorded quality lessons.
• Facilitating differentiated instruction, through, for example, computer-adaptive learning and live one-on-one tutoring.
• Expanding opportunities to practice.
• Increasing learner engagement through videos and games.

Figure 2: Comparative advantages of technology

Here we review the evidence on ed-tech interventions from 37 studies in 20 countries*, organizing them by comparative advantage. It’s important to note that ours is not the only way to classify these interventions (e.g., video tutorials could be considered as a strategy to scale up instruction or increase learner engagement), but we believe it may be useful to highlight the needs that they could address and why technology is well positioned to do so.

When discussing specific studies, we report the magnitude of the effects of interventions using standard deviations (SDs). SDs are a widely used metric in research to express the effect of a program or policy with respect to a business-as-usual condition (e.g., test scores). There are several ways to make sense of them. One is to categorize the magnitude of the effects based on the results of impact evaluations. In developing countries, effects below 0.1 SDs are considered to be small, effects between 0.1 and 0.2 SDs are medium, and those above 0.2 SDs are large (for reviews that estimate the average effect of groups of interventions, called “meta analyses,” see e.g., Conn, 2017; Kremer, Brannen, & Glennerster, 2013; McEwan, 2014; Snilstveit et al., 2015; Evans & Yuan, 2020.)

*In surveying the evidence, we began by compiling studies from prior general and ed-tech specific evidence reviews that some of us have written and from ed-tech reviews conducted by others. Then, we tracked the studies cited by the ones we had previously read and reviewed those, as well. In identifying studies for inclusion, we focused on experimental and quasi-experimental evaluations of education technology interventions from pre-school to secondary school in low- and middle-income countries that were released between 2000 and 2020. We only included interventions that sought to improve student learning directly (i.e., students’ interaction with the material), as opposed to interventions that have impacted achievement indirectly, by reducing teacher absence or increasing parental engagement. This process yielded 37 studies in 20 countries (see the full list of studies in Appendix B).

Scaling up standardized instruction

One of the ways in which technology may improve the quality of education is through its capacity to deliver standardized quality content at scale. This feature of technology may be particularly useful in three types of settings: (a) those in “hard-to-staff” schools (i.e., schools that struggle to recruit educators with the requisite training and experience—typically, in rural and/or remote areas) (see, e.g., Urquiola & Vegas, 2005); (b) those in which many educators are frequently absent from school (e.g., Chaudhury, Hammer, Kremer, Muralidharan, & Rogers, 2006; Muralidharan, Das, Holla, & Mohpal, 2017); and/or (c) those in which educators have low levels of pedagogical and subject matter expertise (e.g., Bietenbeck, Piopiunik, & Wiederhold, 2018; Bold et al., 2017; Metzler & Woessmann, 2012; Santibañez, 2006) and do not have opportunities to observe and receive feedback (e.g., Bruns, Costa, & Cunha, 2018; Cilliers, Fleisch, Prinsloo, & Taylor, 2018). Technology could address this problem by: (a) disseminating lessons delivered by qualified educators to a large number of learners (e.g., through prerecorded or live lessons); (b) enabling distance education (e.g., for learners in remote areas and/or during periods of school closures); and (c) distributing hardware preloaded with educational materials.

Prerecorded lessons

Technology seems to be well placed to amplify the impact of effective educators by disseminating their lessons. Evidence on the impact of prerecorded lessons is encouraging, but not conclusive. Some initiatives that have used short instructional videos to complement regular instruction, in conjunction with other learning materials, have raised student learning on independent assessments. For example, Beg et al. (2020) evaluated an initiative in Punjab, Pakistan in which grade 8 classrooms received an intervention that included short videos to substitute live instruction, quizzes for learners to practice the material from every lesson, tablets for educators to learn the material and follow the lesson, and LED screens to project the videos onto a classroom screen. After six months, the intervention improved the performance of learners on independent tests of math and science by 0.19 and 0.24 SDs, respectively but had no discernible effect on the math and science section of Punjab’s high-stakes exams.

One study suggests that approaches that are far less technologically sophisticated can also improve learning outcomes—especially, if the business-as-usual instruction is of low quality. For example, Naslund-Hadley, Parker, and Hernandez-Agramonte (2014) evaluated a preschool math program in Cordillera, Paraguay that used audio segments and written materials four days per week for an hour per day during the school day. After five months, the intervention improved math scores by 0.16 SDs, narrowing gaps between low- and high-achieving learners, and between those with and without educators with formal training in early childhood education.

Yet, the integration of prerecorded material into regular instruction has not always been successful. For example, de Barros (2020) evaluated an intervention that combined instructional videos for math and science with infrastructure upgrades (e.g., two “smart” classrooms, two TVs, and two tablets), printed workbooks for students, and in-service training for educators of learners in grades 9 and 10 in Haryana, India (all materials were mapped onto the official curriculum). After 11 months, the intervention negatively impacted math achievement (by 0.08 SDs) and had no effect on science (with respect to business as usual classes). It reduced the share of lesson time that educators devoted to instruction and negatively impacted an index of instructional quality. Likewise, Seo (2017) evaluated several combinations of infrastructure (solar lights and TVs) and prerecorded videos (in English and/or bilingual) for grade 11 students in northern Tanzania and found that none of the variants improved student learning, even when the videos were used. The study reports effects from the infrastructure component across variants, but as others have noted (Muralidharan, Romero, & Wüthrich, 2019), this approach to estimating impact is problematic.

A very similar intervention delivered after school hours, however, had sizeable effects on learners’ basic skills. Chiplunkar, Dhar, and Nagesh (2020) evaluated an initiative in Chennai (the capital city of the state of Tamil Nadu, India) delivered by the same organization as above that combined short videos that explained key concepts in math and science with worksheets, facilitator-led instruction, small groups for peer-to-peer learning, and occasional career counseling and guidance for grade 9 students. These lessons took place after school for one hour, five times a week. After 10 months, it had large effects on learners’ achievement as measured by tests of basic skills in math and reading, but no effect on a standardized high-stakes test in grade 10 or socio-emotional skills (e.g., teamwork, decisionmaking, and communication).

Drawing general lessons from this body of research is challenging for at least two reasons. First, all of the studies above have evaluated the impact of prerecorded lessons combined with several other components (e.g., hardware, print materials, or other activities). Therefore, it is possible that the effects found are due to these additional components, rather than to the recordings themselves, or to the interaction between the two (see Muralidharan, 2017 for a discussion of the challenges of interpreting “bundled” interventions). Second, while these studies evaluate some type of prerecorded lessons, none examines the content of such lessons. Thus, it seems entirely plausible that the direction and magnitude of the effects depends largely on the quality of the recordings (e.g., the expertise of the educator recording it, the amount of preparation that went into planning the recording, and its alignment with best teaching practices).

These studies also raise three important questions worth exploring in future research. One of them is why none of the interventions discussed above had effects on high-stakes exams, even if their materials are typically mapped onto the official curriculum. It is possible that the official curricula are simply too challenging for learners in these settings, who are several grade levels behind expectations and who often need to reinforce basic skills (see Pritchett & Beatty, 2015). Another question is whether these interventions have long-term effects on teaching practices. It seems plausible that, if these interventions are deployed in contexts with low teaching quality, educators may learn something from watching the videos or listening to the recordings with learners. Yet another question is whether these interventions make it easier for schools to deliver instruction to learners whose native language is other than the official medium of instruction.

Distance education

Technology can also allow learners living in remote areas to access education. The evidence on these initiatives is encouraging. For example, Johnston and Ksoll (2017) evaluated a program that broadcasted live instruction via satellite to rural primary school students in the Volta and Greater Accra regions of Ghana. For this purpose, the program also equipped classrooms with the technology needed to connect to a studio in Accra, including solar panels, a satellite modem, a projector, a webcam, microphones, and a computer with interactive software. After two years, the intervention improved the numeracy scores of students in grades 2 through 4, and some foundational literacy tasks, but it had no effect on attendance or classroom time devoted to instruction, as captured by school visits. The authors interpreted these results as suggesting that the gains in achievement may be due to improving the quality of instruction that children received (as opposed to increased instructional time). Naik, Chitre, Bhalla, and Rajan (2019) evaluated a similar program in the Indian state of Karnataka and also found positive effects on learning outcomes, but it is not clear whether those effects are due to the program or due to differences in the groups of students they compared to estimate the impact of the initiative.

In one context (Mexico), this type of distance education had positive long-term effects. Navarro-Sola (2019) took advantage of the staggered rollout of the telesecundarias (i.e., middle schools with lessons broadcasted through satellite TV) in 1968 to estimate its impact. The policy had short-term effects on students’ enrollment in school: For every telesecundaria per 50 children, 10 students enrolled in middle school and two pursued further education. It also had a long-term influence on the educational and employment trajectory of its graduates. Each additional year of education induced by the policy increased average income by nearly 18 percent. This effect was attributable to more graduates entering the labor force and shifting from agriculture and the informal sector. Similarly, Fabregas (2019) leveraged a later expansion of this policy in 1993 and found that each additional telesecundaria per 1,000 adolescents led to an average increase of 0.2 years of education, and a decline in fertility for women, but no conclusive evidence of long-term effects on labor market outcomes.

It is crucial to interpret these results keeping in mind the settings where the interventions were implemented. As we mention above, part of the reason why they have proven effective is that the “counterfactual” conditions for learning (i.e., what would have happened to learners in the absence of such programs) was either to not have access to schooling or to be exposed to low-quality instruction. School systems interested in taking up similar interventions should assess the extent to which their learners (or parts of their learner population) find themselves in similar conditions to the subjects of the studies above. This illustrates the importance of assessing the needs of a system before reviewing the evidence.

Preloaded hardware

Technology also seems well positioned to disseminate educational materials. Specifically, hardware (e.g., desktop computers, laptops, or tablets) could also help deliver educational software (e.g., word processing, reference texts, and/or games). In theory, these materials could not only undergo a quality assurance review (e.g., by curriculum specialists and educators), but also draw on the interactions with learners for adjustments (e.g., identifying areas needing reinforcement) and enable interactions between learners and educators.

In practice, however, most initiatives that have provided learners with free computers, laptops, and netbooks do not leverage any of the opportunities mentioned above. Instead, they install a standard set of educational materials and hope that learners find them helpful enough to take them up on their own. Students rarely do so, and instead use the laptops for recreational purposes—often, to the detriment of their learning (see, e.g., Malamud & Pop-Eleches, 2011). In fact, free netbook initiatives have not only consistently failed to improve academic achievement in math or language (e.g., Cristia et al., 2017), but they have had no impact on learners’ general computer skills (e.g., Beuermann et al., 2015). Some of these initiatives have had small impacts on cognitive skills, but the mechanisms through which those effects occurred remains unclear.

To our knowledge, the only successful deployment of a free laptop initiative was one in which a team of researchers equipped the computers with remedial software. Mo et al. (2013) evaluated a version of the One Laptop per Child (OLPC) program for grade 3 students in migrant schools in Beijing, China in which the laptops were loaded with a remedial software mapped onto the national curriculum for math (similar to the software products that we discuss under “practice exercises” below). After nine months, the program improved math achievement by 0.17 SDs and computer skills by 0.33 SDs. If a school system decides to invest in free laptops, this study suggests that the quality of the software on the laptops is crucial.

To date, however, the evidence suggests that children do not learn more from interacting with laptops than they do from textbooks. For example, Bando, Gallego, Gertler, and Romero (2016) compared the effect of free laptop and textbook provision in 271 elementary schools in disadvantaged areas of Honduras. After seven months, students in grades 3 and 6 who had received the laptops performed on par with those who had received the textbooks in math and language. Further, even if textbooks essentially become obsolete at the end of each school year, whereas laptops can be reloaded with new materials for each year, the costs of laptop provision (not just the hardware, but also the technical assistance, Internet, and training associated with it) are not yet low enough to make them a more cost-effective way of delivering content to learners.

Evidence on the provision of tablets equipped with software is encouraging but limited. For example, de Hoop et al. (2020) evaluated a composite intervention for first grade students in Zambia’s Eastern Province that combined infrastructure (electricity via solar power), hardware (projectors and tablets), and educational materials (lesson plans for educators and interactive lessons for learners, both loaded onto the tablets and mapped onto the official Zambian curriculum). After 14 months, the intervention had improved student early-grade reading by 0.4 SDs, oral vocabulary scores by 0.25 SDs, and early-grade math by 0.22 SDs. It also improved students’ achievement by 0.16 on a locally developed assessment. The multifaceted nature of the program, however, makes it challenging to identify the components that are driving the positive effects. Pitchford (2015) evaluated an intervention that provided tablets equipped with educational “apps,” to be used for 30 minutes per day for two months to develop early math skills among students in grades 1 through 3 in Lilongwe, Malawi. The evaluation found positive impacts in math achievement, but the main study limitation is that it was conducted in a single school.

Facilitating differentiated instruction

Another way in which technology may improve educational outcomes is by facilitating the delivery of differentiated or individualized instruction. Most developing countries massively expanded access to schooling in recent decades by building new schools and making education more affordable, both by defraying direct costs, as well as compensating for opportunity costs (Duflo, 2001; World Bank, 2018). These initiatives have not only rapidly increased the number of learners enrolled in school, but have also increased the variability in learner’ preparation for schooling. Consequently, a large number of learners perform well below grade-based curricular expectations (see, e.g., Duflo, Dupas, & Kremer, 2011; Pritchett & Beatty, 2015). These learners are unlikely to get much from “one-size-fits-all” instruction, in which a single educator delivers instruction deemed appropriate for the middle (or top) of the achievement distribution (Banerjee & Duflo, 2011). Technology could potentially help these learners by providing them with: (a) instruction and opportunities for practice that adjust to the level and pace of preparation of each individual (known as “computer-adaptive learning” (CAL)); or (b) live, one-on-one tutoring.

Computer-adaptive learning

One of the main comparative advantages of technology is its ability to diagnose students’ initial learning levels and assign students to instruction and exercises of appropriate difficulty. No individual educator—no matter how talented—can be expected to provide individualized instruction to all learners in his/her class simultaneously . In this respect, technology is uniquely positioned to complement traditional teaching. This use of technology could help learners master basic skills and help them get more out of schooling.

Although many software products evaluated in recent years have been categorized as CAL, many rely on a relatively coarse level of differentiation at an initial stage (e.g., a diagnostic test) without further differentiation. We discuss these initiatives under the category of “increasing opportunities for practice” below. CAL initiatives complement an initial diagnostic with dynamic adaptation (i.e., at each response or set of responses from learners) to adjust both the initial level of difficulty and rate at which it increases or decreases, depending on whether learners’ responses are correct or incorrect.

Existing evidence on this specific type of programs is highly promising. Most famously, Banerjee et al. (2007) evaluated CAL software in Vadodara, in the Indian state of Gujarat, in which grade 4 students were offered two hours of shared computer time per week before and after school, during which they played games that involved solving math problems. The level of difficulty of such problems adjusted based on students’ answers. This program improved math achievement by 0.35 and 0.47 SDs after one and two years of implementation, respectively. Consistent with the promise of personalized learning, the software improved achievement for all students. In fact, one year after the end of the program, students assigned to the program still performed 0.1 SDs better than those assigned to a business as usual condition. More recently, Muralidharan, et al. (2019) evaluated a “blended learning” initiative in which students in grades 4 through 9 in Delhi, India received 45 minutes of interaction with CAL software for math and language, and 45 minutes of small group instruction before or after going to school. After only 4.5 months, the program improved achievement by 0.37 SDs in math and 0.23 SDs in Hindi. While all learners benefited from the program in absolute terms, the lowest performing learners benefited the most in relative terms, since they were learning very little in school.

We see two important limitations from this body of research. First, to our knowledge, none of these initiatives has been evaluated when implemented during the school day. Therefore, it is not possible to distinguish the effect of the adaptive software from that of additional instructional time. Second, given that most of these programs were facilitated by local instructors, attempts to distinguish the effect of the software from that of the instructors has been mostly based on noncausal evidence. A frontier challenge in this body of research is to understand whether CAL software can increase the effectiveness of school-based instruction by substituting part of the regularly scheduled time for math and language instruction.

Live one-on-one tutoring

Recent improvements in the speed and quality of videoconferencing, as well as in the connectivity of remote areas, have enabled yet another way in which technology can help personalization: live (i.e., real-time) one-on-one tutoring. While the evidence on in-person tutoring is scarce in developing countries, existing studies suggest that this approach works best when it is used to personalize instruction (see, e.g., Banerjee et al., 2007; Banerji, Berry, & Shotland, 2015; Cabezas, Cuesta, & Gallego, 2011).

There are almost no studies on the impact of online tutoring—possibly, due to the lack of hardware and Internet connectivity in low- and middle-income countries. One exception is Chemin and Oledan (2020)’s recent evaluation of an online tutoring program for grade 6 students in Kianyaga, Kenya to learn English from volunteers from a Canadian university via Skype ( videoconferencing software) for one hour per week after school. After 10 months, program beneficiaries performed 0.22 SDs better in a test of oral comprehension, improved their comfort using technology for learning, and became more willing to engage in cross-cultural communication. Importantly, while the tutoring sessions used the official English textbooks and sought in part to help learners with their homework, tutors were trained on several strategies to teach to each learner’s individual level of preparation, focusing on basic skills if necessary. To our knowledge, similar initiatives within a country have not yet been rigorously evaluated.

Expanding opportunities for practice

A third way in which technology may improve the quality of education is by providing learners with additional opportunities for practice. In many developing countries, lesson time is primarily devoted to lectures, in which the educator explains the topic and the learners passively copy explanations from the blackboard. This setup leaves little time for in-class practice. Consequently, learners who did not understand the explanation of the material during lecture struggle when they have to solve homework assignments on their own. Technology could potentially address this problem by allowing learners to review topics at their own pace.

Practice exercises

Technology can help learners get more out of traditional instruction by providing them with opportunities to implement what they learn in class. This approach could, in theory, allow some learners to anchor their understanding of the material through trial and error (i.e., by realizing what they may not have understood correctly during lecture and by getting better acquainted with special cases not covered in-depth in class).

Existing evidence on practice exercises reflects both the promise and the limitations of this use of technology in developing countries. For example, Lai et al. (2013) evaluated a program in Shaanxi, China where students in grades 3 and 5 were required to attend two 40-minute remedial sessions per week in which they first watched videos that reviewed the material that had been introduced in their math lessons that week and then played games to practice the skills introduced in the video. After four months, the intervention improved math achievement by 0.12 SDs. Many other evaluations of comparable interventions have found similar small-to-moderate results (see, e.g., Lai, Luo, Zhang, Huang, & Rozelle, 2015; Lai et al., 2012; Mo et al., 2015; Pitchford, 2015). These effects, however, have been consistently smaller than those of initiatives that adjust the difficulty of the material based on students’ performance (e.g., Banerjee et al., 2007; Muralidharan, et al., 2019). We hypothesize that these programs do little for learners who perform several grade levels behind curricular expectations, and who would benefit more from a review of foundational concepts from earlier grades.

We see two important limitations from this research. First, most initiatives that have been evaluated thus far combine instructional videos with practice exercises, so it is hard to know whether their effects are driven by the former or the latter. In fact, the program in China described above allowed learners to ask their peers whenever they did not understand a difficult concept, so it potentially also captured the effect of peer-to-peer collaboration. To our knowledge, no studies have addressed this gap in the evidence.

Second, most of these programs are implemented before or after school, so we cannot distinguish the effect of additional instructional time from that of the actual opportunity for practice. The importance of this question was first highlighted by Linden (2008), who compared two delivery mechanisms for game-based remedial math software for students in grades 2 and 3 in a network of schools run by a nonprofit organization in Gujarat, India: one in which students interacted with the software during the school day and another one in which students interacted with the software before or after school (in both cases, for three hours per day). After a year, the first version of the program had negatively impacted students’ math achievement by 0.57 SDs and the second one had a null effect. This study suggested that computer-assisted learning is a poor substitute for regular instruction when it is of high quality, as was the case in this well-functioning private network of schools.

In recent years, several studies have sought to remedy this shortcoming. Mo et al. (2014) were among the first to evaluate practice exercises delivered during the school day. They evaluated an initiative in Shaanxi, China in which students in grades 3 and 5 were required to interact with the software similar to the one in Lai et al. (2013) for two 40-minute sessions per week. The main limitation of this study, however, is that the program was delivered during regularly scheduled computer lessons, so it could not determine the impact of substituting regular math instruction. Similarly, Mo et al. (2020) evaluated a self-paced and a teacher-directed version of a similar program for English for grade 5 students in Qinghai, China. Yet, the key shortcoming of this study is that the teacher-directed version added several components that may also influence achievement, such as increased opportunities for teachers to provide students with personalized assistance when they struggled with the material. Ma, Fairlie, Loyalka, and Rozelle (2020) compared the effectiveness of additional time-delivered remedial instruction for students in grades 4 to 6 in Shaanxi, China through either computer-assisted software or using workbooks. This study indicates whether additional instructional time is more effective when using technology, but it does not address the question of whether school systems may improve the productivity of instructional time during the school day by substituting educator-led with computer-assisted instruction.

Increasing learner engagement

Another way in which technology may improve education is by increasing learners’ engagement with the material. In many school systems, regular “chalk and talk” instruction prioritizes time for educators’ exposition over opportunities for learners to ask clarifying questions and/or contribute to class discussions. This, combined with the fact that many developing-country classrooms include a very large number of learners (see, e.g., Angrist & Lavy, 1999; Duflo, Dupas, & Kremer, 2015), may partially explain why the majority of those students are several grade levels behind curricular expectations (e.g., Muralidharan, et al., 2019; Muralidharan & Zieleniak, 2014; Pritchett & Beatty, 2015). Technology could potentially address these challenges by: (a) using video tutorials for self-paced learning and (b) presenting exercises as games and/or gamifying practice.

Video tutorials

Technology can potentially increase learner effort and understanding of the material by finding new and more engaging ways to deliver it. Video tutorials designed for self-paced learning—as opposed to videos for whole class instruction, which we discuss under the category of “prerecorded lessons” above—can increase learner effort in multiple ways, including: allowing learners to focus on topics with which they need more help, letting them correct errors and misconceptions on their own, and making the material appealing through visual aids. They can increase understanding by breaking the material into smaller units and tackling common misconceptions.

In spite of the popularity of instructional videos, there is relatively little evidence on their effectiveness. Yet, two recent evaluations of different versions of the Khan Academy portal, which mainly relies on instructional videos, offer some insight into their impact. First, Ferman, Finamor, and Lima (2019) evaluated an initiative in 157 public primary and middle schools in five cities in Brazil in which the teachers of students in grades 5 and 9 were taken to the computer lab to learn math from the platform for 50 minutes per week. The authors found that, while the intervention slightly improved learners’ attitudes toward math, these changes did not translate into better performance in this subject. The authors hypothesized that this could be due to the reduction of teacher-led math instruction.

More recently, Büchel, Jakob, Kühnhanss, Steffen, and Brunetti (2020) evaluated an after-school, offline delivery of the Khan Academy portal in grades 3 through 6 in 302 primary schools in Morazán, El Salvador. Students in this study received 90 minutes per week of additional math instruction (effectively nearly doubling total math instruction per week) through teacher-led regular lessons, teacher-assisted Khan Academy lessons, or similar lessons assisted by technical supervisors with no content expertise. (Importantly, the first group provided differentiated instruction, which is not the norm in Salvadorian schools). All three groups outperformed both schools without any additional lessons and classrooms without additional lessons in the same schools as the program. The teacher-assisted Khan Academy lessons performed 0.24 SDs better, the supervisor-led lessons 0.22 SDs better, and the teacher-led regular lessons 0.15 SDs better, but the authors could not determine whether the effects across versions were different.

Together, these studies suggest that instructional videos work best when provided as a complement to, rather than as a substitute for, regular instruction. Yet, the main limitation of these studies is the multifaceted nature of the Khan Academy portal, which also includes other components found to positively improve learner achievement, such as differentiated instruction by students’ learning levels. While the software does not provide the type of personalization discussed above, learners are asked to take a placement test and, based on their score, educators assign them different work. Therefore, it is not clear from these studies whether the effects from Khan Academy are driven by its instructional videos or to the software’s ability to provide differentiated activities when combined with placement tests.

Games and gamification

Technology can also increase learner engagement by presenting exercises as games and/or by encouraging learner to play and compete with others (e.g., using leaderboards and rewards)—an approach known as “gamification.” Both approaches can increase learner motivation and effort by presenting learners with entertaining opportunities for practice and by leveraging peers as commitment devices.

There are very few studies on the effects of games and gamification in low- and middle-income countries. Recently, Araya, Arias Ortiz, Bottan, and Cristia (2019) evaluated an initiative in which grade 4 students in Santiago, Chile were required to participate in two 90-minute sessions per week during the school day with instructional math software featuring individual and group competitions (e.g., tracking each learner’s standing in his/her class and tournaments between sections). After nine months, the program led to improvements of 0.27 SDs in the national student assessment in math (it had no spillover effects on reading). However, it had mixed effects on non-academic outcomes. Specifically, the program increased learners’ willingness to use computers to learn math, but, at the same time, increased their anxiety toward math and negatively impacted learners’ willingness to collaborate with peers. Finally, given that one of the weekly sessions replaced regular math instruction and the other one represented additional math instructional time, it is not clear whether the academic effects of the program are driven by the software or the additional time devoted to learning math.

The prognosis:

How can school systems adopt interventions that match their needs.

Here are five specific and sequential guidelines for decisionmakers to realize the potential of education technology to accelerate student learning.

1. Take stock of how your current schools, educators, and learners are engaging with technology .

Carry out a short in-school survey to understand the current practices and potential barriers to adoption of technology (we have included suggested survey instruments in the Appendices); use this information in your decisionmaking process. For example, we learned from conversations with current and former ministers of education from various developing regions that a common limitation to technology use is regulations that hold school leaders accountable for damages to or losses of devices. Another common barrier is lack of access to electricity and Internet, or even the availability of sufficient outlets for charging devices in classrooms. Understanding basic infrastructure and regulatory limitations to the use of education technology is a first necessary step. But addressing these limitations will not guarantee that introducing or expanding technology use will accelerate learning. The next steps are thus necessary.

“In Africa, the biggest limit is connectivity. Fiber is expensive, and we don’t have it everywhere. The continent is creating a digital divide between cities, where there is fiber, and the rural areas.  The [Ghanaian] administration put in schools offline/online technologies with books, assessment tools, and open source materials. In deploying this, we are finding that again, teachers are unfamiliar with it. And existing policies prohibit students to bring their own tablets or cell phones. The easiest way to do it would have been to let everyone bring their own device. But policies are against it.” H.E. Matthew Prempeh, Minister of Education of Ghana, on the need to understand the local context.

2. Consider how the introduction of technology may affect the interactions among learners, educators, and content .

Our review of the evidence indicates that technology may accelerate student learning when it is used to scale up access to quality content, facilitate differentiated instruction, increase opportunities for practice, or when it increases learner engagement. For example, will adding electronic whiteboards to classrooms facilitate access to more quality content or differentiated instruction? Or will these expensive boards be used in the same way as the old chalkboards? Will providing one device (laptop or tablet) to each learner facilitate access to more and better content, or offer students more opportunities to practice and learn? Solely introducing technology in classrooms without additional changes is unlikely to lead to improved learning and may be quite costly. If you cannot clearly identify how the interactions among the three key components of the instructional core (educators, learners, and content) may change after the introduction of technology, then it is probably not a good idea to make the investment. See Appendix A for guidance on the types of questions to ask.

3. Once decisionmakers have a clear idea of how education technology can help accelerate student learning in a specific context, it is important to define clear objectives and goals and establish ways to regularly assess progress and make course corrections in a timely manner .

For instance, is the education technology expected to ensure that learners in early grades excel in foundational skills—basic literacy and numeracy—by age 10? If so, will the technology provide quality reading and math materials, ample opportunities to practice, and engaging materials such as videos or games? Will educators be empowered to use these materials in new ways? And how will progress be measured and adjusted?

4. How this kind of reform is approached can matter immensely for its success.

It is easy to nod to issues of “implementation,” but that needs to be more than rhetorical. Keep in mind that good use of education technology requires thinking about how it will affect learners, educators, and parents. After all, giving learners digital devices will make no difference if they get broken, are stolen, or go unused. Classroom technologies only matter if educators feel comfortable putting them to work. Since good technology is generally about complementing or amplifying what educators and learners already do, it is almost always a mistake to mandate programs from on high. It is vital that technology be adopted with the input of educators and families and with attention to how it will be used. If technology goes unused or if educators use it ineffectually, the results will disappoint—no matter the virtuosity of the technology. Indeed, unused education technology can be an unnecessary expenditure for cash-strapped education systems. This is why surveying context, listening to voices in the field, examining how technology is used, and planning for course correction is essential.

5. It is essential to communicate with a range of stakeholders, including educators, school leaders, parents, and learners .

Technology can feel alien in schools, confuse parents and (especially) older educators, or become an alluring distraction. Good communication can help address all of these risks. Taking care to listen to educators and families can help ensure that programs are informed by their needs and concerns. At the same time, deliberately and consistently explaining what technology is and is not supposed to do, how it can be most effectively used, and the ways in which it can make it more likely that programs work as intended. For instance, if teachers fear that technology is intended to reduce the need for educators, they will tend to be hostile; if they believe that it is intended to assist them in their work, they will be more receptive. Absent effective communication, it is easy for programs to “fail” not because of the technology but because of how it was used. In short, past experience in rolling out education programs indicates that it is as important to have a strong intervention design as it is to have a solid plan to socialize it among stakeholders.

Beyond reopening: A leapfrog moment to transform education?

On September 14, the Center for Universal Education (CUE) will host a webinar to discuss strategies, including around the effective use of education technology, for ensuring resilient schools in the long term and to launch a new education technology playbook “Realizing the promise: How can education technology improve learning for all?”

file-pdf Full Playbook – Realizing the promise: How can education technology improve learning for all? file-pdf References file-pdf Appendix A – Instruments to assess availability and use of technology file-pdf Appendix B – List of reviewed studies file-pdf Appendix C – How may technology affect interactions among students, teachers, and content?

About the Authors

Alejandro j. ganimian, emiliana vegas, frederick m. hess.

• Media Relations
• Terms and Conditions
• Privacy Policy

How Important Is Technology in Education? Benefits, Challenges, and Impact on Students

Many of today’s high-demand jobs were created in the last decade, according to the International Society for Technology in Education (ISTE). As advances in technology drive globalization and digital transformation, teachers can help students acquire the necessary skills to succeed in the careers of the future.

How important is technology in education? The COVID-19 pandemic is quickly demonstrating why online education should be a vital part of teaching and learning. By integrating technology into existing curricula, as opposed to using it solely as a crisis-management tool, teachers can harness online learning as a powerful educational tool.

The effective use of digital learning tools in classrooms can increase student engagement, help teachers improve their lesson plans, and facilitate personalized learning. It also helps students build essential 21st-century skills.

Virtual classrooms, video, augmented reality (AR), robots, and other technology tools can not only make class more lively, they can also create more inclusive learning environments that foster collaboration and inquisitiveness and enable teachers to collect data on student performance.

Still, it’s important to note that technology is a tool used in education and not an end in itself. The promise of educational technology lies in what educators do with it and how it is used to best support their students’ needs.

Educational Technology Challenges

BuiltIn reports that 92 percent of teachers understand the impact of technology in education. According to Project Tomorrow, 59 percent of middle school students say digital educational tools have helped them with their grades and test scores. These tools have become so popular that the educational technology market is projected to expand to $342 billion by 2025, according to the World Economic Forum.

However, educational technology has its challenges, particularly when it comes to implementation and use. For example, despite growing interest in the use of AR, artificial intelligence, and other emerging technology, less than 10 percent of schools report having these tools in their classrooms, according to Project Tomorrow. Additional concerns include excessive screen time, the effectiveness of teachers using the technology, and worries about technology equity.

Prominently rising from the COVID-19 crisis is the issue of content. Educators need to be able to develop and weigh in on online educational content, especially to encourage students to consider a topic from different perspectives. The urgent actions taken during this crisis did not provide sufficient time for this. Access is an added concern — for example, not every school district has resources to provide students with a laptop, and internet connectivity can be unreliable in homes.

Additionally, while some students thrive in online education settings, others lag for various factors, including support resources. For example, a student who already struggled in face-to-face environments may struggle even more in the current situation. These students may have relied on resources that they no longer have in their homes.

Still, most students typically demonstrate confidence in using online education when they have the resources, as studies have suggested. However, online education may pose challenges for teachers, especially in places where it has not been the norm.

Despite the challenges and concerns, it’s important to note the benefits of technology in education, including increased collaboration and communication, improved quality of education, and engaging lessons that help spark imagination and a search for knowledge in students.

The Benefits of Technology in Education

Teachers want to improve student performance, and technology can help them accomplish this aim. To mitigate the challenges, administrators should help teachers gain the competencies needed to enhance learning for students through technology. Additionally, technology in the classroom should make teachers’ jobs easier without adding extra time to their day.

Technology provides students with easy-to-access information, accelerated learning, and fun opportunities to practice what they learn. It enables students to explore new subjects and deepen their understanding of difficult concepts, particularly in STEM. Through the use of technology inside and outside the classroom, students can gain 21st-century technical skills necessary for future occupations.

Still, children learn more effectively with direction. The World Economic Forum reports that while technology can help young students learn and acquire knowledge through play, for example, evidence suggests that learning is more effective through guidance from an adult, such as a teacher.

Leaders and administrators should take stock of where their faculty are in terms of their understanding of online spaces. From lessons learned during this disruptive time, they can implement solutions now for the future. For example, administrators could give teachers a week or two to think carefully about how to teach courses not previously online. In addition to an exploration of solutions, flexibility during these trying times is of paramount importance.

Below are examples of how important technology is in education and the benefits it offers to students and teachers.

Increased Collaboration and Communication

Educational technology can foster collaboration. Not only can teachers engage with students during lessons, but students can also communicate with each other. Through online lessons and learning games, students get to work together to solve problems. In collaborative activities, students can share their thoughts and ideas and support each other. At the same time, technology enables one-on-one interaction with teachers. Students can ask classroom-related questions and seek additional help on difficult-to-understand subject matter. At home, students can upload their homework, and teachers can access and view completed assignments using their laptops.

Personalized Learning Opportunities

Technology allows 24/7 access to educational resources. Classes can take place entirely online via the use of a laptop or mobile device. Hybrid versions of learning combine the use of technology from anywhere with regular in-person classroom sessions. In both scenarios, the use of technology to tailor learning plans for each student is possible. Teachers can create lessons based on student interests and strengths. An added benefit is that students can learn at their own pace. When they need to review class material to get a better understanding of essential concepts, students can review videos in the lesson plan. The data generated through these online activities enable teachers to see which students struggled with certain subjects and offer additional assistance and support.

Curiosity Driven by Engaging Content

Through engaging and educational content, teachers can spark inquisitiveness in children and boost their curiosity, which research says has ties to academic success. Curiosity helps students get a better understanding of math and reading concepts. Creating engaging content can involve the use of AR, videos, or podcasts. For example, when submitting assignments, students can include videos or interact with students from across the globe.

Improved Teacher Productivity and Efficiency

Teachers can leverage technology to achieve new levels of productivity, implement useful digital tools to expand learning opportunities for students, and increase student support and engagement. It also enables teachers to improve their instruction methods and personalize learning. Schools can benefit from technology by reducing the costs of physical instructional materials, enhancing educational program efficiency, and making the best use of teacher time.

Become a Leader in Enriching Classrooms through Technology

Educators unfamiliar with some of the technology used in education may not have been exposed to the tools as they prepared for their careers or as part of their professional development. Teachers looking to make the transition and acquire the skills to incorporate technology in education can take advantage of learning opportunities to advance their competencies. For individuals looking to help transform the education system through technology, American University’s School of Education online offers a Master of Arts in Teaching and a Master of Arts in Education Policy and Leadership to prepare educators with essential tools to become leaders. Courses such as Education Program and Policy Implementation and Teaching Science in Elementary School equip graduate students with critical competencies to incorporate technology into educational settings effectively.

Learn more about American University’s School of Education online and its master’s degree programs.

Virtual Reality in Education: Benefits, Tools, and Resources

Data-Driven Decision Making in Education: 11 Tips for Teachers & Administration

Helping Girls Succeed in STEM

BuiltIn, “Edtech 101”

EdTech, “Teaching Teachers to Put Tech Tools to Work”

International Society for Technology in Education, “Preparing Students for Jobs That Don’t Exist”

The Journal, “How Teachers Use Technology to Enrich Learning Experiences”

Pediatric Research, “Early Childhood Curiosity and Kindergarten Reading and Math Academic Achievement”

Project Tomorrow, “Digital Learning: Peril or Promise for Our K-12 Students”

World Economic Forum, “The Future of Jobs Report 2018”

World Economic Forum, “Learning through Play: How Schools Can Educate Students through Technology”

Request Information

AU Program Helper

This AI chatbot provides automated responses, which may not always be accurate. By continuing with this conversation, you agree that the contents of this chat session may be transcribed and retained. You also consent that this chat session and your interactions, including cookie usage, are subject to our  privacy policy .

Global Education Monitoring Report

• 2023 GEM REPORT

Technology in education

• Recommendations
• 2023 Webpage
• Press Release
• RELATED PUBLICATIONS
• Background papers
• 2021/2 GEM Report
• 2020 Report
• 2019 Report
• 2017/8 Report
• 2016 Report

A tool on whose terms?

Ismael Martínez Sánchez/ProFuturo

• Monitoring SDG 4
• 2023 webpage

Major advances in technology, especially digital technology, are rapidly transforming the world. Information and communication technology (ICT) has been applied for 100 years in education, ever since the popularization of radio in the 1920s. But it is the use of digital technology over the past 40 years that has the most significant potential to transform education. An education technology industry has emerged and focused, in turn, on the development and distribution of education content, learning management systems, language applications, augmented and virtual reality, personalized tutoring, and testing. Most recently, breakthroughs in artificial intelligence (AI), methods have increased the power of education technology tools, leading to speculation that technology could even supplant human interaction in education.

In the past 20 years, learners, educators and institutions have widely adopted digital technology tools. The number of students in MOOCs increased from 0 in 2012 to at least 220 million in 2021. The language learning application Duolingo had 20 million daily active users in 2023, and Wikipedia had 244 million page views per day in 2021. The 2018 PISA found that 65% of 15-year-old students in OECD countries were in schools whose principals agreed that teachers had the technical and pedagogical skills to integrate digital devices in instruction and 54% in schools where an effective online learning support platform was available; these shares are believed to have increased during the COVID-19 pandemic. Globally, the percentage of internet users rose from 16% in 2005 to 66% in 2022. About 50% of the world’s lower secondary schools were connected to the internet for pedagogical purposes in 2022.

The adoption of digital technology has resulted in many changes in education and learning. The set of basic skills that young people are expected to learn in school, at least in richer countries, has expanded to include a broad range of new ones to navigate the digital world. In many classrooms, paper has been replaced by screens and pens by keyboards. COVID-19 can be seen as a natural experiment where learning switched online for entire education systems virtually overnight. Higher education is the subsector with the highest rate of digital technology adoption, with online management platforms replacing campuses. The use of data analytics has grown in education management. Technology has made a wide range of informal learning opportunities accessible.

Yet the extent to which technology has transformed education needs to be debated. Change resulting from the use of digital technology is incremental, uneven and bigger in some contexts than others. The application of digital technology varies by community and socioeconomic level, by teacher willingness and preparedness, by education level, and by country income. Except in the most technologically advanced countries, computers and devices are not used in classrooms on a large scale. Technology use is not universal and will not become so any time soon. Moreover, evidence is mixed on its impact: Some types of technology seem to be effective in improving some kinds of learning. The short- and long-term costs of using digital technology appear to be significantly underestimated. The most disadvantaged are typically denied the opportunity to benefit from this technology.

Too much attention on technology in education usually comes at a high cost. Resources spent on technology, rather than on classrooms, teachers and textbooks for all children in low- and lower-middle-income countries lacking access to these resources are likely to lead to the world being further away from achieving the global education goal, SDG 4. Some of the world’s richest countries ensured universal secondary schooling and minimum learning competencies before the advent of digital technology. Children can learn without it.

However, their education is unlikely to be as relevant without digital technology. The Universal Declaration of Human Rights defines the purpose of education as promoting the ‘full development of the human personality’, strengthening ‘respect for … fundamental freedoms’ and promoting ‘understanding, tolerance and friendship’. This notion needs to move with the times. An expanded definition of the right to education could include effective support by technology for all learners to fulfil their potential, regardless of context or circumstance.

Clear objectives and principles are needed to ensure that technology use is of benefit and avoids harm. The negative and harmful aspects in the use of digital technology in education and society include risk of distraction and lack of human contact. Unregulated technology even poses threats to democracy and human rights, for instance through invasion of privacy and stoking of hatred. Education systems need to be better prepared to teach about and through digital technology, a tool that must serve the best interests of all learners, teachers and administrators. Impartial evidence showing that technology is being used in some places to improve education, and good examples of such use, need to be shared more widely so that the optimal mode of delivery can be assured for each context.

CAN TECHNOLOGY HELP SOLVE THE MOST IMPORTANT CHALLENGES IN EDUCATION?

Discussions about education technology are focused on technology rather than education. The first question should be: What are the most important challenges in education? As a basis for discussion, consider the following three challenges:

• Equity and inclusion: Is fulfilment of the right to choose the education one wants and to realize one’s full potential through education compatible with the goal of equality? If not, how can education become the great equalizer?
• Quality: Do education’s content and delivery support societies in achieving sustainable development objectives? If not, how can education help learners to not only acquire knowledge but also be agents of change?
• Efficiency: Does the current institutional arrangement of teaching learners in classrooms support the achievement of equity and quality? If not, how can education balance individualized instruction and socialization needs?

How best can digital technology be included in a strategy to tackle these challenges, and under what conditions? Digital technology packages and transmits information on an unprecedented scale at high speed and low cost. Information storage has revolutionized the volume of accessible knowledge. Information processing enables learners to receive immediate feedback and, through interaction with machines, adapt their learning pace and trajectory: Learners can organize the sequence of what they learn to suit their background and characteristics. Information sharing lowers the cost of interaction and communication. But while such technology has tremendous potential, many tools have not been designed for application to education. Not enough attention has been given to how they are applied in education and even less to how they should be applied in different education contexts.

On the question of equity and inclusion , ICT – and digital technology in particular – helps lower the education access cost for some disadvantaged groups: Those who live in remote areas are displaced, face learning difficulties, lack time or have missed out on past education opportunities. But while access to digital technology has expanded rapidly, there are deep divides in access. Disadvantaged groups own fewer devices, are less connected to the internet (Figure 1) and have fewer resources at home. The cost of much technology is falling rapidly but is still too high for some. Households that are better off can buy technology earlier, giving them more advantages and compounding disparity. Inequality in access to technology exacerbates existing inequality in access to education, a weakness exposed during the COVID-19 school closures.

Figure 1: Internet connectivity is highly unequal

Percentage of 3- to 17-year-olds with internet connection at home, by wealth quintile, selected countries, 2017–19 Source: UNICEF database.

Education quality is a multifaceted concept. It encompasses adequate inputs (e.g. availability of technology infrastructure), prepared teachers (e.g. teacher standards for technology use in classrooms), relevant content (e.g. integration of digital literacy in the curriculum) and individual learning outcomes (e.g. minimum levels of proficiency in reading and mathematics). But education quality should also encompass social outcomes. It is not enough for students to be vessels receiving knowledge; they need to be able to use it to help achieve sustainable development in social, economic and environmental terms.

There are a variety of views on the extent to which digital technologies can enhance education quality. Some argue that, in principle, digital technology creates engaging learning environments, enlivens student experiences, simulates situations, facilitates collaboration and expands connections. But others say digital technology tends to support an individualized approach to education, reducing learners’ opportunities to socialize and learn by observing each other in real-life settings. Moreover, just as new technology overcomes some constraints, it brings its own problems. Increased screen time has been associated with adverse impact on physical and mental health. Insufficient regulation has led to unauthorized use of personal data for commercial purposes. Digital technology has also helped spread misinformation and hate speech, including through education.

Improvements to efficiency may be the most promising way for digital technology to make a difference in education. Technology is touted as being able to reduce the time students and teachers spend on menial tasks, time that can be used in other, educationally more meaningful activities. However, there are conflicting views on what is meaningful. The way that education technology is used is more complex than just a substitution of resources. Technology may be one-to-many, one-to-one or peer-to-peer technology. It may require students to learn alone or with others, online or offline, independently or networked. It delivers content, creates learner communities and connects teachers with students. It provides access to information. It may be used for formal or informal learning and can assess what has been learned. It is used as a tool for productivity, creativity, communication, collaboration, design and data management. It may be professionally produced or have user-generated content. It may be specific to schools and place-based or transcend time and place. As in any complex system, each technology tool involves distinct infrastructure, design, content and pedagogy, and each may promote different types of learning.

Technology is evolving too fast to permit evaluation that could inform decisions on legislation, policy and regulation. Research on technology in education is as complex as technology itself. Studies evaluate experiences of learners of various ages using various methodologies applied in contexts as different as self-study, classrooms and schools of diverse sizes and features, non-school settings, and at system level. Findings that apply in some contexts are not always replicable elsewhere. Some conclusions can be drawn from long-term studies as technologies mature but there is an endless stream of new products. Meanwhile, not all impact can be easily measured, given technology’s ubiquity, complexity, utility and heterogeneity. In brief, while there is much general research on education technology, the amount of research for specific applications and contexts is insufficient, making it difficult to prove that a particular technology enhances a particular kind of learning.

Why is there often the perception nevertheless that technology can address major education challenges? To understand the discourse around education technology, it is necessary to look behind the language being used to promote it, and the interests it serves. Who frames the problems technology should address? What are the consequences of such framing for education? Who promotes education technology as a precondition for education transformation? How credible are such claims? What criteria and standards need to be set to evaluate digital technology’s current and potential future contribution to education so as to separate hype from substance? Can evaluation go beyond short-term assessments of impact on learning and capture potential far-reaching consequences of the generalized use of digital technology in education?

Exaggerated claims about technology go hand in hand with exaggerated estimates of its global market size. In 2022, business intelligence providers’ estimates ranged from USD 123 billion to USD 300 billion. These accounts are almost always projected forward, predicting optimistic expansion, yet they fail to give historic trends and verify whether past projections proved true. Such reporting routinely characterizes education technology as essential and technology companies as enablers and disruptors. If optimistic projections are not fulfilled, responsibility is implicitly placed on governments as a way of maintaining indirect pressure on them to increase procurement. Education is criticized as being slow to change, stuck in the past and a laggard when it comes to innovation. Such coverage plays on users’ fascination with novelty but also their fear of being left behind.

The sections below further explore the three challenges this report addresses: equity and inclusion (in terms of access to education for disadvantaged groups and access to content), quality (in terms of teaching through and about digital technology) and efficiency (in terms of education management). After identifying technology’s potential to tackle these challenges, it discusses three conditions that need to be met for that potential to be fulfilled: equitable access, appropriate governance and regulation, and sufficient teacher capacity.

EQUITY AND INCLUSION: ACCESS FOR DISADVANTAGED GROUPS

A wide range of technology brings education to hard-to-reach learners. Technology has historically opened up education to learners facing significant obstacles in access to schools or well-trained teachers. Interactive radio instruction is used in nearly 40 countries. In Nigeria, radio instruction combined with print and audiovisual materials has been used since the 1990s, reaching nearly 80% of nomads and increasing their literacy, numeracy and life skills. Television has helped educate marginalized groups, notably in Latin America and the Caribbean. The Telesecundaria programme in Mexico, combining televised lessons with in-class support and extensive teacher training, increased secondary school enrolment by 21%. Mobile learning devices, often the only type of device accessible to disadvantaged learners, have been used in hard-to-reach areas and emergencies to share educational materials; complement in-person or remote channels; and foster interactions between students, teachers and parents, notably during COVID-19. Adults have been the main target of online distance learning, with open universities having increased participation for both working and disadvantaged adults.

Inclusive technology supports accessibility and personalization for learners with disabilities. Assistive technology removes learning and communication barriers, with numerous studies reporting a significant positive impact on academic engagement, social participation and the well-being of learners with disabilities. However, such devices remain inaccessible and unaffordable in many countries, and teachers often lack specialized training to use them effectively in learning environments. While people with disabilities used to rely exclusively on specialized devices to gain access to education, technology platforms and devices are increasingly incorporating accessibility features, which support inclusive, personalized learning for all students.

Technology supports learning continuity in emergencies. Mapping of 101 distance education projects in crisis contexts in 2020 showed that 70% used radio, television and basic mobile phones. During the Boko Haram crisis in Nigeria, the Technology Enhanced Learning for All programme used mobile phones and radios to support the learning continuity of 22,000 disadvantaged children, with recorded improvement in literacy and numeracy skills. However, there are significant gaps in terms of rigorous evaluation of education technology in emergencies, despite some limited recorded impact. Meanwhile, most projects are led by non-state actors as short-term crisis responses, raising sustainability concerns; education ministries implemented only 12% of the 101 projects.

Technology supported learning during COVID-19, but millions were left out. During school closures, 95% of education ministries carried out some form of distance learning, potentially reaching over 1 billion students globally. Many of the resources used during the pandemic were first developed in response to previous emergencies or rural education, with some countries building on decades of experience with remote learning. Sierra Leone revived the Radio Teaching Programme, developed during the Ebola crisis, one week after schools closed. Mexico expanded content from its Telesecundaria programme to all levels of education. However, at least half a billion, or 31% of students worldwide – mostly the poorest (72%) and those in rural areas (70%) – could not be reached by remote learning. Although 91% of countries used online learning platforms to deliver distance learning during school closures, the platforms only reached a quarter of students globally. For the rest, low-tech interventions such as radio and television were largely used, in combination with paper-based materials and mobile phones for increased interactivity.

Some countries are expanding existing platforms to reach marginalized groups. Less than half of all countries developed long-term strategies for increasing their resilience and the sustainability of interventions as part of their COVID-19 response plans. Many have abandoned distance learning platforms developed during COVID-19, while others are repurposing them to reach marginalized learners. The digital platform set up in Ukraine during the pandemic was expanded once the war broke out in 2022, allowing 85% of schools to complete the academic year.

EQUITY AND INCLUSION: ACCESS TO CONTENT

Technology facilitates content creation and adaptation. Open educational resources (OERs) encourage the reuse and repurposing of materials to cut development time, avoid duplication of work and make materials more context-specific or relevant to learners. They also significantly reduce the cost of access to content. In the US state of North Dakota, an initial investment of USD 110,000 to shift to OERs led to savings of over USD 1 million in student costs. Social media increases access to user-generated content. YouTube, a major player in both formal and informal learning, is used by about 80% of the world’s top 113 universities. Moreover, collaborative digital tools can improve the diversity and quality of content creation. In South Africa, the Siyavule initiative supported tutor collaboration on the creation of primary and secondary education textbooks.

Digitization of educational content simplifies access and distribution. Many countries, including Bhutan and Rwanda, have created static digital versions of traditional textbooks to increase availability. Others, including India and Sweden, have produced digital textbooks that encourage interactivity and multimodal learning. Digital libraries and educational content repositories such as the National Academic Digital Library of Ethiopia, National Digital Library of India and Teachers Portal in Bangladesh help teachers and learners find relevant materials. Learning management platforms, which have become a key part of the contemporary learning environment, help organize content by integrating digital resources into course structures.

Open access resources help overcome barriers. Open universities and MOOCs can eliminate time, location and cost barriers to access. In Indonesia, where low participation in tertiary education is largely attributed to geographical challenges, MOOCs play an important role in expanding access to post-secondary learning. During COVID-19, MOOC enrolment surged, with the top three providers adding as many users in April 2020 as in all of 2019. Technology can also remove language barriers. Translation tools help connect teachers and learners from various countries and increase the accessibility of courses by non-native students.

Ensuring and assessing the quality of digital content is difficult. The sheer quantity of content and its decentralized production pose logistical challenges for evaluation. Several strategies have been implemented to address this. China established specific quality criteria for MOOCs to be nationally recognized. The European Union developed its OpenupED quality label. India strengthened the link between non-formal and formal education. Micro-credentials are increasingly used to ensure that institution and learner both meet minimum standards. Some platforms aim to improve quality by recentralizing content production. YouTube, for example, has been funnelling financing and resources to a few trusted providers and partnering with well-established education institutions.

Technology may reinforce existing inequality in both access to and production of content. Privileged groups still produce most content. A study of higher-education repositories with OER collections found that nearly 90% were created in Europe or North America; 92% of the material in the OER Commons global library is in English. This influences who has access to digital content. MOOCs, for example, mainly benefit educated learners – studies have shown around 80% of participants on major platforms already have a tertiary degree – and those from richer countries. The disparity is due to divides in digital skills, internet access, language and course design. Regional MOOCs cater to local needs and languages but can also worsen inequality.

TEACHING AND LEARNING

Technology has been used to support teaching and learning in multiple ways. Digital technology offers two broad types of opportunities. First, it can improve instruction by addressing quality gaps, increasing opportunities to practise, increasing available time and personalizing instruction. Second, it can engage learners by varying how content is represented, stimulating interaction and prompting collaboration. Systematic reviews over the past two decades on technology’s impact on learning find small to medium-sized positive effects compared to traditional instruction. However, evaluations do not always isolate technology’s impact in an intervention, making it difficult to attribute positive effects to technology alone rather than to other factors, such as added instruction time, resources or teacher support. Technology companies can have disproportionate influence on evidence production. For example, Pearson funded studies contesting independent analysis that showed its products had no impact.

The prevalence of ICT use in classrooms is not high, even in the world’s richest countries. The 2018 PISA found that only about 10% of 15-year-old students in over 50 participating education systems used digital devices for more than an hour a week in mathematics and science lessons, on average (Figure 2) . The 2018 International Computer and Information Literacy Study (ICILS) showed that in the 12 participating education systems, simulation and modelling software in classrooms was available to just over one third of students, with country levels ranging from 8% in Italy to 91% in Finland.

Figure 2: Even in upper-middle- and high-income countries, technology use in mathematics and science classrooms is limited

Percentage of 15-year-old students who used digital devices for at least one hour per week in mathematics or science classroom lessons, selected upper-middle- and high-income countries, 2018 Source: 2018 PISA database.

Recorded lessons can address teacher quality gaps and improve teacher time allocation. In China, lesson recordings from high-quality urban teachers were delivered to 100 million rural students. An impact evaluation showed improvements in Chinese skills by 32% and a 38% long-term reduction in the rural–urban earning gap. However, just delivering materials without contextualizing and providing support is insufficient. In Peru, the One Laptop Per Child programme distributed over 1 million laptops loaded with content, but no positive impact on learning resulted, partly due to the focus on provision of devices instead of the quality of pedagogical integration.

Enhancing technology-aided instruction with personalization can improve some types of learning. Personalized adaptive software generates analytics that can help teachers track student progress, identify error patterns, provide differentiated feedback and reduce workload on routine tasks. Evaluations of the use of a personalized adaptive software in India documented learning gains in after-school settings and for low-performing students. However, not all widely used software interventions have strong evidence of positive effects compared to teacher-led instruction. A meta-analysis of studies on an AI learning and assessment system that has been used by over 25 million students in the United States found it was no better than traditional classroom teaching in improving outcomes.

Varied interaction and visual representation can enhance student engagement. A meta-analysis of 43 studies published from 2008 to 2019 found that digital games improved cognitive and behavioural outcomes in mathematics. Interactive whiteboards can support teaching and learning if well integrated in pedagogy; but in the United Kingdom, despite large-scale adoption, they were mostly used to replace blackboards. Augmented, mixed or virtual reality used as an experiential learning tool for repeated practice in life-like conditions in technical, vocational and scientific subjects is not always as effective as real-life training but may be superior to other digital methods, such as video demonstrations.

Technology offers teachers low-cost and convenient ways to communicate with parents. The Colombian Institute of Family Welfare’s distance education initiative, which targeted 1.7 million disadvantaged children, relied on social media platforms to relay guidance to caregivers on pedagogical activities at home. However, uptake and effectiveness of behavioural interventions targeting caregivers are limited by parental education levels, as well as lack of time and material resources.

Student use of technology in classrooms and at home can be distracting, disrupting learning. A meta-analysis of research on student mobile phone use and its impact on education outcomes, covering students from pre-primary to higher education in 14 countries, found a small negative effect, and a larger one at the university level. Studies using PISA data indicate a negative association between ICT use and student performance beyond a threshold of moderate use. Teachers perceive tablet and phone use as hampering classroom management. More than one in three teachers in seven countries participating in the 2018 ICILS agreed that ICT use in classrooms distracted students. Online learning relies on student ability to self-regulate and may put low-performing and younger learners at increased risk of disengagement.

DIGITAL SKILLS

The definition of digital skills has been evolving along with digital technology. An analysis for this report shows that 54% of countries have identified digital skills standards for learners. The Digital Competence Framework for Citizens (DigComp), developed on behalf of the European Commission, has five competence areas: information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving. Some countries have adopted digital skills frameworks developed by non-state, mostly commercial, actors. The International Computer Driving Licence (ICDL) has been promoted as a ‘digital skills standard’ but is associated mainly with Microsoft applications. Kenya and Thailand have endorsed the ICDL as the digital literacy standard for use in schools.

Digital skills are unequally distributed. In the 27 European Union (EU) countries, 54% of adults had at least basic digital skills in 2021. In Brazil, 31% of adults had at least basic skills, but the level was twice as high in urban as in rural areas, three times as high among those in the labour force as among those outside it, and nine times as high in the top socioeconomic group as in the two bottom groups. The overall gender gap in digital skills is small, but wider in specific skills. In 50 countries, 6.5% of males and 3.2% of females could write a computer program. In Belgium, Hungary and Switzerland, no more than 2 women for every 10 men could program; in Albania, Malaysia and Palestine, 9 women for every 10 men could do so. According to the 2018 PISA, 5% of 15-year-olds with the strongest reading skills but 24% of those with the weakest ones were at risk of being misled by a typical phishing email.

Formal skills training may not be the main way of acquiring digital skills. About one quarter of adults in EU countries, ranging from 16% in Italy to 40% in Sweden, had acquired skills through a ‘formalised educational institution’. Informal learning, such as self-study and informal assistance from colleagues, relatives and friends, was used by twice as many. Still, formal education is important: In 2018, those with tertiary education in Europe were twice as likely (18%) as those with upper secondary education (9%) to engage in free online training or self-study to improve their computer, software or application use. Solid mastery of literacy and numeracy skills is positively associated with mastery of at least some digital skills.

A curriculum content mapping of 16 education systems showed that Greece and Portugal dedicated less than 10% of the curriculum to data and media literacy while Estonia and the Republic of Korea embedded both in half their curricula. In some countries, media literacy in curricula is explicitly connected to critical thinking in subject disciplines, as under Georgia’s New School Model. Asia is characterized by a protectionist approach to media literacy that prioritizes information control over education. But in the Philippines, the Association for Media and Information Literacy successfully advocated for incorporation of media and information literacy in the curriculum, and it is now a core subject in grades 11 and 12.

Digital skills in communication and collaboration matter in hybrid learning arrangements. Argentina promoted teamwork skills as part of a platform for programming and robotics competitions in primary and secondary education. Mexico offers teachers and students digital education resources and tools for remote collaboration, peer learning and knowledge sharing. Ethical digital behaviour includes rules, conventions and standards to be learned, understood and practised by digital users when using digital spaces. Digital communication’s anonymity, invisibility, asynchronicity and minimization of authority can make it difficult for individuals to understand its complexities.

Competences in digital content creation include selecting appropriate delivery formats and creating copy, audio, video and visual assets; integrating digital content; and respecting copyright and licences. The ubiquitous use of social media has turned content creation into a skill with direct application in electronic commerce. In Indonesia, the Siberkreasi platform counts collaborative engagement among its core activities. The Kenya Copyright Board collaborates closely with universities to provide copyright education and conducts frequent training sessions for students in the visual arts and ICT.

Education systems need to strengthen preventive measures and respond to many safety challenges, from passwords to permissions, helping learners understand the implications of their online presence and digital footprint. In Brazil, 29% of schools have conducted debates or lectures on privacy and data protection. In New Zealand, the Te Mana Tūhono (Power of Connectivity) programme delivers digital protection and security services to almost 2,500 state and state-integrated schools. A systematic review of interventions in Australia, Italy, Spain and the United States estimated that the average programme had a 76% chance of reducing cyberbullying perpetration. In Wales, United Kingdom, the government has advised schools how to prepare for and respond to harmful viral online content and hoaxes.

The definition of problem-solving skills varies widely among education systems. Many countries perceive them in terms of coding and programming and as part of a computer science curriculum that includes computational thinking, algorithm use and automation. A global review estimated that 43% of students in high-income countries, 62% in upper-middle-income, 5% in lower-middle-income but no students in low-income countries take computer science as compulsory in primary and/or secondary education. Only 20% of education systems require schools to offer computer science as an elective or core course. Non-state actors often support coding and programming skills. In Chile, Code.org has partnered with the government to provide educational resources in computer science.

EDUCATION MANAGEMENT

Education management information systems focus on efficiency and effectiveness. Education reforms have been characterized by increased school autonomy, target setting and results-based performance, all of which require more data. By one measure, since the 1990s, the number of policies making reference to data, statistics and information has increased by 13 times in high-income, 9 times in upper-middle-income, and 5 times in low- and lower-middle-income countries. But only 54% of countries globally – and as low as 22% in sub-Saharan Africa – have unique student identification mechanisms.

Geospatial data can support education management. Geographical information systems help address equity and efficiency in infrastructure and resource distribution in education systems. School mapping has been used to foster diversity and reduce inequality of opportunity. Ireland links three databases to decide in which of its 314 planning areas to build new schools. Geospatial data can identify areas where children live too far from the nearest school. For instance, it has been estimated that 5% of the population in Guatemala and 41% in the United Republic of Tanzania live more than 3 kilometres away from the nearest primary school.

Education management information systems struggle with data integration. In 2017, Malaysia introduced the Education Data Repository as part of its 2019–23 ICT Transformation Plan to progressively integrate its 350 education data systems and applications scattered across institutions. By 2019, it had integrated 12 of its main data systems, aiming for full integration through a single data platform by the end of 2023. In New Zealand, schools had been procuring student management systems independently and lack of interoperability between them was preventing authorities from tracking student progress. In 2019, the government began setting up the National Learner Repository and Data Exchange to be hosted in cloud data centres, but deployment was paused in 2021 due to cybersecurity concerns. European countries have been addressing interoperability concerns collectively to facilitate data sharing between countries and across multiple applications used in higher-education management through the EMREX project.

Computer-based assessments and computer adaptive testing have been replacing many paper-based assessments. They reduce test administration costs, improve measurement quality and provide rapid scoring. As more examinations shift online, the need for online cheating detection and proctoring tools has also increased. While these can reduce cheating, their effectiveness should be weighed against fairness and psychological effects. Evidence on the quality and usefulness of technology-based assessments has started to emerge, but much less is known about cost efficiency. Among 34 papers on technology-based assessments reviewed for this report, transparent data on cost were lacking.

Learning analytics can increase formative feedback and enable early detection systems. In China, learning analytics has been used to identify learners’ difficulties, predict learning trajectories and manage teacher resources. In the United States, Course Signals is a system used to flag the likelihood of a student not passing a course; educators can then target them for additional support. However, learning analytics requires all actors to have sufficient data literacy. Successful education systems typically have absorptive capacity, including strong school leaders and confident teachers willing to innovate. Yet often seemingly trivial issues, such as maintenance and repair, are ignored or underestimated.

ACCESS TO TECHNOLOGY: EQUITY, EFFICIENCY AND SUSTAINABILITY

Access to electricity and devices is highly unequal between and within countries. In 2021, almost 9% of the global population – and more than 70% of people in rural sub-Saharan Africa – lacked access to electricity. Globally, one in four primary schools do not have electricity. A 2018 study in Cambodia, Ethiopia, Kenya, Myanmar, Nepal and Niger found that 31% of public schools were on grid and 9% were off grid, with only 16% enjoying uninterrupted power supply. Globally, 46% of households had a computer at home in 2020; the share of schools with computers for pedagogical purposes was 47% in primary, 62% in lower secondary and 76% in upper secondary education. There were at most 10 computers per 100 students in Brazil and Morocco but 160 computers per 100 students in Luxembourg, according to the 2018 PISA.

Internet access, a vital enabler of economic, social and cultural rights, is also unequal. In 2022, two in three people globally used the internet. In late 2021, 55% of the world’s population had mobile broadband access. In low- and middle-income countries, 16% less women than men used mobile internet in 2021. An estimated 3.2 billion people do not use mobile internet services despite being covered by a mobile broadband network. Globally, 40% of primary, 50% of lower secondary and 65% of upper secondary schools are connected to the internet. In India, 53% of private unaided and 44% of private aided schools are connected, compared with only 14% of government schools.

Various policies are used to improve access to devices. Some one in five countries have policies granting subsidies or deductions to buy devices. One-to-one technology programmes were established in 30% of countries at one time; currently only 15% of countries pursue such programmes. A number of upper-middle- and high-income countries are shifting from providing devices to allowing students to use their own devices in school. Jamaica adopted a Bring Your Own Device policy framework in 2020 to aim for sustainability.

Some countries champion free and open source software. Education institutions with complex ICT infrastructure, such as universities, can benefit from open source software to add new solutions or functionalities. By contrast, proprietary software does not permit sharing and has vendor locks that hinder interoperability, exchange and updates. In India, the National e-Governance Plan makes it mandatory for all software applications and services used in government to be built on open source software to achieve efficiency, transparency, reliability and affordability.

Countries are committed to universal internet provision at home and in school. About 85% of countries have policies to improve school or learner connectivity and 38% have laws on universal internet provision. A review of 72 low- and middle-income countries found that 29 had used universal service funds to reduce costs for underserved groups. In Kyrgyzstan, renegotiated contracts helped cut prices by nearly half and almost doubled internet speed. In Costa Rica, the Hogares Conectados (Connected Households) programme, which provided an internet cost subsidy to the poorest 60% of households with school-age children, helped reduce the share of unconnected households from 41% in 2016 to 13% in 2019. Zero-rating, or providing free internet access for education or other purposes, has been used, especially during COVID-19, but is not without problems, as it violates the net neutrality principle.

Education technology is often underutilized. In the United States, an average of 67% of education software licences were unused and 98% were not used intensively. According to the EdTech Genome Project, 85% of some 7,000 pedagogical tools, which cost USD 13 billion, were ‘either a poor fit or implemented incorrectly’. Less than one in five of the top 100 education technology tools used in classrooms met the requirements of the US Every Student Succeeds Act. Research had been published for 39% of these tools but the research was aligned with the act in only 26% of cases.

Evidence needs to drive education technology decisions. A review in the United Kingdom found that only 7% of education technology companies had conducted randomized controlled trials, 12% had used third-party certification and 18% had engaged in academic studies. An online survey of teachers and administrators in 17 US states showed that only 11% requested peer-reviewed evidence prior to adopting education technology. Recommendations influence purchase decisions, yet ratings can be manipulated through fake reviews disseminated on social media. Few governments try to fill the evidence gap, so demand has grown for independent reviews. Edtech Tulna, a partnership between a private think tank and a public university in India, offers quality standards, an evaluation toolkit and publicly available expert reviews.

Education technology procurement decisions need to take economic, social and environmental sustainability into account. With respect to economic considerations, it is estimated that initial investment in education technology accounts for just 25% or less of the eventual total cost. Regarding social concerns, procurement processes need to address equity, accessibility, local ownership and appropriation. In France, the Territoires Numériques Educatifs (Digital Educational Territories) initiative was criticized because not all subsidized equipment met local needs, and local governments were left out of the decisions on which equipment to purchase. Both issues have since been addressed. Concerning environmental considerations, it has been estimated that extending the lifespan of all laptops in the European Union by a year would save the equivalent of taking almost 1 million cars off the road in terms of CO2 emissions.

Regulation needs to address risks in education technology procurement. Public procurement is vulnerable to collusion and corruption. In 2019, Brazil’s Comptroller General of the Union found irregularities in the electronic bidding process for the purchase of 1.3 million computers, laptops and notebooks for state and municipal public schools. Decentralizing public procurement to local governments is one way to balance some of the risks. Indonesia has used its SIPLah e-commerce platform to support school-level procurement processes. However, decentralization is vulnerable to weak organizational capacity. A survey of administrators in 54 US school districts found that they had rarely carried out needs assessments.

GOVERNANCE AND REGULATION

Governance of the education technology system is fragmented. A department or an agency responsible for education technology has been identified in 82% of countries. Placing education ministries in charge of education technology strategies and plans could help ensure that decisions are primarily based on pedagogical principles. However, this is the case in just 58% of countries. In Kenya, the 2019 National Information, Communications and Technology Policy led the Ministry of Information, Communications and Technology to integrate ICT at all levels of education.

Participation is often limited in the development of education technology strategies and plans. Nepal established a Steering and a Coordination Committee under the 2013–17 ICT in Education Master Plan for intersectoral and inter-agency coordination and cooperation in its implementation. Including administrators, teachers and students can help bridge the knowledge gap with decision makers to ensure that education technology choices are appropriate. In 2022, only 41% of US education sector leaders agreed that they were regularly included in planning and strategic conversations about technology.

The private sector’s commercial interests can clash with government equity, quality and efficiency goals. In India, the government alerted families about the hidden costs of free online content. Other risks relate to data use and protection, privacy, interoperability and lock-in effects, whereby students and teachers are compelled to use specific software or platforms. Google, Apple and Microsoft produce education platforms tied to particular hardware and operating systems.

Privacy risks to children make their learning environment unsafe. One analysis found that 89% of 163 education technology products recommended for children’s learning during the COVID-19 pandemic could or did watch children outside school hours or education settings. In addition, 39 of 42 governments providing online education during the pandemic fostered uses that ‘risked or infringed’ upon children’s rights. Data used for predictive algorithms can bias predictions and decisions and lead to discrimination, privacy violations and exclusion of disadvantaged groups. The Cyberspace Administration of China and the Ministry of Education introduced regulations in 2019 requiring parental consent before devices powered by AI, such as cameras and headbands, could be used with students in schools and required data to be encrypted.

Children’s exposure to screen time has increased. A survey of screen time of parents of 3- to 8-year-olds in Australia, China, Italy, Sweden and the United States found that their children’s screen exposure increased by 50 minutes during the pandemic for both education and leisure. Extended screen time can negatively affect self-control and emotional stability, increasing anxiety and depression. Few countries have strict regulations on screen time. In China, the Ministry of Education limited the use of digital devices as teaching tools to 30% of overall teaching time. Less than one in four countries are banning the use of smartphones in schools. Italy and the United States have banned the use of specific tools or social media from schools. Cyberbullying and online abuse are rarely defined as offences but can fall under existing laws, such as stalking laws as in Australia and harassment laws in Indonesia.

Monitoring of data protection law implementation is needed. Only 16% of countries explicitly guarantee data privacy in education by law and 29% have a relevant policy, mainly in Europe and Northern America. The number of cyberattacks in education is rising. Such attacks increase exposure to theft of identity and other personal data, but capacity and funds to address the issue are often insufficient. Globally, 5% of all ransomware attacks targeted the education sector in 2022, accounting for more than 30% of cybersecurity breaches. Regulations on sharing children’s personal information are rare but are starting to emerge under the EU’s General Data Protection Regulation. China and Japan have binding instruments on protecting children’s data and information.

Technology has an impact on the teaching profession. Technology allows teachers to choose, modify and generate educational materials. Personalized learning platforms offer teachers customized learning paths and insights based on student data. During the COVID-19 pandemic, France facilitated access to 17 online teaching resource banks mapped against the national curriculum. The Republic of Korea temporarily eased copyright restrictions for teachers. Online teacher-student collaboration platforms provide access to support services, facilitate work team creation, allow participation in virtual sessions and promote sharing of learning materials.

Obstacles to integrating technology in education prevent teachers from fully embracing it. Inadequate digital infrastructure and lack of devices hinder teachers’ ability to integrate technology in their practice. A survey in 165 countries during the pandemic found that two in five teachers used their own devices, and almost one third of schools had only one device for education use. Some teachers lack training to use digital devices effectively. Older teachers may struggle to keep up with rapidly changing technology. The 2018 Teaching and Learning International Survey (TALIS) found that older teachers in 48 education systems had weaker skills and lower self-efficacy in using ICT. Some teachers may lack confidence. Only 43% of lower secondary school teachers in the 2018 TALIS said they felt prepared to use technology for teaching after training, and 78% of teachers in the 2018 ICILS were not confident in using technology for assessment.

Education systems support teachers in developing technology-related professional competencies. About half of education systems worldwide have ICT standards for teachers in a competency framework, teacher training framework, development plan or strategy. Education systems set up annual digital education days for teachers, promote OER, support the exchange of experiences and resources between teachers, and offer training. One quarter of education systems have legislation to ensure teachers are trained in technology, either through initial or in-service training. Some 84% of education systems have strategies for in-service teacher professional development, compared with 72% for pre-service teacher education in technology. Teachers can identify their development needs using digital self-assessment tools such as that provided by the Centre for Innovation in Brazilian Education.

Technology is changing teacher training. Technology is used to create flexible learning environments, engage teachers in collaborative learning, support coaching and mentoring, increase reflective practice, and improve subject or pedagogical knowledge. Distance education programmes have promoted teacher learning in South Africa and even equalled the impact of in-person training in Ghana. Virtual communities have emerged, primarily through social networks, for communication and resource sharing. About 80% of teachers surveyed in the Caribbean belonged to professional WhatsApp groups and 44% used instant messaging to collaborate at least once a week. In Senegal, the Reading for All programme used in-person and online coaching. Teachers considered face-to-face coaching more useful, but online coaching cost 83% less and still achieved a significant, albeit small, improvement in how teachers guided students’ reading practice. In Flanders, Belgium, KlasCement, a teacher community network created by a non-profit and now run by the Ministry of Education, expanded access to digital education and provided a platform for discussions on distance education during the pandemic.

Many actors support teacher professional development in ICT. Universities, teacher training institutions and research institutes provide specialized training, research opportunities and partnerships with schools for professional development in ICT. In Rwanda, universities collaborated with teachers and the government to develop the ICT Essentials for Teachers course. Teacher unions also advocate for policies that support teachers. The Confederation of Education Workers of the Argentine Republic established the right of teachers to disconnect. Civil society organizations, including the Carey Institute for Global Good, offer support through initiatives such as providing OER and online courses for refugee teachers in Chad, Kenya, Lebanon and Niger.

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

• Machine learning
• Sustainability
• Black holes
• Classes and programs

Departments

• Aeronautics and Astronautics
• Brain and Cognitive Sciences
• Architecture
• Political Science
• Mechanical Engineering

Centers, Labs, & Programs

• Abdul Latif Jameel Poverty Action Lab (J-PAL)
• Picower Institute for Learning and Memory
• Lincoln Laboratory
• School of Architecture + Planning
• School of Engineering
• School of Humanities, Arts, and Social Sciences
• Sloan School of Management
• School of Science
• MIT Schwarzman College of Computing

What 126 studies say about education technology

Press contact :.

Previous image Next image

In recent years, there has been widespread excitement around the transformative potential of technology in education. In the United States alone, spending on education technology has now exceeded $13 billion . Programs and policies to promote the use of education technology may expand access to quality education, support students’ learning in innovative ways, and help families navigate complex school systems.

However, the rapid development of education technology in the United States is occurring in a context of deep and persistent inequality . Depending on how programs are designed, how they are used, and who can access them, education technologies could alleviate or aggravate existing disparities. To harness education technology’s full potential, education decision-makers, product developers, and funders need to understand the ways in which technology can help — or in some cases hurt — student learning.

To address this need, J-PAL North America recently released a new publication summarizing 126 rigorous evaluations of different uses of education technology. Drawing primarily from research in developed countries, the publication looks at randomized evaluations and regression discontinuity designs across four broad categories: (1) access to technology, (2) computer-assisted learning or educational software, (3) technology-enabled nudges in education, and (4) online learning.

This growing body of evidence suggests some areas of promise and points to four key lessons on education technology.

First, supplying computers and internet alone generally do not improve students’ academic outcomes from kindergarten to 12th grade, but do increase computer usage and improve computer proficiency. Disparities in access to information and communication technologies can exacerbate existing educational inequalities. Students without access at school or at home may struggle to complete web-based assignments and may have a hard time developing digital literacy skills.

Broadly, programs to expand access to technology have been effective at increasing use of computers and improving computer skills. However, computer distribution and internet subsidy programs generally did not improve grades and test scores and in some cases led to adverse impacts on academic achievement. The limited rigorous evidence suggests that distributing computers may have a more direct impact on learning outcomes at the postsecondary level.

Second, educational software (often called “computer-assisted learning”) programs designed to help students develop particular skills have shown enormous promise in improving learning outcomes, particularly in math. Targeting instruction to meet students’ learning levels has been found to be effective in improving student learning, but large class sizes with a wide range of learning levels can make it hard for teachers to personalize instruction. Software has the potential to overcome traditional classroom constraints by customizing activities for each student. Educational software programs range from light-touch homework support tools to more intensive interventions that re-orient the classroom around the use of software.

Most educational software that have been rigorously evaluated help students practice particular skills through personalized tutoring approaches. Computer-assisted learning programs have shown enormous promise in improving academic achievement, especially in math. Of all 30 studies of computer-assisted learning programs, 20 reported statistically significant positive effects, 15 of which were focused on improving math outcomes.

Third, technology-based nudges — such as text message reminders — can have meaningful, if modest, impacts on a variety of education-related outcomes, often at extremely low costs. Low-cost interventions like text message reminders can successfully support students and families at each stage of schooling. Text messages with reminders, tips, goal-setting tools, and encouragement can increase parental engagement in learning activities, such as reading with their elementary-aged children.

Middle and high schools, meanwhile, can help parents support their children by providing families with information about how well their children are doing in school. Colleges can increase application and enrollment rates by leveraging technology to suggest specific action items, streamline financial aid procedures, and/or provide personalized support to high school students.

Online courses are developing a growing presence in education, but the limited experimental evidence suggests that online-only courses lower student academic achievement compared to in-person courses. In four of six studies that directly compared the impact of taking a course online versus in-person only, student performance was lower in the online courses. However, students performed similarly in courses with both in-person and online components compared to traditional face-to-face classes.

The new publication is meant to be a resource for decision-makers interested in learning which uses of education technology go beyond the hype to truly help students learn. At the same time, the publication outlines key open questions about the impacts of education technology, including questions relating to the long-term impacts of education technology and the impacts of education technology on different types of learners.

To help answer these questions, J-PAL North America’s Education, Technology, and Opportunity Initiative is working to build the evidence base on promising uses of education technology by partnering directly with education leaders.

Education leaders are invited to submit letters of interest to partner with J-PAL North America through its  Innovation Competition . Anyone interested in learning more about how to apply is encouraged to contact initiative manager Vincent Quan .

Share this news article on:

Related links.

• J-PAL Education, Technology, and Opportunity Initiative
• Education, Technology, and Opportunity Innovation Competition
• Article: "Will Technology Transform Education for the Better?"
• Abdul Latif Jameel Poverty Action Lab
• Department of Economics

Related Topics

• School of Humanities Arts and Social Sciences
• Education, teaching, academics
• Technology and society
• Computer science and technology

Related Articles

J-PAL North America calls for proposals from education leaders

J-PAL North America’s Education, Technology, and Opportunity Innovation Competition announces inaugural partners

New learning opportunities for displaced persons

J-PAL North America announces new partnerships with three state and local governments

A new way to measure women’s and girls’ empowerment in impact evaluations

Previous item Next item

More MIT News

When the lights turned on in the universe

Read full story →

3 Questions: How to prove humanity online

Lincoln Laboratory and National Strategic Research Institute launch student research program to tackle biothreats to national security

Christine Ortiz named director of MIT Technology and Policy Program

MIT engineers design tiny batteries for powering cell-sized robots

New open-source tool helps to detangle the brain

• More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

• Map (opens in new window)
• Events (opens in new window)
• People (opens in new window)
• Careers (opens in new window)
• Accessibility
• Social Media Hub
• MIT on Facebook
• MIT on YouTube
• MIT on Instagram

Articles on Technology in Education

Displaying 1 - 20 of 21 articles.

Do smartphones belong in classrooms? Four scholars weigh in

Louis-Philippe Beland , Carleton University ; Arnold Lewis Glass , Rutgers University ; Daniel G. Krutka , University of North Texas , and Sarah Rose , Staffordshire University

ChatGPT is the push higher education needs to rethink assessment

Sioux McKenna , Rhodes University ; Dan Dixon , University of Sydney ; Daniel Oppenheimer , Carnegie Mellon University ; Margaret Blackie , Rhodes University , and Sam Illingworth , Edinburgh Napier University

5 challenges of doing college in the metaverse

Nir Kshetri , University of North Carolina – Greensboro

Six benefits that the metaverse offers to colleges and universities

School tech: teachers explain what they need to make it work better

Craig Blewett , University of KwaZulu-Natal

Marrying technology and home language boosts maths and science learning

Mmaki Jantjies , University of the Western Cape

Why putting the words ‘learning’ and ‘Facebook’ together isn’t an oxymoron

Technology can help kids learn, but only if parents and teachers are involved

Yashwant Ramma , Mauritius Institute of Education

To stay in the game universities need to work with tech companies

Martin Hall , University of Cape Town

It’s true, internet surfing during class is not so good for grades

Susan Ravizza , Michigan State University

Why schools shouldn’t approach technology like businesses once did

Schools must get the basics right before splashing out on technology

Technology is no longer a luxury for universities – it’s a necessity

Michael Rowe , University of the Western Cape

Which digital books work best in the classroom?

Natalia Kucirkova , The Open University

Why replacing teachers with automated education lacks imagination

George Veletsianos , Royal Roads University

Unlocking the habits of Britain’s smartphone generation

Leslie Haddon , London School of Economics and Political Science

The value of MOOCs lies with employers

Dan Jerker B. Svantesson , Bond University

How to choose the best educational app for your child

Videogames should be a teacher’s best friend

Michael Kasumovic , UNSW Sydney

As laptop scheme ends, what next for families and learning?

Jason M Lodge , The University of Melbourne

Related Topics

• Digital economy
• Educational technology
• Higher ed attainment
• Higher education
• Smartphones
• Student learning
• technology in higher education
• technology in schools

Top contributors

Associate Professor in Education & Technology, University of KwaZulu-Natal

Professor of Reading and Children’s Development, The Open University

Professor of Management, University of North Carolina – Greensboro

Senior researcher and visiting lecturer in the Department of Media and Communications, London School of Economics and Political Science

Associate Professor of Psychology, Michigan State University

Professor specialising in Internet law, Bond University

Adjunct Professor Faculty of Law, Queensland University of Technology

Deputy Associate Dean (Academic), Faculty of Humanities and Social Sciences; Associate Professor of Educational Psychology, School of Education, The University of Queensland

Associate Professor in Physiotherapy, University of the Western Cape

Evolutionary Biologist, ARC Future Fellow, UNSW Sydney

Emeritus Professor, MTN Solution Space Graduate School of Business, University of Cape Town

Professor & Head, School of Science & Mathematics, Mauritius Institute of Education

Professor of Psychology, Rutgers University

Professor of Decision Science and Psychology, Carnegie Mellon University

Professor of Creative Pedagogies, Edinburgh Napier University

• X (Twitter)
• Unfollow topic Follow topic

The future of educational technology

Dan Schwartz is a cognitive psychologist and dean of the Stanford Graduate School of Education.

He says that artificial intelligence is a different beast, but he is optimistic about its future in education. “It’s going to change stuff. It’s really an exciting time,” he says. Schwartz imagines a world not where AI is the teacher, but where human students learn by teaching AI chatbots key concepts. It’s called the Protégé Effect, Schwartz says, providing host Russ Altman a glimpse of the future of education on this episode of Stanford Engineering’s The Future of Everything podcast.

Listen on your favorite podcast platform:

Related : Dan Schwartz , professor of educational technology

[00:00:00] Dan Schwartz: You know, the tough question for me is, should you let the kid use ChatGPT during the test? Right? And we had this argument over calculators, right? And finally they came up with ways to ask questions where it was okay if the kids had calculators. Because the calculator was doing the routine stuff and that's not really what you cared about. What you cared about was, could the kid be innovative? Could they take another, a second approach to solve a problem? Things like that.

[00:00:33] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. If you're enjoying The Future of Everything podcast, please hit the follow button in the app that you're listening to now. This will guarantee that you never miss an episode. 

[00:00:46] Today, Dan Schwartz will tell us how AI is impacting education. He studies educational technology and he finds that there's a lot of promise and a lot of worries about how we're going to use AI in the classroom. It's the future of educational technology. Before we get started, please remember to follow the show in the app that you listen to. You'll be alerted to all of our episodes and it'll make sure that you never miss the future of anything.

[00:01:16] You know, the rise of AI has been on people's minds ever since the release of ChatGPT. Especially the powerful one that started to do things that were scary good. We've seen people using it in business, in sports, in entertainment, and definitely in education. When it comes to education, there are some fundamental questions, however, are we teaching students how to use AI? Or are we teaching students? How do we assess them? Can teachers grade papers with AI? Can students write papers with AI? Why is anybody doing anything? Why don't we just have the AI talk to itself all day? These are real questions that come up in AI. 

[00:01:55] Fortunately, we're going to be talking to Dan Schwartz, who's a professor of education and a dean of the School of Education at Stanford University about how AI is impacting education.

[00:02:06] Dan, the release of ChatGPT has had an impact all over the world, people are using it in all kinds of ways. And clearly one of the areas that AI, especially generative AI has made impact is in education. Students are clearly using it, teachers are thinking about using it or using it. You're the Dean of Education at Stanford. What's your take on the situation right now for AI in education? 

[00:02:33] Dan Schwartz: Okay, so lots of answers to that, but, but, you know, the thing I've enjoyed the most is, uh, showing it to people and watching their reaction. So I'm a cognitive psychologist. I study creativity, learning, what it means to understand. And you show this to people and you just see them go, oh my lord.

[00:02:53] And then the next thing you see is they begin to say, uh, what's left for humans? Like what's left? And then they sort of say, wait a minute, will there be any jobs? And then finally they sort of say. Oh my goodness, education needs to change. And as a dean who raises money for a school, this is the best thing to ever happen. No, whether it's good or bad, it doesn't matter. Everybody realizes it's going to change stuff. And so it's really an exciting time. 

[00:03:22] Russ Altman: So that is really good news. I have to say going into this and I have to reveal a bias. I have often wondered if technology has any place in a classroom. And I think it's because I was, uh, I was injured as a youth.

[00:03:37] This is in the 1970s when some teachers tried to put a computer program in front of me and I was a pretty motivated student and I worked with this computer for about six minutes, and I should say, I'm not an anti-computer person. I literally spent all my time writing algorithms and doing computation work. But I just felt as a youth that I wanted to have a teacher in front of me, a human telling me things. Uh, and so that is clearly not the direction, I hear you laughing. So talk to me about the appropriate way to think about computers. Because I really have a big negative reaction to the idea of anything standing between me and a teacher.

[00:04:18] Dan Schwartz: You must have had very good teachers. 

[00:04:19] Russ Altman: I might have. 

[00:04:19] Dan Schwartz: So Russ, you sound like someone who doesn't play video games. 

[00:04:23] Russ Altman: I do not play video games. 

[00:04:24] Dan Schwartz: So there's this world out there where people can experience things they could never experience, uh, directly. And no teacher can deliver this immersive experience of you in the Amazon searching for anthropological artifacts. There's also something called social media that people use. 

[00:04:43] Russ Altman: I've heard about this. 

[00:04:43] Dan Schwartz: Yeah. Yeah. 

[00:04:44] Russ Altman: I think we disseminate the show using it. 

[00:04:46] Dan Schwartz: So back in the day. 

[00:04:47] Russ Altman: Okay. So I'm a dinosaur. 

[00:04:49] Dan Schwartz: Uh, back in the day, you got the Apple 2 maybe, and it's about 64 K, maybe. It's got a big floppy drive and it takes all its CPU power to draw a picture of a two plus two on the screen. So I think things have changed a little bit Russ. But I appreciate your desire to be connected to teachers. I don't think we're replacing them. 

[00:05:14] Russ Altman: I'm not going to give you a lecture about teaching. But I will say this one sentence that was reverberating through my brain when I was getting ready for our interview, which was when I'm in a classroom, and this has been since I've been in third grade. I am watching the teacher trying to understand, how they think about the information and how they struggle with it to like understand it and then try to relay it to me.

[00:05:34] And so it is, that's where I'm learning. I'm, it's not even what they're saying. It's they're painting a picture for their cognitive model of what they're talking about. And that's what I'm trying to pull out to this day. And so that's why I have such a negative reaction to anything standing between me and this other human who has a model that is more advanced than mine about the material that we're struggling with and I just, I'm trying to download that model. 

[00:06:01] Dan Schwartz: Wow. You're, you are a cognitive psychologist, Russ? 

[00:06:03] Russ Altman: I don't know. 

[00:06:05] Dan Schwartz: Like I had a buddy who sort of became a Nobel laureate. And he talked about how he loved take apart cars, and I'd say I love to watch you take apart cars, just to figure out what you're doing. No, so I think, let's separate this. There's the part where you think the interaction with the teacher is important. I don't know that you need it eight hours a day. You know, that's an awful lot of interaction. I'm not sure I want to be with my mom and dad for eight hours a day trying to figure out their thinking. So you don't need it all the time.

[00:06:34] On the other side, you know, we can do creative things with the computers. So for example, I wrote a program where students learn by teaching a computer agent. And so they're trying to figure out how to get the agent to think the way it should in the domain. Turns out it's highly motivating. The kids learn a lot. The problem was the technology quickly became obsolete. Because after kids used it for a couple of days, they no longer needed it, 'cause they'd figured out sort of how to do the kind of reasoning that we wanted them to teach the agent to do for reasoning. 

[00:07:06] Russ Altman: That's exactly what I was talking about before, about my relationship with my teacher. And you just flipped it, but it's the same idea, which is that there's a cognitive model that you're trying to transfer. And by doing that transfer, you get in, you introspect on it and you understand what it is that you're thinking about. 

[00:07:22] Dan Schwartz: I think that's right. You know, so the concern is the computer does all the work, right? And so I'm just sitting there pressing a button that isn't relevant to the domain I'm trying to learn. But you know, uh, one of the things computers are really good at, like as good as casinos, is motivation. So some computer programs, they gamify it. I'm not sure that's a great use of it. Because you, you know, you try and you learn to just beat the game for the reward. 

[00:07:49] Russ Altman: Right.

[00:07:49] Dan Schwartz: As opposed to learn the content. But things like having, teaching an intelligent agent how to think. There's something called the protege effect, which is you'll try harder to learn the content to teach your agent than you will to prepare for a test. Right? So we can make the computer pretty social. 

[00:08:08] Russ Altman: Okay. So you are clearly a technology optimist in education. And in addition to the amazing fundraising and like, there's so many questions to be answered. What I think a lot of people are worried about is, are we at risk of losing a gen. We've already lost a few generations of students, some people argue, because of the pandemic and the terrible impact it had, especially on, uh, on people who weren't privileged in society and in their education.

[00:08:34] Are we about to enter yet another shock to the system where, because of the ease of having essays written and having, and grading papers, that we really don't serve a generation of students well? Or do you think that's a overhyped, unlikely to happen thing? 

[00:08:51] Dan Schwartz: No, it's a good question. You know, that part of this is people's view about cheating, you know? And so it's too easy for students to do certain things. But there's another response that I want to hang on to. I want to ask you, Russ, are you using, you teach. 

[00:09:07] Russ Altman: Yeah. 

[00:09:07] Dan Schwartz: Are you like putting in all sorts of rules to prevent students from cheating, or are you saying, use it, do whatever you can. I'm going to outsmart your technique anyway.

[00:09:17] Russ Altman: It's a little bit more on the latter. So we, uh, I teach an ethics class, which is a writing class. And we allow ChatGPT because the, my fellow instructor and I decided, and this was the quote, we want to be part of the future, not part of the past. So we said to the students, 

[00:09:33] Dan Schwartz: Sorry, The Future of Everything, Russ.

[00:09:34] Russ Altman: Thank you. Thank you. Thank you. And thanks for the plug. So, uh, we allow it. We asked them to tell us what prompt they used and to show us the initial output that they got from that prompt. And then we, of course, have them hand in the final thing. And we instruct the TAs and ourselves, when we grade that we're grading the final product with or without a declaration of whether ChatGPT is used.

[00:09:56] We do have engineers as TAs, which means that they did a careful analysis. Students who used ChatGPT, and I don't think this is a surprise, got slightly lower grades, but spend substantially less time on the assignment. So if you're a busy student, you might say, I will make that trade off because the grades weren't a ton worse. It was like two points out of a hundred, like from a ninety to an eighty-eight, and they completed it in like half the time. 

[00:10:25] Dan Schwartz: Uh, do you think they learned less? 

[00:10:28] Russ Altman: So we don't know. We don't know. And, uh, the evaluation of learning is something that I'm looking to you, Dan. Uh, how do I tell? So, um, so we do try to use it. But we are stressed out. We have seen cases where people say they used ChatGPT, but tried to mislead us in how they use it. They said, I only used it for copy editing, but it was clear that they did more than copy editing with it. And so there's at the edges, there are some challenges. But in the end, we said motivated students who want to learn will use it as a tool and we'll learn. And the students who we have failed to motivate, and it is our failure, you could argue. They're just going to do whatever they do, and we're not going to be able to really impact that trajectory very much. 

[00:11:12] Dan Schwartz: Yeah, you know, you sort of see the same thing with video, video-based lectures. So I'm online. I've got this lecture. Do I really want to sit and listen to the whole thing? Not really. I'm going to skim forward to find the information. I skim back. I'm probably going to end up doing the minimum amount if it's not a great lecture. 

[00:11:29] Russ Altman: Yeah.

[00:11:29] Dan Schwartz: So I'm not sure this is a ChatGPT phenomenon. It's just, it's sort of an enabler. I think the challenge is thinking of the right assignment. So like, you can grade things on novel and appropriateness. So, are they novel? You know, if they use ChatGPT like everybody else, they won't be novel. They'll all produce the same thing. 

[00:11:48] Russ Altman: It's incredibly, yes. It, so it is, um, there's the most common type of, uh, moral theory is called common morality. And it turns out that ChatGPT does pretty well at that one because there's so many examples that it has seen. And it's terrible at Kant. Deontology, it really can't do. Okay, so let me. 

[00:12:07] Dan Schwartz: So let me get back to your question. 

[00:12:09] Russ Altman: Yeah. 

[00:12:09] Dan Schwartz: So here's what I see going on right now. There, there are like, uh, big industry conferences. Because they're going to, they're producing the technology that schools can adopt. Right? And there's a lot of money there. And twenty years ago, there were zero unicorns, and about, uh, I think last year, fifty-four billion dollar valuation companies in ed tech. So this is a big change. So what are they doing? They're basically creating things to do stuff to students, right? 

[00:12:42] So maybe they're marketing to the teachers, but it's, you know, it's, I'll make a tutor that, uh, is more efficient at delivering information to the students. Or, I will make a program that can correct their math very quickly. And so what's happening is the industry is sort of using the AI in the way that nobody else uses it.

[00:13:04] Because everybody who's got this tool wants to create stuff, right? Like, uh, my brother. It's my birthday, what does he do? He has ChatGPT to write me a poem about Dan Schwartz at Stanford. What he doesn't know is that there's a lot of Dan Schwartz's and so evidently I wear colorful ties, but this is what everybody wants to do. They want to create with it. Meanwhile, the field is trying to push towards efficiency. Can we get the kids done faster? Can we get them through the curriculum faster? Can we correct them faster? In which case the kids are going to optimize for being really efficient, right? As opposed to just trying to be creative, innovative, use it for deeper kinds of things. So this is my big fear. 

[00:13:42] Russ Altman: And so you're watching these companies and I'm guessing that they don't always ask your opinion about what's, what would you tell, so let's say a, one of these unicorn billion dollar or more companies comes to you and says, we want to do this right. We want to use the best educational research to create AI that can bring education to people who might otherwise not have quality education. What would you tell them? 

[00:14:04] Dan Schwartz: So this is a challenge, right? This is something we're actively trying to solve. So we've created a Stanford accelerator for learning to kind of figure out how to do this. 'Cause I've been in this ed tech position for quite a while. And the companies come in and they say, we really want your opinion. And then they present what they're doing. And I go, uh, have you ever thought of, and they go, wait, let me finish. And this goes on for fifty-five minutes. Where they're telling me what they want to do. And I'm trying to say, you know, if you just did this. And the way it ends is I say to them, look, you, if you do these three things, I'll consider being an advisor.

[00:14:42] Russ Altman: Right.

[00:14:42] Dan Schwartz: They never come back. 

[00:14:45] Russ Altman: So the message you're sending them is just not in their worldview. 

[00:14:50] Dan Schwartz: It's because they have a vision. Everybody wants to start their own school. 

[00:14:53] Russ Altman: Yeah. 

[00:14:53] Dan Schwartz: They have their vision of what it should be and they're urgent to get it done. And you know, it's a startup mentality. So trying to figure out how can we educate them? You know, I think we know a lot about how people learn that, uh, that we didn't know twenty years ago when they went to school. And the AI, you know, one of the things it can do is implement some of these theories of learning in ways that don't exist in textbooks and things like that.

[00:15:17] So that's the big hope. And the question is, how can you take advantage of industry? You know, education is a public good, but they still buy all their products. And so going through those companies is one way to sort of bring a positive revolution. But again, I'm a little worried that the companies are, and they're sort of optimizing for local minima.

[00:15:41] Russ Altman: Yeah. 

[00:15:41] Dan Schwartz: You know, to accommodate the current schools and things like that. 

[00:15:44] Russ Altman: Should we take, so what, should we take solace in the teachers? So many of us are fans of teachers, grammar school teachers, middle school teachers, high school teachers, but many of these folks are incredibly dedicated. Will they be a final, um, uh, a final filter that looks at these, uh, educational technologies and says, absolutely not. Or yeah, we'll use that, but we're going to use that in a way that makes sense for my way of teaching. Or are they not in a position to make those kinds of, what you could call courageous decisions, about kind of modifying the use of these tools to make them as good as possible in, uh, on the ground? 

[00:16:21] Dan Schwartz: So it's pretty interesting. The surveys I've seen, uh, sort of over the last year, the different groups do different surveys. It, it sort of, if I take the average, about sixty percent of K 12 teachers are using GenAI, right? And about thirty percent of the kids. If I go to the college level, about thirty percent of the faculty are using GenAI in teaching and about eighty percent of the kids are using it. So I do think in the pre K to 12 space, the teachers are making decisions. They do a lot of curriculum. There are, so a great application is, um, project-based learning. So project-based learning is a lot of fun. Kids learn a lot. They sort of develop a passion, a certain depth. As opposed to just mastering sort of the requirements, but it's really hard to manage. You know, when I was a high school teacher, I had a hundred and thirty kids, right?

[00:17:11] If all of them have a separate project, I have to help plan them and make them goal, you know, learning goal appropriate. So the GenAI can help me do that. It can help me, uh, have the kids sort of help use it to help them design a successful project. Uh, it can help me with a dashboard that helps manage them, hitting their milestones, things like that.

[00:17:31] And there, you know, it's, it, the, teacher is like, I can do something I just couldn't do before. 

[00:17:35] Russ Altman: Yeah. Yeah. 

[00:17:36] Dan Schwartz: It's different than the model where you put the kids in the back of the room who finished early and say, go use the computer. I think, you know, most schools, kids are carrying computers in classes. So it's a little different. It's more integrated than it used to be. 

[00:17:52] Russ Altman: This is the Future of Everything with Russ Altman. More with Dan Schwartz, next.

[00:18:06] Welcome back to The Future of Everything. I'm Russ Altman and I'm speaking with Dan Schwartz, professor of education at Stanford University. 

[00:18:12] In the last segment, Dan told us about AI, education, some of the promises and some of the pitfalls that he's looking at on the ground, thinking about how to educate the next generation.

[00:18:23] In this segment, I'm going to ask him about assessment, grading. How do we do that with AI and how do we make sure it goes well? Also going to ask him about physical activity, which turns out physical ness is an important part of learning. 

[00:18:39] I want to get a little bit more detailed, Dan, in this next segment, and I want to start off with assessment, grading. I know you've thought about this a lot. People are worried that um, AI is going to start to doing, be doing all the grading. Everybody knows that a high school teacher with a big, couple of big classes can spend their entire weekend grading essays. It is so tempting just to feed that into ChatGPT and say, hey, how good is this essay? How should we think about, maybe worry about, but maybe just think about, assessment in education in the future? 

[00:19:11] Dan Schwartz: Yeah, this was, uh, you remember the MOOCs? 

[00:19:14] Russ Altman: Yes. 

[00:19:14] Dan Schwartz: Massively online, open courses. And, uh, you're hoping you have ten thousand students, and then you gotta grade the papers for ten thousand students. So what do you do? You give a multiple-choice tests, which can be machine coded, right? So, so I think that's always there. I'm going to take it a slightly different direction, which is, uh, I'm interacting with a computer system and while I'm interacting with it, it's, it can be constantly assessing in real time, right?

[00:19:41] And so there's a field that's sometimes called educational data mining or learning analytics. And there's thousands of people who are working on, how do I get informative signal out of students interactions. Like, are they trying to game the system? Are they reflecting? And so forth. So this is something the computer can do pretty well, right?

[00:20:02] It can sort of track what students are doing, assess, and then ideally deliver the right piece of instruction at the moment. So yours, you could use the assessments to give people a grade, but really the more important thing is, can you use the assessments to make instructional decisions? So I think this is a big area of advancement, but here's my concern.

[00:20:25] We've gotten very good at assessing things that are objectively right and wrong. Like did you remember the right word? Did you get two plus two correctly? For most of the things we care about now, they're like strategic and heuristic, which means it's not a guaranteed right answer. And so what you really want to do is assess students choices for what to do. So for example, uh, creativity, it's just for the most part, it's a large set of strategies. Right? There's a bunch of strategies that help you be creative. The question is, do the students choose to do that or do they take the safe route? 'Cause creativity is a risk, right? 'Cause you're not sure.

[00:21:02] So I think this is where the field needs to go. Is being willing to say that certain kinds of choices about learning are better than others. Uh, and it's a, it becomes more of an ethical question now. Instead of saying two plus two equals four, there's no ethics to it. 

[00:21:16] Russ Altman: Are you going to be able to convince non educators who hold purse strings, let's call them the government, that these kinds of assessments are important and need to be included? Because my sense is that when it filters up to boards of education or elected leaders, a lot of that stuff goes out of the window. And they just want to know how good are they at reading comprehensive and can they do enough math to be competitive with, you know, country X? 

[00:21:43] Dan Schwartz: Yeah. Yeah. So different assessments serve different purposes. Like the big year end tests that kids take, those aren't to inform the instruction of that child. They're not even for that teacher. They're for school districts to decide are our policies working. And so it's really a different kind of assessment than me as a teacher trying to decide what should I give the kid next. So I think it's going to vary. You know, the tough question for me is should you let the kid use ChatGPT during the test? Right?

[00:22:14] And we had this argument over calculators, right? And finally they came up with ways to ask questions where it was okay if the kids had calculators. Because the calculator was doing the routine stuff. And that's not really what you cared about. What you cared about was, could the kid be innovative? Could they take a, another, a second approach to solve a problem? 

[00:22:34] Russ Altman: Yeah. 

[00:22:34] Dan Schwartz: Things like that. 

[00:22:34] Russ Altman: We, so I teach another class where it's a programming class, the students write programs, and we have switched, um, and we've actually downgraded the value. So as you know, very well, just as background, there is now an amazing, ChatGPT can also write computer code essentially. And so a lot of coding now is kind of done for you and you don't need to do it. We are trying to make sure that they understand the algorithms that we ask them to code. And so what we're doing is we're downgrading the amount of points you get for working code.

[00:23:04] You still get some, but we're upgrading the quiz about how the algorithm works. Do you understand exactly why this happened the way it did? Why is this data structure a good choice or a bad choice? And so it's forcing us, and you could have argued that we should have done this twenty years ago in the same class, but this is making it a more urgent issue, because if we don't, people can just get an automatic piece of code. They can run it. It'll work. They have no understanding of what happened. And so it's really a positive. It's putting more of a burden on us to figure out why the heck did we have them write this code in the first place? 

[00:23:39] Dan Schwartz: No, this was my point. It makes you sort of rethink what is valuable to learn. And you stop doing what was easy to grade. So I have an interesting one. This is a little nerdy. 

[00:23:51] Russ Altman: Okay. I love it. I love it. 

[00:23:52] Dan Schwartz: I teach the intro PhD statistics course in education. And lots of students say, I took statistics, right? And I'm sort of like, well, that's great. Let me ask you one question. And I say, I'm going to email you a question and you'll have five minutes to respond. You let me know when you're ready for it. And I ask them, uh, this is just for you, Russ. But why is the tail of the T distribution fat in small sample sizes? And I, what I get back usually is because they're small sample sizes.

[00:24:24] Russ Altman: Right. Or because it's the T distribution. 

[00:24:27] Dan Schwartz: Or it's, yes, even better. And then I come back and I sort of say, well, have you ever heard of the standard error? And I begin to get at the conceptual stuff, right? And, uh, I suspect if I gave it, uh, so there are ways to get conceptual questions that are really important. But you know, being able to prompt or write R code, you know, that's a good thing. You want them to learn the skills as well. 

[00:24:50] Russ Altman: Exactly. 

[00:24:51] Dan Schwartz: So I don't know, you know, when the calculator showed up, there's a big debate, right? What should students learn? Can they use the calculator? The apocryphal solution was you had to learn the regular math and the calculator now. You just had to learn twice as much. And so maybe that's what it's going to be. 

[00:25:08] Russ Altman: And that's a very likely transitional strategy and then we'll see where we end up. Okay. In the final few minutes, I, this seems like it's unrelated to AI, but I bet it's not. You've done a lot of work on physical activity and learning. You've even been on a paper recently where you talk about having a walk during a teaching session and whether you get better outcomes than if you were just standing or sitting. So tell me about that interest and tell me if it has anything to do with today's topic. 

[00:25:37] Dan Schwartz: I can make the bridge. I can do it, Russ. Right. So we did some studies. Um, I've done a lot of it. It's called embodiment where, yeah, there was, I got clued into this where, uh, I was asking people about why, about gears. And I say, you know, you have three gears in a line, and you turn the gear on the left clockwise. What does the gear on the right do? Far right. And I'd watch them, and they'd go like this with their hands. They'd model with their hands. And then I was sort of like, well, what's the basis of this? And I'd say well why? And they say because this one's turning that way that one, I go but why. And in the end, they just bottom out. They just show me their hands. They didn't say things like one molecule displaces another. 

[00:26:20] Russ Altman: Right. 

[00:26:21] Dan Schwartz: So that sort of clued me in. 

[00:26:22] Russ Altman: This pinky is going up and this other pinky is going down. 

[00:26:26] Dan Schwartz: Yes. 

[00:26:26] Russ Altman: What don't you understand about that? 

[00:26:28] Dan Schwartz: Pretty much. Well, it was nonverbal. 

[00:26:31] Russ Altman: Yeah. 

[00:26:31] Dan Schwartz: So we went on, you know, we discovered that the basis for negative numbers, right? Is actually perceptual symmetry. And we did some neuro stuff. And so the question is sort of how does this perceptual apparatus, which some people, we're just loaded with perception, right? The brain's just one giant perceiving. So how do you get that going? So part of the embodiment is my ability to take action, right? And so this is where we started, right? Right now, the AI feels very verbal, very abstract. Even the video generation, it's amazing, but it's pretty passive for me. So enter virtual worlds, they're still working on the form factor where I can move my hand in space. 

[00:27:16] Russ Altman: Yeah. 

[00:27:17] Dan Schwartz: And something will happen in the environment in response to that. You know, I think medicine is, you know, really been working on haptics so surgeons can practice. Uh, there was a great guy who made a virtual world for different heart congenital defects, and you could go in and practice surgery and see what would happen to the blood flow. So I think, uh, that embodiment where you get to bring all your senses to bear, it's not just words, but it's everything, can really do a lot for learning, for engagement, uh, not just physical skills. 

[00:27:49] Russ Altman: So that's a challenge to, I'm hearing a challenge to AI, which is as an educator, you know that this physicality can be an critical part of learning. And by the way, would this be a surprise? I mean, we're, we've been on earth evolving for several hundred million years. And, uh, you would be surprised if our ability to manipulate and look at three dimensional situations wasn't critical to learning, and yet that's not what AI is doing right now. So this is a clear challenge to AI among other things. 

[00:28:17] Dan Schwartz: Right. So, uh, I have a colleague, Renate Fruchter. And, uh, she teaches architecture, and she has students make a blueprint for the building, right? And then she feeds the blueprint to a CAD system that creates the building. She then takes the building and puts it into a physics engine, it can basically render the building and make walls so you can't move through them, and it has gravity and things like that.

[00:28:42] She then puts the, uh, original student who designed the building in a wheelchair and has them try to navigate through that environment. At which point they sort of understand, oh this is why you need so much space so they can turn around, so they can navigate near the door. I am sure that is an incredibly compelling experience that allows them to be generative about all their future designs.

[00:29:03] So yeah, this is a challenge and part of the co-mingling of the AI and the virtual worlds, I think this is a big challenge. It's computationally very heavy, but it will open the door for lots of ways of teaching that you just couldn't do before. 

[00:29:17] Russ Altman: Thanks to Dan Schwartz. That was the future of educational technology.

[00:29:21] You've been listening to The Future of Everything and I'm Russ Altman. You know what? We have an archive with more than 250 back episodes of The Future of Everything. So you have instant access to a wide array of discussions that can keep you entertained and informed. Also, remember to rate, review, and follow. I care deeply about that request. 

[00:29:41] And also, if you want to follow me, you can follow me on X @ @RBAltman, and you can follow Stanford Engineering @ StanfordENG.

James Landay: Paving a Path for Human-Centered Computing

Exploring space-related, human-centered design

Engineers conduct first in-orbit test of ‘swarm’ satellite autonomous navigation

Publication

Global education monitoring report summary, 2023: technology in education: a tool on whose terms? (hin)

The new 2023 GEM Report on  Technology in education: A tool on whose terms?  addresses the use of technology in education around the world through the lenses of relevance, equity, scalability and sustainability.

It argues that education systems should always ensure that learners’ interests are placed at the center and that digital technologies are used to support an education based on human interaction rather than aiming at substituting it. The report looks at ways in which technology can help reach disadvantaged learners but also ensure more knowledge reaches more learners in more engaging and cheaper formats. It focuses on how quality can be improved, both in teaching and learning basic skills, and in developing the digital skills needed in daily life. It recognizes the role of technology in system management with special reference to assessment data and other education management information.

Related items

• Guidelines and tools
• Policy Advice
• Country page: India
• Topics: Flagship report
• Region: Asia and the Pacific
• UNESCO Office in New Delhi
• See more add

More on this subject

Other recent publications

• Future Students
• Current Students
• Faculty/Staff

News and Media

• News & Media Home
• Research Stories
• School’s In
• In the Media

You are here

How technology is reinventing education.

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of  Stanford Graduate School of Education  (GSE), who is also a professor of educational technology at the GSE and faculty director of the  Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately  worried  that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or  coach  students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the  AI + Education initiative  at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of  CRAFT  (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the  Digital Learning initiative  at the Stanford Accelerator for Learning, which runs a program exploring the use of  virtual field trips  to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

More Stories

⟵ Go to all Research Stories

Get the Educator

Subscribe to our monthly newsletter.

Stanford Graduate School of Education

482 Galvez Mall Stanford, CA 94305-3096 Tel: (650) 723-2109

• Contact Admissions
• GSE Leadership
• Site Feedback
• Web Accessibility
• Career Resources
• Faculty Open Positions
• Explore Courses
• Academic Calendar
• Office of the Registrar
• Cubberley Library
• StanfordWho
• StanfordYou

Improving lives through learning

• Stanford Home
• Maps & Directions
• Search Stanford
• Emergency Info
• Terms of Use
• Non-Discrimination
• Accessibility

© Stanford University , Stanford , California 94305 .

Become an Insider

Sign up today to receive premium content.

Technology in the Classroom & The Benefits for K-12 Schools

Rebecca Torchia is a web editor for  EdTech: Focus on K–12 . Previously, she has produced podcasts and written for several publications in Maryland, Washington, D.C., and her hometown of Pittsburgh.

Technology integration is no longer about whether tech belongs in classrooms. In today’s education landscape, it pertains to how technology is chosen and used for learning.

Schools have received waves of government funding for educational technology. Administrators and IT leadership still have  until September 2022 and September 2023  to obligate ESSER I and ESSER II funds, respectively. To get the best return on investment with this funding, districts must ensure technology integration is done effectively.

Students benefit from technology integration when it is done well. It can lead to a more equitable educational experience and give students the tools to be successful in life.

Click the banner to unlock exclusive tech content when you register as an Insider.

What Does Technology in The Classroom Look Like Today?

Technology integration is the use of technology in teaching and learning to achieve academic goals.

“I don’t use tech unless it solves a problem I have in the classroom,” says Lisa Highfill, a technology integration specialist at  Pleasanton Virtual Academy  in California.

For example, Highfill says, she’ll use a  Jamboard  where students can post their responses instead of calling on them one at a time. “Then, when they’re all quiet, what are they doing? They’re reading each other’s comments.”

Meaningful tech integration should be done thoughtfully to enhance a learning experience. “You don’t want to use technology just for technology’s sake,” says Melissa Lim, a technology integration specialist at Oregon’s  Portland Public Schools . “We recommend using the Triple E Framework as a simple tool to help determine if it’s worth using technology or if you’re just using it as a substitute.”

The Triple E Framework  was developed by Liz Kolb, a clinical associate professor of education and learning technologies at the University of Michigan. When K–12 IT leaders evaluate new tech based on this framework, they can determine “how well technology tools integrated into lessons are helping students engage in, enhance and extend learning goals,” according to Kolb’s website for the framework.

“It’s all about the learning first,” Lim says.

Why Is Integrating Technology Important in Education?

Technology integration in Education is important for multiple reasons. It makes learning more equitable for K–12 students, and — when used in lower grades — it sets them up for success in school and, moving forward, in their careers.

“If you’re a teacher who doesn’t use a lot of technology, your students aren’t getting equitable access to learning experiences that another teacher who uses technology is giving to their students,” Lim says.

Melissa Lim Technology Integration Specialist, Portland Public Schools

Now that many students have devices and access to technology, educators and school leaders must work to  narrow the digital divide  through equity of use. If students aren’t exposed to technology and taught how to use it, they will fall behind their peers.

“Educators should make sure logging in is a really easy, smooth process,” Highfill says. “Once I get everyone logged in, the No. 1 thing I have to get students to learn how to do is share their screen.”

This not only helps her work through problems with students, she says, but also helps students take  a more active role in their learning . Students will find new ways to achieve a goal or manipulate a technology and can show the class — and the teacher — how they’ve accomplished it by sharing their screen. “You empower them and put them in the teaching role,” Highfill adds.

What Are the Benefits of Technology for Students?

Through technology, schools can support all students. There are roughly 60 grade school students and nearly 250 high school students enrolled at Pleasanton Virtual Academy. “I’m so excited our district put in that investment,” Highfill says. “We’re a public school virtual academy. They invested in a quality virtual academy to meet the needs of all students.”

Even students who are learning in an in-person environment are  using technology in their daily lives . Integrating it into the classroom gives them an opportunity to learn to use tech in a meaningful way.

READ MORE:   Build the themes of digital citizenship into instruction and business planning.

“If you have the skills and know how to research and find information and discern whether that information is true or not, that’s going to help you not only in school with your schoolwork, but also with life in general,” Lim says.

“I watch the kids, and they’re very addicted to their devices,” says Highfill. “So, it’s my new teaching point: How can you take a digital diet, and how can you identify when tech is not doing good things for you?”

Highfill says that anytime there’s a fear about introducing technology to the classroom, educators should use that. “We have to teach students how to take care of themselves if they’re going to use technology,” she says.

• Digital Content
• Online Learning
• Digital Workspace
• Digital Citizenship
• Digital Transformation

Related Articles

CDW Education Events

Find out what's happening in your area.

Copyright © 2024 CDW LLC 200 N. Milwaukee Avenue , Vernon Hills, IL 60061 Do Not Sell My Personal Information

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

Stella timotheou.

1 CYENS Center of Excellence & Cyprus University of Technology (Cyprus Interaction Lab), Cyprus, CYENS Center of Excellence & Cyprus University of Technology, Nicosia-Limassol, Cyprus

Ourania Miliou

Yiannis dimitriadis.

2 Universidad de Valladolid (UVA), Spain, Valladolid, Spain

Sara Villagrá Sobrino

Nikoleta giannoutsou, romina cachia.

3 JRC - Joint Research Centre of the European Commission, Seville, Spain

Alejandra Martínez Monés

Andri ioannou, associated data.

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​ Table1 1 .

Inclusion and exclusion criteria for the selection of resources on the impact of digital technologies on education

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​ Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​ Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

The impact of digital technologies on schools’ stakeholders based on the literature review

Tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​ Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

Factors that affect the impact of ICTs on education

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

Data availability statement

Declarations.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Archer K, Savage R, Sanghera-Sidhu S, Wood E, Gottardo A, Chen V. Examining the effectiveness of technology use in classrooms: A tertiary meta-analysis. Computers & Education. 2014; 78 :140–149. doi: 10.1016/j.compedu.2014.06.001. [ CrossRef ] [ Google Scholar ]
• Aromatario O, Van Hoye A, Vuillemin A, Foucaut AM, Pommier J, Cambon L. Using theory of change to develop an intervention theory for designing and evaluating behavior change SDApps for healthy eating and physical exercise: The OCAPREV theory. BMC Public Health. 2019; 19 (1):1–12. doi: 10.1186/s12889-019-7828-4. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arztmann, M., Hornstra, L., Jeuring, J., & Kester, L. (2022). Effects of games in STEM education: A meta-analysis on the moderating role of student background characteristics. Studies in Science Education , 1-37. 10.1080/03057267.2022.2057732
• Bado N. Game-based learning pedagogy: A review of the literature. Interactive Learning Environments. 2022; 30 (5):936–948. doi: 10.1080/10494820.2019.1683587. [ CrossRef ] [ Google Scholar ]
• Balanskat, A. (2009). Study of the impact of technology in primary schools – Synthesis Report. Empirica and European Schoolnet. Retrieved 30 June 2022 from: https://erte.dge.mec.pt/sites/default/files/Recursos/Estudos/synthesis_report_steps_en.pdf
• Balanskat, A. (2006). The ICT Impact Report: A review of studies of ICT impact on schools in Europe, European Schoolnet. Retrieved 30 June 2022 from:  https://en.unesco.org/icted/content/ict-impact-report-review-studies-ict-impact-schools-europe
• Balanskat, A., Blamire, R., & Kefala, S. (2006). The ICT impact report.  European Schoolnet . Retrieved from: http://colccti.colfinder.org/sites/default/files/ict_impact_report_0.pdf
• Balyer, A., & Öz, Ö. (2018). Academicians’ views on digital transformation in education. International Online Journal of Education and Teaching (IOJET), 5 (4), 809–830. Retrieved 30 June 2022 from  http://iojet.org/index.php/IOJET/article/view/441/295
• Baragash RS, Al-Samarraie H, Moody L, Zaqout F. Augmented reality and functional skills acquisition among individuals with special needs: A meta-analysis of group design studies. Journal of Special Education Technology. 2022; 37 (1):74–81. doi: 10.1177/0162643420910413. [ CrossRef ] [ Google Scholar ]
• Bates, A. W. (2015). Teaching in a digital age: Guidelines for designing teaching and learning . Open Educational Resources Collection . 6. Retrieved 30 June 2022 from: https://irl.umsl.edu/oer/6
• Bingimlas KA. Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, Science and Technology Education. 2009; 5 (3):235–245. doi: 10.12973/ejmste/75275. [ CrossRef ] [ Google Scholar ]
• Blaskó Z, Costa PD, Schnepf SV. Learning losses and educational inequalities in Europe: Mapping the potential consequences of the COVID-19 crisis. Journal of European Social Policy. 2022; 32 (4):361–375. doi: 10.1177/09589287221091687. [ CrossRef ] [ Google Scholar ]
• Bocconi S, Lightfoot M. Scaling up and integrating the selfie tool for schools' digital capacity in education and training systems: Methodology and lessons learnt. European Training Foundation. 2021 doi: 10.2816/907029,JRC123936. [ CrossRef ] [ Google Scholar ]
• Brooks, D. C., & McCormack, M. (2020). Driving Digital Transformation in Higher Education . Retrieved 30 June 2022 from: https://library.educause.edu/-/media/files/library/2020/6/dx2020.pdf?la=en&hash=28FB8C377B59AFB1855C225BBA8E3CFBB0A271DA
• Cachia, R., Chaudron, S., Di Gioia, R., Velicu, A., & Vuorikari, R. (2021). Emergency remote schooling during COVID-19, a closer look at European families. Retrieved 30 June 2022 from  https://publications.jrc.ec.europa.eu/repository/handle/JRC125787
• Çelik B. The effects of computer simulations on students’ science process skills: Literature review. Canadian Journal of Educational and Social Studies. 2022; 2 (1):16–28. doi: 10.53103/cjess.v2i1.17. [ CrossRef ] [ Google Scholar ]
• Chapman, C., & Sammons, P. (2013). School Self-Evaluation for School Improvement: What Works and Why? . CfBT Education Trust. 60 Queens Road, Reading, RG1 4BS, England.
• Chauhan S. A meta-analysis of the impact of technology on learning effectiveness of elementary students. Computers & Education. 2017; 105 :14–30. doi: 10.1016/j.compedu.2016.11.005. [ CrossRef ] [ Google Scholar ]
• Chen, Q., Chan, K. L., Guo, S., Chen, M., Lo, C. K. M., & Ip, P. (2022a). Effectiveness of digital health interventions in reducing bullying and cyberbullying: a meta-analysis. Trauma, Violence, & Abuse , 15248380221082090. 10.1177/15248380221082090 [ PubMed ]
• Chen B, Wang Y, Wang L. The effects of virtual reality-assisted language learning: A meta-analysis. Sustainability. 2022; 14 (6):3147. doi: 10.3390/su14063147. [ CrossRef ] [ Google Scholar ]
• Cheok ML, Wong SL. Predictors of e-learning satisfaction in teaching and learning for school teachers: A literature review. International Journal of Instruction. 2015; 8 (1):75–90. doi: 10.12973/iji.2015.816a. [ CrossRef ] [ Google Scholar ]
• Cheung, A. C., & Slavin, R. E. (2011). The Effectiveness of Education Technology for Enhancing Reading Achievement: A Meta-Analysis. Center for Research and reform in Education .
• Coban, M., Bolat, Y. I., & Goksu, I. (2022). The potential of immersive virtual reality to enhance learning: A meta-analysis. Educational Research Review , 100452. 10.1016/j.edurev.2022.100452
• Condie, R., & Munro, R. K. (2007). The impact of ICT in schools-a landscape review. Retrieved 30 June 2022 from: https://oei.org.ar/ibertic/evaluacion/sites/default/files/biblioteca/33_impact_ict_in_schools.pdf
• Conrads, J., Rasmussen, M., Winters, N., Geniet, A., Langer, L., (2017). Digital Education Policies in Europe and Beyond: Key Design Principles for More Effective Policies. Redecker, C., P. Kampylis, M. Bacigalupo, Y. Punie (ed.), EUR 29000 EN, Publications Office of the European Union, Luxembourg, 10.2760/462941
• Costa P, Castaño-Muñoz J, Kampylis P. Capturing schools’ digital capacity: Psychometric analyses of the SELFIE self-reflection tool. Computers & Education. 2021; 162 :104080. doi: 10.1016/j.compedu.2020.104080. [ CrossRef ] [ Google Scholar ]
• Cussó-Calabuig R, Farran XC, Bosch-Capblanch X. Effects of intensive use of computers in secondary school on gender differences in attitudes towards ICT: A systematic review. Education and Information Technologies. 2018; 23 (5):2111–2139. doi: 10.1007/s10639-018-9706-6. [ CrossRef ] [ Google Scholar ]
• Daniel SJ. Education and the COVID-19 pandemic. Prospects. 2020; 49 (1):91–96. doi: 10.1007/s11125-020-09464-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Delcker J, Ifenthaler D. Teachers’ perspective on school development at German vocational schools during the Covid-19 pandemic. Technology, Pedagogy and Education. 2021; 30 (1):125–139. doi: 10.1080/1475939X.2020.1857826. [ CrossRef ] [ Google Scholar ]
• Delgado, A., Wardlow, L., O’Malley, K., & McKnight, K. (2015). Educational technology: A review of the integration, resources, and effectiveness of technology in K-12 classrooms. Journal of Information Technology Education Research , 14, 397. Retrieved 30 June 2022 from  http://www.jite.org/documents/Vol14/JITEv14ResearchP397-416Delgado1829.pdf
• De Silva MJ, Breuer E, Lee L, Asher L, Chowdhary N, Lund C, Patel V. Theory of change: A theory-driven approach to enhance the Medical Research Council's framework for complex interventions. Trials. 2014; 15 (1):1–13. doi: 10.1186/1745-6215-15-267. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Di Pietro G, Biagi F, Costa P, Karpiński Z, Mazza J. The likely impact of COVID-19 on education: Reflections based on the existing literature and recent international datasets. Publications Office of the European Union; 2020. [ Google Scholar ]
• Elkordy A, Lovinelli J. Competencies, Culture, and Change: A Model for Digital Transformation in K12 Educational Contexts. In: Ifenthaler D, Hofhues S, Egloffstein M, Helbig C, editors. Digital Transformation of Learning Organizations. Springer; 2020. pp. 203–219. [ Google Scholar ]
• Eng TS. The impact of ICT on learning: A review of research. International Education Journal. 2005; 6 (5):635–650. [ Google Scholar ]
• European Commission. (2020). Digital Education Action Plan 2021 – 2027. Resetting education and training for the digital age. Retrieved 30 June 2022 from  https://ec.europa.eu/education/sites/default/files/document-library-docs/deap-communication-sept2020_en.pdf
• European Commission. (2019). 2 nd survey of schools: ICT in education. Objective 1: Benchmark progress in ICT in schools . Retrieved 30 June 2022 from: https://data.europa.eu/euodp/data/storage/f/2019-03-19T084831/FinalreportObjective1-BenchmarkprogressinICTinschools.pdf
• Eurydice. (2019). Digital Education at School in Europe , Luxembourg: Publications Office of the European Union. Retrieved 30 June 2022 from: https://eacea.ec.europa.eu/national-policies/eurydice/content/digital-education-school-europe_en
• Escueta, M., Quan, V., Nickow, A. J., & Oreopoulos, P. (2017). Education technology: An evidence-based review. Retrieved 30 June 2022 from  https://ssrn.com/abstract=3031695
• Fadda D, Pellegrini M, Vivanet G, Zandonella Callegher C. Effects of digital games on student motivation in mathematics: A meta-analysis in K-12. Journal of Computer Assisted Learning. 2022; 38 (1):304–325. doi: 10.1111/jcal.12618. [ CrossRef ] [ Google Scholar ]
• Fernández-Gutiérrez M, Gimenez G, Calero J. Is the use of ICT in education leading to higher student outcomes? Analysis from the Spanish Autonomous Communities. Computers & Education. 2020; 157 :103969. doi: 10.1016/j.compedu.2020.103969. [ CrossRef ] [ Google Scholar ]
• Ferrari, A., Cachia, R., & Punie, Y. (2011). Educational change through technology: A challenge for obligatory schooling in Europe. Lecture Notes in Computer Science , 6964 , 97–110. Retrieved 30 June 2022  https://link.springer.com/content/pdf/10.1007/978-3-642-23985-4.pdf
• Fielding, K., & Murcia, K. (2022). Research linking digital technologies to young children’s creativity: An interpretive framework and systematic review. Issues in Educational Research , 32 (1), 105–125. Retrieved 30 June 2022 from  http://www.iier.org.au/iier32/fielding-abs.html
• Friedel, H., Bos, B., Lee, K., & Smith, S. (2013). The impact of mobile handheld digital devices on student learning: A literature review with meta-analysis. In Society for Information Technology & Teacher Education International Conference (pp. 3708–3717). Association for the Advancement of Computing in Education (AACE).
• Fu JS. ICT in education: A critical literature review and its implications. International Journal of Education and Development Using Information and Communication Technology (IJEDICT) 2013; 9 (1):112–125. [ Google Scholar ]
• Gaol FL, Prasolova-Førland E. Special section editorial: The frontiers of augmented and mixed reality in all levels of education. Education and Information Technologies. 2022; 27 (1):611–623. doi: 10.1007/s10639-021-10746-2. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garzón J, Acevedo J. Meta-analysis of the impact of Augmented Reality on students’ learning gains. Educational Research Review. 2019; 27 :244–260. doi: 10.1016/j.edurev.2019.04.001. [ CrossRef ] [ Google Scholar ]
• Garzón, J., Baldiris, S., Gutiérrez, J., & Pavón, J. (2020). How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis. Educational Research Review , 100334. 10.1016/j.edurev.2020.100334
• Grgurović M, Chapelle CA, Shelley MC. A meta-analysis of effectiveness studies on computer technology-supported language learning. ReCALL. 2013; 25 (2):165–198. doi: 10.1017/S0958344013000013. [ CrossRef ] [ Google Scholar ]
• Haßler B, Major L, Hennessy S. Tablet use in schools: A critical review of the evidence for learning outcomes. Journal of Computer Assisted Learning. 2016; 32 (2):139–156. doi: 10.1111/jcal.12123. [ CrossRef ] [ Google Scholar ]
• Haleem A, Javaid M, Qadri MA, Suman R. Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers. 2022; 3 :275–285. doi: 10.1016/j.susoc.2022.05.004. [ CrossRef ] [ Google Scholar ]
• Hardman J. Towards a pedagogical model of teaching with ICTs for mathematics attainment in primary school: A review of studies 2008–2018. Heliyon. 2019; 5 (5):e01726. doi: 10.1016/j.heliyon.2019.e01726. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hattie J, Rogers HJ, Swaminathan H. The role of meta-analysis in educational research. In: Reid AD, Hart P, Peters MA, editors. A companion to research in education. Springer; 2014. pp. 197–207. [ Google Scholar ]
• Hattie J. Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. 2008 doi: 10.4324/9780203887332. [ CrossRef ] [ Google Scholar ]
• Higgins S, Xiao Z, Katsipataki M. The impact of digital technology on learning: A summary for the education endowment foundation. Education Endowment Foundation and Durham University; 2012. [ Google Scholar ]
• Higgins, K., Huscroft-D’Angelo, J., & Crawford, L. (2019). Effects of technology in mathematics on achievement, motivation, and attitude: A meta-analysis. Journal of Educational Computing Research , 57(2), 283-319.
• Hillmayr D, Ziernwald L, Reinhold F, Hofer SI, Reiss KM. The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education. 2020; 153 (1038):97. doi: 10.1016/j.compedu.2020.103897. [ CrossRef ] [ Google Scholar ]
• Istenic Starcic A, Bagon S. ICT-supported learning for inclusion of people with special needs: Review of seven educational technology journals, 1970–2011. British Journal of Educational Technology. 2014; 45 (2):202–230. doi: 10.1111/bjet.12086. [ CrossRef ] [ Google Scholar ]
• Jewitt C, Clark W, Hadjithoma-Garstka C. The use of learning platforms to organise learning in English primary and secondary schools. Learning, Media and Technology. 2011; 36 (4):335–348. doi: 10.1080/17439884.2011.621955. [ CrossRef ] [ Google Scholar ]
• JISC. (2020). What is digital transformation?.  Retrieved 30 June 2022 from: https://www.jisc.ac.uk/guides/digital-strategy-framework-for-university-leaders/what-is-digital-transformation
• Kalati, A. T., & Kim, M. S. (2022). What is the effect of touchscreen technology on young children’s learning?: A systematic review. Education and Information Technologies , 1-19. 10.1007/s10639-021-10816-5
• Kalemkuş, J., & Kalemkuş, F. (2022). Effect of the use of augmented reality applications on academic achievement of student in science education: Meta-analysis review. Interactive Learning Environments , 1-18. 10.1080/10494820.2022.2027458
• Kao C-W. The effects of digital game-based learning task in English as a foreign language contexts: A meta-analysis. Education Journal. 2014; 42 (2):113–141. [ Google Scholar ]
• Kampylis P, Punie Y, Devine J. Promoting effective digital-age learning - a European framework for digitally competent educational organisations. JRC Technical Reports. 2015 doi: 10.2791/54070. [ CrossRef ] [ Google Scholar ]
• Kazu IY, Yalçin CK. Investigation of the effectiveness of hybrid learning on academic achievement: A meta-analysis study. International Journal of Progressive Education. 2022; 18 (1):249–265. doi: 10.29329/ijpe.2022.426.14. [ CrossRef ] [ Google Scholar ]
• Koh C. A qualitative meta-analysis on the use of serious games to support learners with intellectual and developmental disabilities: What we know, what we need to know and what we can do. International Journal of Disability, Development and Education. 2022; 69 (3):919–950. doi: 10.1080/1034912X.2020.1746245. [ CrossRef ] [ Google Scholar ]
• König J, Jäger-Biela DJ, Glutsch N. Adapting to online teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education. 2020; 43 (4):608–622. doi: 10.1080/02619768.2020.1809650. [ CrossRef ] [ Google Scholar ]
• Lawrence JE, Tar UA. Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International. 2018; 55 (1):79–105. doi: 10.1080/09523987.2018.1439712. [ CrossRef ] [ Google Scholar ]
• Lee, S., Kuo, L. J., Xu, Z., & Hu, X. (2020). The effects of technology-integrated classroom instruction on K-12 English language learners’ literacy development: A meta-analysis. Computer Assisted Language Learning , 1-32. 10.1080/09588221.2020.1774612
• Lei, H., Chiu, M. M., Wang, D., Wang, C., & Xie, T. (2022a). Effects of game-based learning on students’ achievement in science: a meta-analysis. Journal of Educational Computing Research . 10.1177/07356331211064543
• Lei H, Wang C, Chiu MM, Chen S. Do educational games affect students' achievement emotions? Evidence from a meta-analysis. Journal of Computer Assisted Learning. 2022; 38 (4):946–959. doi: 10.1111/jcal.12664. [ CrossRef ] [ Google Scholar ]
• Liao YKC, Chang HW, Chen YW. Effects of computer application on elementary school student's achievement: A meta-analysis of students in Taiwan. Computers in the Schools. 2007; 24 (3–4):43–64. doi: 10.1300/J025v24n03_04. [ CrossRef ] [ Google Scholar ]
• Li Q, Ma X. A meta-analysis of the effects of computer technology on school students’ mathematics learning. Educational Psychology Review. 2010; 22 (3):215–243. doi: 10.1007/s10648-010-9125-8. [ CrossRef ] [ Google Scholar ]
• Liu, M., Pang, W., Guo, J., & Zhang, Y. (2022). A meta-analysis of the effect of multimedia technology on creative performance. Education and Information Technologies , 1-28. 10.1007/s10639-022-10981-1
• Lu Z, Chiu MM, Cui Y, Mao W, Lei H. Effects of game-based learning on students’ computational thinking: A meta-analysis. Journal of Educational Computing Research. 2022 doi: 10.1177/07356331221100740. [ CrossRef ] [ Google Scholar ]
• Martinez L, Gimenes M, Lambert E. Entertainment video games for academic learning: A systematic review. Journal of Educational Computing Research. 2022 doi: 10.1177/07356331211053848. [ CrossRef ] [ Google Scholar ]
• Mayne J. Useful theory of change models. Canadian Journal of Program Evaluation. 2015; 30 (2):119–142. doi: 10.3138/cjpe.230. [ CrossRef ] [ Google Scholar ]
• Moran J, Ferdig RE, Pearson PD, Wardrop J, Blomeyer RL., Jr Technology and reading performance in the middle-school grades: A meta-analysis with recommendations for policy and practice. Journal of Literacy Research. 2008; 40 (1):6–58. doi: 10.1080/10862960802070483. [ CrossRef ] [ Google Scholar ]
• OECD. (2015). Students, Computers and Learning: Making the Connection . PISA, OECD Publishing, Paris. Retrieved from: 10.1787/9789264239555-en
• OECD. (2021). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. Retrieved from: https://www.oecd-ilibrary.org/education/oecd-digital-education-outlook-2021_589b283f-en
• Pan Y, Ke F, Xu X. A systematic review of the role of learning games in fostering mathematics education in K-12 settings. Educational Research Review. 2022; 36 :100448. doi: 10.1016/j.edurev.2022.100448. [ CrossRef ] [ Google Scholar ]
• Pettersson F. Understanding digitalization and educational change in school by means of activity theory and the levels of learning concept. Education and Information Technologies. 2021; 26 (1):187–204. doi: 10.1007/s10639-020-10239-8. [ CrossRef ] [ Google Scholar ]
• Pihir, I., Tomičić-Pupek, K., & Furjan, M. T. (2018). Digital transformation insights and trends. In Central European Conference on Information and Intelligent Systems (pp. 141–149). Faculty of Organization and Informatics Varazdin. Retrieved 30 June 2022 from https://www.proquest.com/conference-papers-proceedings/digital-transformation-insights-trends/docview/2125639934/se-2
• Punie, Y., Zinnbauer, D., & Cabrera, M. (2006). A review of the impact of ICT on learning. Working Paper prepared for DG EAC. Retrieved 30 June 2022 from: http://www.eurosfaire.prd.fr/7pc/doc/1224678677_jrc47246n.pdf
• Quah CY, Ng KH. A systematic literature review on digital storytelling authoring tool in education: January 2010 to January 2020. International Journal of Human-Computer Interaction. 2022; 38 (9):851–867. doi: 10.1080/10447318.2021.1972608. [ CrossRef ] [ Google Scholar ]
• Ran H, Kim NJ, Secada WG. A meta-analysis on the effects of technology's functions and roles on students' mathematics achievement in K-12 classrooms. Journal of computer assisted learning. 2022; 38 (1):258–284. doi: 10.1111/jcal.12611. [ CrossRef ] [ Google Scholar ]
• Ređep, N. B. (2021). Comparative overview of the digital preparedness of education systems in selected CEE countries. Center for Policy Studies. CEU Democracy Institute .
• Rott, B., & Marouane, C. (2018). Digitalization in schools–organization, collaboration and communication. In Digital Marketplaces Unleashed (pp. 113–124). Springer, Berlin, Heidelberg.
• Savva M, Higgins S, Beckmann N. Meta-analysis examining the effects of electronic storybooks on language and literacy outcomes for children in grades Pre-K to grade 2. Journal of Computer Assisted Learning. 2022; 38 (2):526–564. doi: 10.1111/jcal.12623. [ CrossRef ] [ Google Scholar ]
• Schmid RF, Bernard RM, Borokhovski E, Tamim RM, Abrami PC, Surkes MA, Wade CA, Woods J. The effects of technology use in postsecondary education: A meta-analysis of classroom applications. Computers & Education. 2014; 72 :271–291. doi: 10.1016/j.compedu.2013.11.002. [ CrossRef ] [ Google Scholar ]
• Schuele CM, Justice LM. The importance of effect sizes in the interpretation of research: Primer on research: Part 3. The ASHA Leader. 2006; 11 (10):14–27. doi: 10.1044/leader.FTR4.11102006.14. [ CrossRef ] [ Google Scholar ]
• Schwabe, A., Lind, F., Kosch, L., & Boomgaarden, H. G. (2022). No negative effects of reading on screen on comprehension of narrative texts compared to print: A meta-analysis. Media Psychology , 1-18. 10.1080/15213269.2022.2070216
• Sellar S. Data infrastructure: a review of expanding accountability systems and large-scale assessments in education. Discourse: Studies in the Cultural Politics of Education. 2015; 36 (5):765–777. doi: 10.1080/01596306.2014.931117. [ CrossRef ] [ Google Scholar ]
• Stock WA. Systematic coding for research synthesis. In: Cooper H, Hedges LV, editors. The handbook of research synthesis, 236. Russel Sage; 1994. pp. 125–138. [ Google Scholar ]
• Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence , 100065. 10.1016/j.caeai.2022.100065
• Su J, Yang W. Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence. 2022; 3 :100049. doi: 10.1016/j.caeai.2022.100049. [ CrossRef ] [ Google Scholar ]
• Sung YT, Chang KE, Liu TC. The effects of integrating mobile devices with teaching and learning on students' learning performance: A meta-analysis and research synthesis. Computers & Education. 2016; 94 :252–275. doi: 10.1016/j.compedu.2015.11.008. [ CrossRef ] [ Google Scholar ]
• Talan T, Doğan Y, Batdı V. Efficiency of digital and non-digital educational games: A comparative meta-analysis and a meta-thematic analysis. Journal of Research on Technology in Education. 2020; 52 (4):474–514. doi: 10.1080/15391523.2020.1743798. [ CrossRef ] [ Google Scholar ]
• Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational research, 81 (1), 4–28. Retrieved 30 June 2022 from 10.3102/0034654310393361
• Tamim, R. M., Borokhovski, E., Pickup, D., Bernard, R. M., & El Saadi, L. (2015). Tablets for teaching and learning: A systematic review and meta-analysis. Commonwealth of Learning. Retrieved from: http://oasis.col.org/bitstream/handle/11599/1012/2015_Tamim-et-al_Tablets-for-Teaching-and-Learning.pdf
• Tang C, Mao S, Xing Z, Naumann S. Improving student creativity through digital technology products: A literature review. Thinking Skills and Creativity. 2022; 44 :101032. doi: 10.1016/j.tsc.2022.101032. [ CrossRef ] [ Google Scholar ]
• Tolani-Brown, N., McCormac, M., & Zimmermann, R. (2011). An analysis of the research and impact of ICT in education in developing country contexts. In ICTs and sustainable solutions for the digital divide: Theory and perspectives (pp. 218–242). IGI Global.
• Trucano, M. (2005). Knowledge Maps: ICTs in Education. Washington, DC: info Dev / World Bank. Retrieved 30 June 2022 from  https://files.eric.ed.gov/fulltext/ED496513.pdf
• Ulum H. The effects of online education on academic success: A meta-analysis study. Education and Information Technologies. 2022; 27 (1):429–450. doi: 10.1007/s10639-021-10740-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Underwood, J. D. (2009). The impact of digital technology: A review of the evidence of the impact of digital technologies on formal education. Retrieved 30 June 2022 from: http://dera.ioe.ac.uk/id/eprint/10491
• Verschaffel, L., Depaepe, F., & Mevarech, Z. (2019). Learning Mathematics in metacognitively oriented ICT-Based learning environments: A systematic review of the literature. Education Research International , 2019 . 10.1155/2019/3402035
• Villena-Taranilla R, Tirado-Olivares S, Cózar-Gutiérrez R, González-Calero JA. Effects of virtual reality on learning outcomes in K-6 education: A meta-analysis. Educational Research Review. 2022; 35 :100434. doi: 10.1016/j.edurev.2022.100434. [ CrossRef ] [ Google Scholar ]
• Voogt J, Knezek G, Cox M, Knezek D, ten Brummelhuis A. Under which conditions does ICT have a positive effect on teaching and learning? A call to action. Journal of Computer Assisted Learning. 2013; 29 (1):4–14. doi: 10.1111/j.1365-2729.2011.00453.x. [ CrossRef ] [ Google Scholar ]
• Vuorikari, R., Punie, Y., & Cabrera, M. (2020). Emerging technologies and the teaching profession: Ethical and pedagogical considerations based on near-future scenarios  (No. JRC120183). Joint Research Centre. Retrieved 30 June 2022 from: https://publications.jrc.ec.europa.eu/repository/handle/JRC120183
• Wang LH, Chen B, Hwang GJ, Guan JQ, Wang YQ. Effects of digital game-based STEM education on students’ learning achievement: A meta-analysis. International Journal of STEM Education. 2022; 9 (1):1–13. doi: 10.1186/s40594-022-00344-0. [ CrossRef ] [ Google Scholar ]
• Wen X, Walters SM. The impact of technology on students’ writing performances in elementary classrooms: A meta-analysis. Computers and Education Open. 2022; 3 :100082. doi: 10.1016/j.caeo.2022.100082. [ CrossRef ] [ Google Scholar ]
• Zheng B, Warschauer M, Lin CH, Chang C. Learning in one-to-one laptop environments: A meta-analysis and research synthesis. Review of Educational Research. 2016; 86 (4):1052–1084. doi: 10.3102/0034654316628645. [ CrossRef ] [ Google Scholar ]

Educational Technology Research in Higher Education: New Considerations and Evolving Goals

Challenges and concerns about technology’s role in education.

Substantial and unbiased evidence regarding the effects of educational technology is scarce. The rapid evolution of technology often means it outpaces the ability of researchers and educators to thoroughly evaluate its impact on education. As a result, robust evidence regarding the added value of digital technology in educational settings is in short supply. A recently released UNESCO report on technology's use in education highlights the fact that educational technology products change every three years, on average. The lack of research is further evident in the report, which found that in the United Kingdom, only 7% of educational technology companies had conducted randomized controlled trials, and a survey of teachers and administrators in 17 U.S. states showed that only 11% requested peer-reviewed evidence prior to the adoption of technologies.

A recent meta-analysis of educational technology indicates that the research we have on educational technology is not the research we need and that "perhaps we can better transform education by fostering incremental changes through collaborative research and development with practitioners." In a story produced by The Hechinger Report , Kathryn Stack, who spent 27 years at the White House Office of Management and Budget and helped design grant programs that award money based on evidence of effectiveness, said, "[W]e're still in a place where there isn't a ton of great evidence about what works in educational technology." This lack of comprehensive research makes it challenging for faculty and leadership to make informed decisions about integrating educational technology into classrooms. Without substantial evidence on the impact of these tools, it becomes harder to determine their true value in enhancing teaching and learning experiences.

Efforts are being made by researchers and organizations to bridge this gap by conducting more rigorous studies and sharing findings through academic journals and conferences. However, the ongoing challenge will remain keeping up with the pace at which technology evolves while simultaneously providing reliable evidence of its effectiveness in educational settings.

Online content is produced by dominant groups, affecting access to it. According to the recent UNESCO report on technology in higher education, nearly 90% of content in higher education repositories with open education resource collections was created in Europe and North America; 92% of the content in the OER Commons global library is in English.

Additionally, the report suggests that massive open online courses (MOOCs) mainly benefit educated learners and those from richer countries. Still, a lot of learners enroll in those MOOCs. In 2021, the number of students enrolled in MOOCs worldwide continued to grow to over 220 million . Although this access to education is commendable, it also raises concerns regarding the regulation and quality control of online content. The lack of diversity in online content can limit perspectives and hinder inclusive learning experiences. It is crucial to ensure that digital platforms promote diverse voices and offer a wide range of educational resources that cater to different backgrounds, cultures, and learning styles.

Access to technology and to stable internet continues to be a big concern. The Tyton Partners report Time for Class 2023 found that 40% of students have experienced stress due to unstable internet connectivity and 22% have experienced stress due to not having access to a computer or laptop. Not only that, but students of color were six percentage points more likely to have experienced stress due to lack of access to devices and the internet.

The rapid growth of online content since the pandemic has brought about significant challenges in terms of equitable access, and as the data from the 2023 EDUCAUSE student and faculty technology reports show, the desire and need for online content isn't going away. Institutions will need to consider how they can best bridge the gap of access.

Engaging students in higher education with educational technology

• Open access
• Published: 06 August 2024

Cite this article

You have full access to this open access article

• Mikkel Godsk   ORCID: orcid.org/0000-0002-8332-2712 1 &
• Karen Louise Møller   ORCID: orcid.org/0000-0002-0539-1763 1  

528 Accesses

Explore all metrics

There is a widespread agenda of improving teaching and learning in higher education by engaging students with educational technology. Based on a large-scale literature review, the article presents 61 specific, research-based recommendations for realising the engagement potential of eight types of educational technologies in higher education. These recommendations can be used, for example, by educators to incorporate specific, available educational technologies into their teaching or as an educational development method to enhance particular forms of student engagement. Based on the evidence, the article points out that some educational technologies have a more documented and sometimes also broader potential to engage the students behaviourally, affectively, and/or cognitively than others and that this likely is related to the extent the technology supports structure, active learning, communication, interaction, and activities on the higher levels on the learning taxonomies.

Similar content being viewed by others

A Comprehensive Review of Educational Technology on Objective Learning Outcomes in Academic Contexts

Mapping research in student engagement and educational technology in higher education: a systematic evidence map

Pedagogical Considerations for Technology-Enhanced Learning

Explore related subjects.

• Artificial Intelligence
• Digital Education and Educational Technology

Avoid common mistakes on your manuscript.

1 Introduction

The use of digital educational technology is not a new phenomenon in higher education and gained traction in the early ‘70s in the form of telecourses and the ‘80s in the form of computer-assisted learning and online learning (Garrison, 1985 ). In recent years, technology has received significant attention as a means to support distance education during the COVID-19 pandemic (Abu Talib et al., 2021 ) and as a disruptor of traditional teaching, learning, and assessment forms with the advent of generative artificial intelligence (GenAI) tools such as ChatGPT, Google Gemini, and Dall-E (Farrelly & Baker, 2023 ; Godsk & Elving, 2024 ). Studies show that educational technology has the potential for improving learning outcomes, motivation, engagement, and pass rates (Garrison & Kanuka, 2004 ; Price & Kirkwood, 2011 ; Schindler et al., 2017 ), as well as the business potential for reducing costs, increasing intakes, and increasing student retention (Daniel et al., 2009 ). In higher education in Europe and English-speaking countries, student engagement is often linked to the students’ experience, satisfaction, and learning outcomes, which is why there is a widespread desire to benefit from the technology’s potential to engage students (Payne, 2019 ; Schindler et al., 2017 ). Despite the evidence and interest, universities are struggling to make effective and systematic use of technology to support student engagement (Henrie et al., 2015b ). This may be due to limited systematic evidence on how to engage students with specific educational technologies in terms of practical, concrete recommendations or guidelines, which can be directly applied by educators in their lesson planning or connection with Learning Design processes (Henrie et al., 2015b ; Schindler et al., 2017 ).

1.1 The concept of student engagement and educational technology

The concept of “student engagement” has significantly evolved and expanded within educational research and higher education. Unlike traditional views that mainly focus on observable behaviours and indicators of involvement in educational activities, such as attendance and participation, recent studies adopt broader conceptualisations, analysing how students behave, feel, and think in the context of teaching and learning (Bond et al., 2020 ; Fredricks et al., 2004 ; Henrie et al., 2015a ). This includes aspects like the general student experience and the resultant institutional reputation (Trowler, 2010 ; Wimpenny & Savin-Baden, 2013 ), viewing student engagement as an interconnected and psychosocial process influenced by personal and contextual factors (Kahu, 2013 ), “force-fields” (i.e., driving/resisting forces) for and against intrinsic and extrinsic motivation (Payne, 2019 ), or as described through three dimensions of engagement: behavioural, affective/emotional, and cognitive (Fredricks et al., 2004 ; Newmann et al., 1992 ). These dimensions are sometimes supplemented by additional dimensions such as “the will to succeed” (Kahu, 2013 ), social-behavioural engagement in the context of group work (Linnenbrink-Garcia et al., 2011 ), and student agency (Reeve & Tseng, 2011 ), which other researchers consider unnecessary, as they believe the three existing dimensions already adequately capture these aspects of student engagement (Kahu, 2013 ). These varied conceptualisations also reflect a broader debate between narrow, “mainstream”, and broad, holistic views of student engagement. The narrow view often restricts engagement to specific, measurable behaviours within classroom settings related to an effective learning process (Henrie et al., 2015b ; Zepke, 2015 ), whereas the more broad and holistic view considers engagement as encompassing a wide range of student activities, interactions, and emotions both within and beyond academic environments that contribute to a richer learning experience (Bond et al., 2020 ; Fredricks et al., 2004 ; Henrie et al., 2015b ; Zepke, 2015 ).

In addition, educational technology can involve students in teaching activities that were previously inconceivable (e.g., in virtual reality, simulations, online self-test quizzes, and GenAI-based formative feedback) (Kirkwood & Price, 2014 ; Puentedura, 2010 ; Godsk & Elving, 2024 ) and engagement can be expressed in ways and with indicators that could not previously be observed without technology (Bond & Bedenlier, 2019 ; Fredricks et al., 2004 ). This underscores the importance of not limiting focus to readily observable indicators of student engagement or confining the understanding to just one indicator, as both approaches risk overly simplifying the potential for student engagement. Such narrow focus may also overlook other forms of engagement that are indirectly related or cannot be observed without technology.

In other words, both the general desire to improve students’ learning experiences by engaging them with educational technology and the potential of the technology to engage in numerous ways that are not necessarily observable but interconnected (Payne, 2019 ) advocate for a need to adopt a broad conceptualisation of student engagement. One of the broad and widely used conceptualisations is based on Fredricks et al. ( 2004 ) and Newmann et al.’s ( 1992 ) three perspectives on student engagement and defined by Bond et al. ( 2020 ) in the context of higher education as: “The energy and effort that students employ within their learning community, observable via any number of behavioural, cognitive or affective indicators across a continuum.” (Bond et al., 2020 , p. 2). This conceptualisation extends the narrow behavioural perspective by adding affective and cognitive dimensions, including how students feel and think about their learning experiences, which may significantly affect their engagement (Fredricks et al., 2004 ). “Behavioural engagement” is typically indicated by participation, interaction, involvement, achievement, confidence, and study habits; “affective engagement” or “emotional engagement” is often indicated by positive interaction, enjoyment, attitude, motivation, and enthusiasm; and “cognitive engagement” is typically indicated by peer learning, deep learning, self-regulated learning, positive self-perception, and critical thinking (Bond et al., 2020 ; Fredricks et al., 2004 ). This breadth of Fredricks et al. ( 2004 )’s conceptualisation of student engagement, along with Bond et al. ( 2020 )’s extensive and thorough list of indicators based on a large-scale review related to the three dimensions of engagement, therefore provides a coherent and practical framework for mapping studies of educational technologies and their use to actual engagement types, including the broader, holistic views of student engagement. Although no direct relationship between introducing specific educational technologies and student engagement in higher education has been established (Schindler et al., 2017 ; see also Pickering & Swinnerton, 2019 ), studies show that technology in education does influence student engagement and that more research is needed to understand the potential of specific educational technologies and how to benefit from them (Bond & Bedenlier, 2019 ; Clark, 1994 ; Lillejord et al., 2018 ) and thus ultimately meet the widespread desire to promote student engagement with educational technology. This leads to the following research question:

How to engage students with educational technology in higher education?

A systematic literature review guided by the PRISMA process and utilising the inclusion and exclusion criteria in Table  1 was conducted to answer this question. The PRISMA process involved four steps: (1) searching for, screening and identifying relevant studies based on abstracts; (2) screening of and excluding studies that were not relevant based on full-text; (3) assessment of eligibility based on full-text; and (4) selection (‘inclusion’), coding, and analysis of the relevant studies in the final synthesis (see 6. for details). The analysis was based on a deductive and inductive coding of the studies according to educational technology, subject area, educational level, modality, type of student engagement, research method, and aim (Khan et al., 2003 ; Littell et al., 2008 ; Moher et al., 2009 ); and supplemented with follow-up searches (“Round 2”) on the identified types of educational technologies in step 4 (see details in 6. and Godsk et al, 2021 ). Fredricks et al.’s ( 2004 ) conceptualisation of student engagement as comprising three perspectives — behavioural, affective (emotional), and cognitive engagement — as well as Bond et al. ( 2020 )’s identification of 55 specific indicators related to these dimensions, served as the basis for the coding of the engagement type (see Bond et al., 2020 , Additional file 2). In Round 1, the searches were limited to empirical studies from OECD countries from 2013 onwards for maximum comparability of the educational contexts regarding teaching tradition, educational regulations, including GDPR, and the available technologies. In Round 2, there were no exclusion criteria related to country or resource type as long as the resource was scientifically robust and directly or indirectly based on empirical data. However, only resources that included firsthand empirical data were included as the basis of the synthesis and recommendations, while, for example, systematic reviews and reports were used for perspective and discussion.

In the first round, 2,154 articles were screened, and 112 empirical studies were included in the synthesis. The 112 studies document a positive or negative engagement potential of educational technology related to eight major clusters of educational technologies, hereafter referred to as “types”: (1) learning management systems, (2) discussion forums and weblogs, (3) audience response systems and tablets, (4) online quizzes, (5) social media, (6) video and audio, (7) games and gamification, and (8) virtual reality and simulation. In addition, only eight eligible studies addressed diverse technologies that did not fall within these eight types of technologies (i.e., digital curation tools, e-portfolios, peer feedback tools, haptic devices (except virtual and augmented reality), digital magazines, open badges, word clouds, and diverse or non-specified mobile technologies), thereby constituting an insufficient basis to conclude on their engagement potential and thus excluded from the article. In the second round, the eight identified types of technology were used to search more specifically for the engagement potential of each respective technology. This resulted in screening 618 new articles, of which 60 ended up being added, bringing the total number of studies and other publications included in the article to 196 (see Table  2 and Appendix  for details). The second round of searches validated and expanded the already identified recommendations, but only eight new recommendations were identified, suggesting that the list was already saturated.

The coding revealed that a wide range of subject areas were represented, including the social sciences, comprising psychology and business; natural and technical sciences; humanities; and health sciences, as well as a representation of first-year, other undergraduate, and postgraduate teaching. The coding also revealed that most included studies were based on qualitative case studies or quantitative quasi-experimental research methods involving pre- and post-studies or a control group receiving conventional teaching, analysing differences in students’ test results, activity level, perceived engagement, or attitude. However, despite the wide representation of subject areas and levels and the thorough research, it is difficult to generalise findings from these kinds of studies from various contexts. Thus, the findings and recommendations in this article build on the heterogeneity principle (Patton, 2015 ) that any common finding that emerges from a great variation suggests a potentially more general pattern and forms the basis for the recommendations for each technology collected in Table  2 .

The included studies show that educational technology can engage students in higher education behaviourally, affectively, and cognitively. However, the studies also show that this potential depends on the context, how the technology is pedagogically and didactically integrated into teaching practice, and that the potential type of engagement varies between the specific educational technologies (Vercellotti, 2018 ). The findings for actualising the engagement potential of the eight types of educational technologies are further unfolded in the following sections and Table  2 .

3.1 Learning Management Systems

Learning Management Systems (LMS) is a collective term for web-based learning platforms for developing, distributing, delivering, and administrating educational materials and activities via the Internet (Weller, 2007 ). 99% of higher education institutions have at least one platform available, of which Canvas, Blackboard, Brightspace, and Moodle are currently the most widespread (Dahlstrom & Bichsel, 2014 ). Clark et al. ( 2016 ) demonstrate that an LMS can lead to increased engagement, better student-educator interaction, and improved learning when used to structure flipped classrooms with online video lessons supplemented by face-to-face activities. Zanjani et al. ( 2017 ) also note that engagement is generally strengthened by simple structure and navigation and a manageable number of links and tools that students can customise according to their needs and preferences. Furthermore, Karaksha et al. ( 2013 ) highlight that it is relevant to remind students of the available digital tools to increase their use and engagement potential. Vercellotti ( 2018 ) compares students’ learning outcomes in online and face-to-face teaching and finds that how the technology is utilised to support an active learning pedagogy plays a crucial role, while Osman ( 2022 ) finds that combining synchronous and asynchronous activities in the LMS enhances students’ interaction and engagement and ultimately their satisfaction. Orcutt and Dringus ( 2017 ) highlight how educators’ online presence and passion for teaching influence the students’ intellectual curiosity. Wdowik ( 2014 ) highlights the opportunities to support more interaction and collaboration between educator and students, as well as among students, using the video conferencing tool in the LMS.

Another potential of LMSs is linked to their tools for tracking students’ activities, progress, and submissions (Veluvali & Surisetti, 2022 ). Lawrence et al. ( 2019 ) point out how learning analytics can promote desired study behaviour and increase behavioural engagement by identifying and assisting students at a low academic level or close to dropping out through reminders, links to resources, or other support for task completion. The study also emphasises the need to explicitly communicate expectations for online students and prepare them for online activities (Pepple, 2022 ). The tools to monitor students’ progression also influence their retention through continuous summative assessment and peer feedback, and students can monitor their learning. This can be done, for example, through the educator’s feedback on activities and tasks submitted on the e-learning platform (Holmes, 2018 ) or via peer assessment activities, where students anonymously assess each other’s activities and assignments (Mirmotahari et al., 2019 ; Sullivan & Watson, 2015 ).

3.2 Discussion forums and weblogs

Discussion forums and weblogs are typically used for asynchronous activities in which students and the educator discuss and develop ideas related to the course content and form using threaded discussions, text, and possibly multimedia independently of time and place. Most LMSs have a built-in discussion forum that the educator typically manages, whereas weblogs are often managed by the students individually. Research on this technology, in general, focuses primarily on how the technology can be used to train writing, critical thinking, reflection, and argumentation, social constructivist online teaching and peer learning, “scaffolding” (Arend, 2009 ; Dalsgaard & Paulsen, 2009 ; MacKnight, 2000 ; Salmon, 2000 ; Szabo & Schwartz, 2011 ), and how students can be activated in their learning processes (Balaji & Chakrabarti, 2010 ; Dennen, 2005 ). The included studies show that it is essential that the educator outlines the code of conduct as well as provides short, precise instructions. Additionally, open questions at an appropriate academic level that can encourage all students to participate and discussions where students can apply existing experiences or relate them to their lives can be stimulating. Likewise, the peer aspect of online discussions can contribute to developing students’ professional identity and sense of belonging, thereby increasing their participation (Willis et al, 2013 ). In addition, audiovisual media can make discussions more authentic for the students (Douglas et al., 2020 ; Harvey et al., 2018 ; Kebble, 2017 ; Page et al., 2020 ). Stimulating questions can, for example, be formulated based on Bloom’s taxonomy (Badenhorst & Mather, 2014 ; Shaw & Irwin, 2017 ), and students’ participation can be strengthened by providing exemplars of “quality discussions” (Kebble, 2017 ). It is also effective to let the discussion be based on questions and topics that are engaging for students, such as relevant cases and real situations, and that invite students to share different opinions and develop personal perspectives through reflection questions (Buelow et al., 2018 ; Fukuzawa & Boyd, 2016 ). Another important factor is the educator’s visible and active participation in the discussion forum, which can consist of relevant contributions related to the issues the students are discussing (Collins et al., 2019 ; Mokoena, 2013 ; Mooney et al., 2014 ) or guide and point out relevant teaching materials that students can work with (Fukuzawa & Boyd, 2016 ). It also has a positive effect on engagement if students are assigned roles that frame their active participation in the discussion (Mooney et al., 2014 ; Truhlar et al., 2018 ), there is a requirement to use a specific argumentation model (Oh & Kim, 2016 ), or the students’ participation is assessed according to well-defined criteria (Kebble, 2017 ; Wyatt, 2021 ). Truhlar et al. ( 2018 ) highlight that activities in which students summarise discussions stimulate higher-order thinking. Discussions with many participants and repetitive and extensive posts are experienced as frustrating, so large groups should consider this (Fukuzawa & Boyd, 2016 ; Kebble, 2017 ). Concerning weblogs in formal settings, Sharma and Tietjen ( 2016 ) demonstrate a similar effect on education, indicating that the technology is viable for supporting both students’ collaboration and meaning-making.

3.3 Audience response systems

Audience response systems and devices (ARS) are a collective term for a range of software and hardware-based technologies that allow students to participate in activities such as polls or ask questions and provide answers interactively during lectures using their computer, tablet, mobile phone, or a so-called clicker. The majority of studies find that activities involving audience response systems enhance student engagement (Çakir, 2020 ; Fischer et al., 2015 ; Funnell, 2017 ; Habel & Stubbs, 2014 ; Han & Finkelstein, 2013 ; Jozwiak, 2015 ; Kay & LeSage, 2009 ; Remón et al., 2017 ; Sawang et al., 2017 ; Shaw et al., 2015 ; Sun et al., 2014 ), and a comprehensive literature review from 2009 highlights the technology’s potential to particularly increase behavioural and cognitive engagement (Kay & LeSage, 2009 ). Shaw et al. ( 2015 ) and Lim’s ( 2017 ) studies demonstrate that digital polls with questions and answers foster a sense of cohesion between the educator and students, which is not typically experienced in large classes. The technology also provides educators with insights into students’ learning outcomes for continuous feedback and addressing their questions (McKenzie & Ziemann, 2020 ; Remón et al., 2017 ; Robson & Basse, 2018 ; Yilmaz, 2017 ) and allows students to pause the classroom if they needed more time (Dong et al., 2017 ). Polls should ideally be academically challenging (Sawang et al., 2017 ), preferably combined with group activities (Jozwiak, 2015 ) or plenary discussions in the class (Robson & Basse, 2018 ; Sawang et al., 2017 ), and ideally allow students to respond anonymously (Heaslip et al., 2014 : Remón et al., 2017 ). Notably, the opportunity to discuss the reasoning behind poll responses is crucial (Habel & Stubbs, 2014 ; Steadman, 2015 ; see also “Peer Instruction,” Crouch & Mazur, 2001 , and Thomas et al., 2017 ). It can also enhance engagement if students formulate questions themselves (Song et al., 2017 ) or if the question is open-ended, controversial, or requires ethical consideration or higher-order thinking (Campbell & Monk, 2015 ; Steadman, 2015 ; Wood & Shirazi, 2020 ). Finally, the technology can support students’ mutual dialogue through a “backchannel,” where students can discuss ongoing teaching, leading to higher student satisfaction, higher grades, and more frequent use of class content (Neustifter et al., 2016 ).

3.4 Online quizzes

In online quizzes, students can answer questions related to the subject matter. Online quizzes differ from audience response stems by being fully online and, typically, asynchronous so that they can be used and reused regardless of time and place. The activities contribute to students’ understanding and deep learning and consolidate what has been learned (Argyriou et al., 2022 ; Browne, 2019 ; Russell et al., 2016 ). Students appreciate the flexible access, the options to revisit the quizzes, and the ability to do the quizzes at their own pace (Browne, 2019 ). When quizzes are used regularly for providing feedback, it promotes students’ engagement (Browne, 2019 ; Holmes, 2015 ; Lee & Harris, 2018 ; McKenzie et al., 2013 ) and is an effective mechanism for incentivising student completion of preparatory work (Cann, 2016 ; Cook & Babon, 2017 ; Cossu et al., 2022 ). It is important to use various quiz question types (Browne, 2019 ) and provide the students with specific feedback so that they can monitor and self-regulate their studying and progression (Evans et al., 2021 ; Thomas et al., 2017 ). Combining quizzes with group activities promotes students’ engagement and learning outcomes (Balta & Awedh, 2017 ) and supports collaborative learning.

3.5 Social media

Social media is a collective term for web-based social networks where users can socialise, communicate, and share files and other information. Social media is typically not an institutionalised learning technology but often plays a role in students’ social interaction and their informal digital learning environment (frequently referred to as “personal learning environment,” PLE, see also Caviglia et al., 2018 ) or as part of the curriculum (see Delello et al., 2015 ; Megele, 2015 ). Overall, studies indicate that increased interaction and collaboration opportunities offered by the social media in terms of their flexibility and the ability to incorporate external resources contribute to enhanced motivation and interest in teaching (Camus et al., 2016 ; Cooper & Naatus, 2014 ; Chugh & Ruhi, 2018 ; Delello et al., 2015 ; Evans, 2014 ; Glowatz & Bofin, 2014 ; Graham, 2014 ; Gregory et al., 2016 ; Kent, 2013 ; Northey et al., 2015 ; Scott & Stanway, 2015 ; Sharma & Tietjen, 2016 ). Students prefer Facebook and Twitter (now “X”) over discussion forums in LMSs, as they are perceived as more accessible than the LMSs’ discussion forums (Kent, 2013 ) and are more familiar (Clements, 2015 ). However, other studies suggest that familiarity with Facebook does not guarantee its use for study purposes (Dyson et al., 2015 ; Gregory et al., 2016 ). Similarly, Cooke ( 2017 ) points out a risk that students may lose interest in the specific social media and, as a result, its value as a supplementary tool for supporting discussions if the platform is their primary learning platform and its use is mandatory (Cooke, 2017 ). Both Camus et al. ( 2016 ) and Kent ( 2013 ) note that the use of Facebook resulted in more dialogue compared to the institutionalised LMS, and Evans ( 2014 ), Tiernan ( 2014 ), and Pallas et al. ( 2019 ) find that social media can also contribute to increasing student collaboration, creating an inclusive atmosphere that increases the participation of “quiet” students and supporting deep learning (Megele, 2015 ). However, if assessment is involved, it is important to be explicit about expectations and criteria (O’Brien & Freund, 2018 ). Similarly, Barber et al. ( 2015 ) show that a “Digital Moments” course helped create meaningful online learning communities among the students. Kent ( 2013 ) also points to a different perception and use of social media and LMS. LMS is associated with formal learning, while social media is more often used for practical questions and informal collaboration. Several studies describe different ways Twitter has been used: as a channel for questions to the instructor during class (Kunka, 2020 ; Tiernan, 2014 ; Prestridge, 2014 ), as a discussion forum between students and possibly external participants (Bender, 2021 ; Dragseth, 2020 ; Megele, 2015 ), and as a channel for students to share academic examples (Prestridge, 2014 ). Diug et al. ( 2016 ) demonstrate that Twitter gave students a sense of increased access to their educators while supporting their collaboration.

3.6 Video, audio, and multimedia

Video, audio, and multimedia are used here as a broad term for synchronous and asynchronous, audiovisual and digital multimedia, such as video presentations of course content and feedback on assignments, video recordings from field trips, and video assignments, produced by both the educator, students, or external providers. Video can be used, for example, to “flip” the teaching, allowing students to watch video lectures at home, creating more time for in-class dialogue (Noetel et al., 2021 ; Willis et al., 2018 ), appealing to multiple sensory channels simultaneously (Mayer, 2008 ), and supporting more authentic communication compared to written communication (Henderson & Phillips, 2015 ; McCarthy, 2015 ; Noetel et al., 2021 ; Oh & Kim, 2016 ). Activities where students produce audio can enhance their engagement, provided they have the equipment and skills to create them (Bolliger & Armier, 2013 ). In addition, student-produced audio materials can have a socialising effect on teaching due to their authenticity and personal touch, offering variation compared to traditional written assignments (Barber et al., 2015 ; Bolliger & Armier, 2013 ). Similarly, audio and video feedback from the educator is perceived as more personal and information-rich than written feedback (Cavaleri et al., 2019 ; Pearson, 2018 ; Rasi & Vuojärvi, 2018 ; Seery, 2015 ; Zhan, 2023 ) as well as video conferences can make the educator more visible and “accessible” than in face-to-face teaching (Gleason & Greenhow, 2017 ; Ng, 2018 ; Wdowik, 2014 ), thus creating a closer connection and being perceived as more personal (Steele et al., 2018 ). Educator feedback on video is often revisited and used in later assignments (Speicher & Stollhans, 2015 ). Several studies document a generally positive attitude towards video lectures and instructions among students, providing greater flexibility and allowing more independence in the learning process compared to face-to-face teaching (O’Callaghan et al., 2017 ; Gnaur & Hüttel, 2014 ; Lin et al., 2017 ; Lupinski & Kaufman, 2023 ; Scagnoli et al., 2019 ; Seery, 2015 ; Speicher & Stollhans, 2015 ). Scagnoli et al. ( 2019 ) conclude that the more video lectures students watch, the more positively they perceive the medium. However, they also emphasise the importance of familiarity with and experience using video for learning purposes, students’ academic level (postgraduate students are more positive than undergraduates), and how well the video lectures are integrated into the course. In addition, Brame ( 2016 ) stresses the importance of minimising students’ cognitive load when watching the videos — a parallel theme to research on “attention span,” which ambiguously indicates various durations students can maintain concentration depending on the context, teaching format, subject matter, and the students’ characteristics (Bradbury, 2016 ; Hartley & Davies, 1978 ). However, there are also studies highlighting the risk of a more superficial learning approach (Francescucci & Rohani, 2019 ; Trenholm et al., 2019 ), lower learning outcomes (Roberts, 2015 ), lower attendance in class (O’Callaghan et al., 2017 ), and lower engagement with video lectures where in particular the low-performing students are at risk (Murphy & Stewart, 2015 ). Lin et al. ( 2017 ) point out that students found concrete, instructional videos for laboratory work more useful and essential for their learning than video lectures of a generally more conceptual nature. However, the longer the videos are, the fewer students will watch them to the end (Lin et al., 2017 ). Video combined with other activities such as quizzes, small assignments, group work, or individual feedback positively impacts student engagement (Brame, 2016 ; Gnaur & Hüttel, 2014 ; Jozwiak, 2015 ; Paiva et al., 2017 ). In addition, student-produced video and audio for learning and assessment purposes may also positively impact students’ learning experience and contribute to the development of their communication, knowledge construction, and teamwork skills (Arsenis et al., 2022 ; Mathany & Dodd, 2018 ; Morena et al., 2019 ), for example, in the form of digital storytelling, which can also contribute to developing social and cultural competencies (Grant & Bolin, 2016 ; Ribiero, 2016 ; Yousuf & Conlan, 2018 ).

3.7 Games and gamification

Games and gamification as educational technology involve activities with various forms of game elements such as leaderboards, points, badges, or other forms of rewards or competition. The technology distinguishes itself from online quizzes by extensively using entertainment and possible competitive elements to motivate students’ participation and learning (Educause Learning Initiative, 2011 ). Subhash and Cudney ( 2018 ) find in their review that the elements mentioned above increase, in particular, the students’ attitude, level of participation, motivation, and performance. However, several studies also highlight the importance of authenticity and its relation to reality. Edmonds and Smith ( 2017 ) find that mobile learning games can engage students if they involve interactive investigations of phenomena with fellow students and involve them as designers of similar games. Similarly, Buckley and Doyle ( 2016 ) find that involving games with real-world dilemmas and decisions increases student engagement. However, it is important to note that students who are already gamers are more positive towards games in education than other students (Davis et al., 2018 ). Bawa ( 2019 ), Plump and LaRosa ( 2017 ), and Holbrey ( 2020 ) find that the game-inspired polling tool Kahoot can increase student engagement and participation in education if used for students to play together in groups against other groups, collaboratively create quizzes for other groups based on the curriculum, and this subsequently forms the basis for discussion among the students. Viswanathan and Radhakrishnan ( 2018 ) document in this context that students find it engaging to be co-developers of a game and that it supports their critical thinking. The combination of games and collaboration is also highlighted by Christopoulos et al. ( 2018 ), who, in their study, emphasise the importance of both the interaction among students and the function of the game. For example, individual games that test students’ knowledge will only be engaging for a few students (Christopoulos et al., 2018 ).

3.8 Virtual reality and simulation

Virtual reality (VR) and simulation are computer-generated simulations of an environment where educators and students can interact via a computer or, for example, through a dedicated headset (Makransky & Petersen, 2019 ). Studies indicate a general increase in engagement, especially due to the sense of presence (Cavanaugh et al., 2023 ; Chulkov & Wang, 2020 ; Papanastasiou et al., 2019 ; Rafiq et al., 2022 ), the simulated first-hand experiences that would have been impossible in the real world (Di Natale et al., 2020 ) for instance, interacting with three-dimensional virtual molecular phenomena (Elford et al., 2021 ), doing virtual field trips in Google Earth (McDaniel, 2022 ), use virtual microscopes for manipulation of online images (Herodotou et al., 2020 ) and provide variation for the students in the learning process (Hayes et al., 2021 ). However, opinions on the technology may be divided, and reservations among students often stem from a lack of experience and comfort in participating and interacting in VR (Francescucci & Foster, 2013 ). Francescucci and Foster ( 2013 ) and Makransky and Lilleholt ( 2018 ) find it essential to ensure that students have a high level of autonomy through a sense of control and active learning when using the technology, while others find it important that educators have the qualifications to use VR for learning purposes, give time for students to get familiar with the technology and have access to support in initial phases (Nesenbergs et al., 2020 ; Pellas et al., 2021 ). Luo et al. ( 2021 ) find that activities in VR can benefit from being combined with non-VR activities, including group or educator debriefings related to the VR activities. Pellas and Kazanidis ( 2015 ) found significantly positive learning outcomes and engagement results for teaching conducted solely in Second Life, compared to combined Second Life and face-to-face teaching. Matthew & Butler’s ( 2017 ) study showed that video from Second Life was suitable for simulating authentic problems, positively influencing students’ engagement and learning outcomes. Similarly, Sobocan and Klemenc-Ketis ( 2017 ) document that virtual patients in teaching for diagnosis and medical practice are perceived as beneficial due to the increased opportunities for skill training. Likewise, a positive effect on student engagement is demonstrated in simulations. Pallas et al. ( 2019 ) identify how simulations can increase students’ online interaction and reflection, including involving otherwise quiet students. Irby et al. ( 2018 ) and Marques et al. ( 2014 ) point out that virtual laboratories can be just as engaging as working in a physical laboratory and, in some situations, primarily introductory modules, completely replace face-to-face laboratory work.

4 Discussion

Overall, the included studies document the potential of educational technology to engage students in higher education behaviourally, affectively, and cognitively, which is dependent on the context, integration, and the specific educational technology as well as the specific technology’s support for structure, active learning, communication, and interaction between students and/or educators (Fig.  1 , further developed from Schindler et al., 2017 ). Furthermore, the synthesis indicates that each of the eight technologies has the potential to support all three forms of engagement, of which some are more well-documented than others and that they are interconnected.

Overview of the potential of educational technology for student engagement

Across 64 studies, the impact on students’ behavioural engagement is documented, particularly in the context of LMSs, discussion forums, audience response systems, online quizzes, social media, video and audio, and virtual reality and simulations. The studies document that technologies suitable for conveying curriculum content, creating structure, providing assessment tasks, and facilitating interaction and active learning effectively support students’ behavioural engagement. The interaction between students and content, educators, and peers is crucial for behavioural engagement (McCallum et al., 2015 ) as well as a course organisation with clear learning goals, logical course structures, recurring activities, and regular interactions with peers and educators contribute to behavioural engagement, satisfaction, and learning (Gray & DiLoreto, 2016 ; Gross et al., 2015 ; Porcaro et al., 2016 ; Ravenscroft & Luhanga, 2018 ; Ravishankar et al., 2018 ). Thus, this also shows how structure influences students’ affective engagement. Muir et al. ( 2019 ) highlight the importance of assessment tasks, workload, work-life balance, assignment quality, and educator presence. While online activities can enhance retention and engagement (Callahan, 2016 ), Dumford & Miller, ( 2018 ) note a link between students’ preferences and experience with online learning. Studies emphasise the flexibility of access to online teaching materials, with video lectures freeing up time for more engaging in-class activities (Steen-Utheim & Foldnes, 2018 ).

The impact of technology on students’ affective engagement is highly linked to how it influences the communication and interaction between students and educators, as documented in 59 studies. Communication tools within the LMS, discussion forums for peer learning, social media, competitive game elements, VR and simulations, and other audiovisual media can play a key role in this context. In general, technologies facilitating multi-faceted communication and interaction and educator involvement are often effective for affective engagement (Vayre & Vonthron, 2017 ). Educator presence, social support, figurative language, and effective facilitation are pivotal factors in online settings (Dixson et al., 2017 ; O’Shea et al., 2015 ; Orcutt & Dringus, 2017 ; Yates et al., 2014 ). Nevertheless, students’ low technological skills can negatively impact their affective engagement (Butz et al., 2016 ; Vayre & Vonthron, 2017 ), and some students may prefer using technologies they are already familiar with (del Barrio-Garcia et al., 2015 ). While students generally have experience with and a positive attitude towards technology in education, they may lack the skills to use technology in their academic work (Kim et al., 2019 ). Technology and online teaching can also hinder students’ involvement in the informal, implicit aspects of academic work (Selwyn, 2016 ).

The cognitive engagement is documented in 46 studies and notably supported by technologies such as audio and video, virtual reality and simulations, and audience response systems used to facilitate active and flexible student involvement in high taxonomic learning activities, such as collaboration, problem-solving, reflection, authentic exploration, and hypothesis testing. Flexible technology access supports self-directed learning, motivating students to engage actively (Mello, 2016 ; Mihret et al., 2017 ). McGuinness and Fulton ( 2019 ) illustrate the value of online tutorials as a flexible supplement to in-class teaching, aiding students in self-paced learning. Mihret et al.’s ( 2017 ) case-based teaching, combined with online discussions and ongoing e-portfolio assessment, enhances self-directed learning compared to face-to-face participation. However, high flexibility may negatively impact affective engagement due to the self-discipline required (McCallum et al., 2015 ). The technology may also support adaptive learning involving diagnostic quizzes, individual materials, formative tests, lectures, and summative tests that enhance satisfaction, performance, and cognitive engagement, as McKenzie et al. ( 2013 ) and Pourdana ( 2022 ) demonstrated. Technology supporting pedagogical strategies, like Baum’s ( 2013 ) guided inquiry, blends short video lectures and self-organised problem-solving, proving less confusing than traditional teaching. Gibbings et al. ( 2015 ) highlight the role of technology in providing authentic online activities and fostering communication, collaboration, and personal development despite geographical distances. Activities that challenge students’ understanding of societal issues, entertaining elements, and connections to past experiences also enhance cognitive and affective engagement (Buelow et al., 2018 ; O’Shea et al., 2015 ).

4.1 Breadth and Interconnectedness

When looking across the three types of engagement, there is no clear pattern in which technologies that engage students in more than one way. However, as also stressed by Payne ( 2019 ) and Fredricks et al. ( 2004 ), engagement is often interconnected, and indicators can be ambiguous. This interconnectedness is notably evident from the 25 included studies on specific technologies and background studies that document the technology’s potential to engage students in multiple ways simultaneously as well as from the studies that investigate the impact of technology-enhanced learning designs in education (e.g., Gray & DiLoreto, 2016 ; Gross et al., 2015 ; Porcaro et al., 2016 ; Ravenscroft & Luhanga, 2018 ; Ravishankar et al., 2018 ). Audience response systems and video, audio, and multimedia appeared most frequently in the studies of specific technologies, with seven and five studies, respectively, and three studies demonstrated a potential to support all three types of engagement simultaneously (Chulkov & Wang, 2020 , on VR, and Christopoulos et al., 2018 , on games and gamification; and Neustifter et al., 2016 , on audience response systems). The varying documented breadth may be due to a narrow focus of the individual studies, but it may also suggest a diverse potential to support student engagement more broadly. Furthermore, it may indicate that it often does not make sense to talk about a specific type of engagement potential as they are often interconnected and/or prerequisites for each other, just as important indicators can be overlooked. For example, Bond et al. ( 2020 ) categorise “confidence” as a (direct) indicator of affective engagement as well as an (indirect) indicator of behavioural engagement. The rationale is that the students’ confidence with the technology is manifested in their constructive behaviour. Likewise, cognitive engagement can manifest as self-regulated behaviour or simple memorisation (Fredricks et al., 2004 ).

Overall, the conceptual framework of student engagement by Fredricks et al. ( 2004 ) and the indicators provided by Bond et al. ( 2020 ) are useful for capturing a broad spectrum of the concept. This includes both observable behaviours, traditionally associated with narrow understandings of student engagement and the broader understandings, where student engagement is linked to experience, satisfaction, learning outcomes, and various affective and cognitive factors. This broader conceptualisation also addresses the additional dimensions proposed by Kahu ( 2013 ), Linnenbrink-Garcia et al. ( 2011 ), and Reeve and Tseng ( 2011 ). Furthermore, these frameworks accommodate indicators that may be overlooked without technology. For example, the use of technology allows for the observation of student engagement in online peer feedback activities (Mirmotahari et al., 2019 ) and supports self-regulated behaviours through online quizzes that enable students to monitor their progress and receive automated feedback (Evans et al., 2021 ; McKenzie et al., 2013 ; Thomas et al., 2017 ). However, the results suggest that one should place little importance on the actual classification but rather consider whether a given indicator may point to multiple types of engagement and be connected to other indicators.

4.2 How to Engage Students with Educational Technology in Higher Education?

The answer to the research question depends on the type of engagement one wishes to support, available technologies, and the specific context and educator competencies. For instance, to increase students’ behavioural engagement, educators may utilise technologies that provide structure and support active content delivery, such as LMSs, ARSs, and online quizzes, and follow the provided recommendations (Table  2 ). Those aiming to increase students’ affective engagement can benefit from technologies supporting student interaction, like discussion forums, social media, and games. Educators wanting to support students’ cognitive engagement can use simulations to aid students in authentic exploration of a given topic, have the students produce their own video, or facilitate structured online discussions. If there is a need to engage students behaviourally, affectively, and/or cognitively at the same time, it is relevant to consider technologies with a documented, broad engagement potential. However, if the educational technology is already provided, the recommendations provided (Table  2 ) can increase the chances of engaging students with the respective technology.

4.3 Limitations

The study in this article has revealed limitations related to the concept of student engagement and an inherent limitation associated with the research methods of the available studies.

The term “engagement” is ambiguous in English and may refer to attending something in a broad sense (Payne, 2019 ) or, in a narrow sense, referring to student behaviour in class (Zepke, 2015 ). Conversely, studies may deal with student engagement without necessarily using the term. A similar limitation is seen in the naming of educational technologies, which are often referred to by the name of the software or hardware and not necessarily by the type of technology, which is why it is easy to overlook relevant studies with a traditional protocol-driven search strategy based on keywords.

In addition, the study confirmed that engagement can be interconnected and indicators can be ambiguous. This is not a problem per se in realising the technology’s engagement potential but rather a problem in analysing studies that investigate and document a narrow engagement potential. Thus, further validation and mapping of the interconnectedness of Bond et al. ( 2020 )'s indicators would be useful.

Finally, there is a limitation that relates to the nature of the available studies, which are often characterised by qualitative case and quasi-experimental studies and other research methodologies in which it is difficult to distinguish the cause of the effect from other factors such as the novelty effect (McKechnie, 2008 ), the redesign of teaching that the introduction of technology entails (Kirkwood & Price, 2014 ), and the context (Schindler et al., 2017 ) and thus also to generalise findings. This calls for more research on the significance of the teaching context, including course design, course delivery, and other contextual factors.

5 Conclusion and implications

The article has identified the potential of educational technology to support students’ behavioural, affective, and cognitive engagement, along with a series of specific recommendations on how to realise this potential. These recommendations can be used, for example, by educators to incorporate specific, available educational technologies into their teaching or as an educational development method to enhance particular forms of student engagement. Educators and educational developers can use these recommendations to qualify the use of educational technology for student engagement in higher education. While the studies highlight various engagement potentials of educational technology, the synthesis also revealed that whether this potential is realised is dependent on the context, integration, the specific technology, and the educator’s competencies in teaching with technology (see also Orcutt & Dringus, 2017 , and Schindler et al., 2017 ). Furthermore, the synthesis also shows that all included technologies can support all three kinds of engagement, that engagement is often interconnected, and that technologies may vary in how broad their engagement potential is. Therefore, the recommendations should be viewed for what they are — practical guidelines derived from what was effective in another context — and should always be adjusted based on what is possible and relevant in the given situation. One cannot expect a specific effect on learning outcomes or student engagement simply by introducing a specific educational technology, and only few studies investigate aspects such as the importance of context, the role of the educator, how students interact, and what happens in the actual learning process (e.g., Bertheussen & Myrland, 2016 ; Butz et al., 2016 ; Evans, 2014 ; Steen-Utheim & Foldnes, 2018 ; Vercellotti, 2018 ). This calls for more research on the influence of course context and delivery on student engagement.

The synthesis also revealed that many aspects that determine whether the potential is realised are recognisable from traditional face-to-face teaching. For example, the educator’s active role as a facilitator of learning, active involvement of students, and consideration for students and their needs are crucial, as well as the technical support, feedback, authenticity, and learning environment. This is not surprising but important to remember when designing and delivering technology-enhanced, blended, and online learning. Careful considerations should be made for both the design and delivery of teaching: What is the purpose of educational technology, what potential does this technology hold for student engagement, and what determines whether this potential is realised? Thus, if all forms of engagement are to be supported by technology, the educator must have competencies in structuring, developing, and delivering technology-enhanced teaching, as well as taking the possibilities, engagement broadness, and limitations of the technology into account. Furthermore, the educator must be able to communicate and involve students in an activating way in high-taxonomic learning activities, as well as support students’ communication and interaction through suitable technology.

Data availability

The review is based on published research and other publicly available resources. A search protocol can be obtained from the authors upon reasonable request.

Abu Talib, M., Bettayeb, A. M., & Omer, R. I. (2021). Analytical study on the impact of technology in higher education during the age of COVID-19: Systematic literature review. Education and Information Technologies, 26 , 6719–6746. https://doi.org/10.1007/s10639-021-10507-1

Article   Google Scholar  

Arend, B. (2009). Encouraging critical thinking in online threaded discussions. Journal of Educators Online, 6 (1), n1. https://doi.org/10.9743/JEO.2009.1.1

Argyriou, P., Benamar, K., & Nikolajeva, M. (2022). What to Blend? Exploring the relationship between student engagement and academic achievement via a blended learning approach. Psychology Learning & Teaching, 21 (2), 126–137. https://doi.org/10.1177/14757257221091512

Arsenis, P., Flores, M., & Petropoulou, D. (2022). Enhancing graduate employability skills and student engagement through group video assessment. Assessment & Evaluation in Higher Education, 47 (2), 245–258. https://doi.org/10.1080/02602938.2021.1897086

Badenhorst, C., & Mather, C. (2014). Blogging geographies. Journal of Geography in Higher Education, 38 (2), 193–207. https://doi.org/10.1080/03098265.2014.908276

Balaji, M. S., & Chakrabarti, D. (2010). Student interactions in online discussion forum: Empirical research from ‘media richness theory’ perspective. Journal of Interactive Online Learning , 9 (1), 1-22.

Balta, N., & Awedh, M. H. (2017). The effect of student collaboration in solving physics problems using an online interactive response system. European Journal of Educational Research, 6 (3), 385–394. https://doi.org/10.12973/eu-jer.6.3.385

Barber, W., King, S., & Buchanan, S. (2015). Problem based learning and authentic assessment in digital pedagogy: Embracing the role of collaborative communities. Electronic Journal of e-Learning, 13 (2), 59–67.

Google Scholar  

Baum, E. J. (2013). Augmenting guided-inquiry learning with a blended classroom approach. Journal of College Science Teaching, 42 (6), 27–33.

Bawa, P. (2019). Using Kahoot to Inspire. Journal of Educational Technology Systems, 47 (3), 373–390. https://doi.org/10.1177/0047239518804173

Bender, R. M. (2021). From snaps to maps: Using literature, mobile applications, and mapping software to design an engaging L2 curriculum. Hispania, 104 (4), 557–570. https://doi.org/10.1353/hpn.2021.0126

Bertheussen, B. A., & Myrland, Ø. (2016). Relation between academic performance and students’ engagement in digital learning activities. Journal of Education for Business, 91 (3), 125–131. https://doi.org/10.1080/08832323.2016.1140113

Bolliger, D. U., & Armier, D. D., Jr. (2013). Active learning in the online environment: The integration of student-generated audio files. Active Learning in Higher Education, 14 (3), 201–211. https://doi.org/10.1177/1469787413498032

Bond, M., & Bedenlier, S. (2019). Facilitating student engagement through educational technology: Towards a conceptual framework. Journal of Interactive Media in Education, 2019 (1). Retrieved July 28, 2024, from https://jime.open.ac.uk/articles/10.5334/jime.528/

Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17 , 1–30. https://doi.org/10.1186/s41239-019-0176-8

Bradbury, N. A. (2016). Attention span during lectures: 8 seconds, 10 minutes, or more? Advances in Physiology Education, 40 (4). https://doi.org/10.1152/advan.00109.2016

Brame, C. J. (2016). Effective educational videos: Principles and guidelines for maximizing student learning from video content. CBE—Life Sciences Education, 15 (4), es6. https://doi.org/10.1187/cbe.16-03-0125

Browne, C. J. (2019). Assessing the engagement rates and satisfaction levels of various clinical health science student sub-groups using supplementary eLearning resources in an introductory anatomy and physiology unit. Health Education, 119 (1), 2–17. https://doi.org/10.1108/HE-04-2018-0020

Buckley, P., & Doyle, E. (2016). Gamification and Student Motivation. Interactive Learning Environments, 24 (6), 1162–1175. https://doi.org/10.1080/10494820.2014.964263

Buelow, J. R., Barry, T., & Rich, L. E. (2018). Supporting learning engagement with online students. Online Learning, 22 (4), 313–340. https://doi.org/10.24059/olj.v22i4.1384

Butz, N. T., Stupnisky, R. H., Pekrun, R., Jensen, J. L., & Harsell, S. M. (2016). The impact of emotions on student achievement in synchronous hybrid business and public administration programs: A longitudinal test of control-value theory. Decision Sciences Journal of Innovative Education, 14 (4), 441–474. https://doi.org/10.1111/dsji.12110

Çakir, A. M. K. (2020). Engaging students with questions: Attitudes towards using student response systems in higher education. Journal of Learning and Teaching in Digital Age, 5 (1), 24–34.

MathSciNet   Google Scholar  

Callahan, J. T. (2016). Assessing online homework in first-semester Calculus. PRIMUS, 26 (6), 545–556. https://doi.org/10.1080/10511970.2015.1128501

Campbell, C., & Monk, S. (2015). Introducing a learner response system to pre-service education students: Increasing student engagement. Active Learning in Higher Education, 16 (1), 25–36. https://doi.org/10.1177/1469787414558981

Camus, M., Hurt, N. E., Larson, L. R., & Prevost, L. (2016). Facebook as an online teaching tool: Effects on student participation, learning, and overall course performance. College Teaching, 64 (2), 84–94. https://doi.org/10.1080/87567555.2015.1099093

Cann, A. J. (2016). Increasing student engagement with practical classes through online pre-lab quizzes. Journal of Biological Education, 50 (1), 101–112. https://doi.org/10.1080/00219266.2014.986182

Cavaleri, M., Kawaguchi, S., Di Biase, B., & Power, C. (2019). How recorded audio-visual feedback can improve academic language support. Journal of University Teaching & Learning Practice, 16 (4). https://doi.org/10.53761/1.16.4.6

Cavanaugh, G., Condry, H. M., Afable, C. F., Morris, M., De, S., Madison, H. E., & Weiner, M. (2023). Immersive learning and participatory engagement: Connecting in the online classroom through virtual reality. International Journal of Distance Education Technologies (IJDET), 21 (1), 1–19. https://doi.org/10.4018/IJDET.317364

Caviglia, F., Dalsgaard, C., Davidsen, J., & Ryberg, T. (2018). Studerendes digitale læringsmiljøer: læringsplatform eller medieøkologi? Tidsskriftet Læring og Medier (LOM), 10 (18). https://doi.org/10.7146/lom.v10i18.96928

Christopoulos, A., Conrad, M., & Shukla, M. (2018). Interaction with educational games in hybrid virtual worlds. Journal of Educational Technology Systems, 46 (4), 385–413. https://doi.org/10.1177/0047239518757986

Chugh, R., & Ruhi, U. (2018). Social media in higher education: A literature review of facebook. Education and Information Technologies, 23 (2), 605–616. https://doi.org/10.1007/s10639-017-9621-2

Chulkov, D., & Wang, X. (2020). The educational value of simulation as a teaching strategy in a finance course. e-Journal of Business Education and Scholarship of Teaching, 14 (1), 40–56.

Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42 (2), 21–29. https://doi.org/10.1007/BF02299088

Clark, R. M., Besterfield-Sacre, M., Budny, D., Bursic, K. M., Clark, W. W., Norman, B. A., Parker, R. S., Patzer, I. I., & Slaughter, W. S. (2016). Flipping engineering courses: A school wide initiative. Advances in Engineering Education, 5 (3), 1–39.

Clements, J. C. (2015). Using facebook to enhance independent student engagement: A case study of first-year undergraduates. Higher Education Studies, 5 (4), 131–146. https://doi.org/10.5539/hes.v5n4p131

Cooke, S. (2017). Social teaching: Student perspectives on the inclusion of social media in higher education. Education and Information Technologies, 22 (1), 255–269. https://doi.org/10.1007/s10639-015-9444-y

Collins, K., Groff, S., Mathena, C., & Kupczynski, L. (2019). Asynchronous video and the development of instructor social presence and student engagement. Turkish Online Journal of Distance Education, 20 (1), 53–70. https://doi.org/10.17718/tojde.522378

Cook, B. R., & Babon, A. (2017). Active learning through online quizzes: Better learning and less (busy) work. Journal of Geography in Higher Education, 41 (1), 24–38. https://doi.org/10.1080/03098265.2016.1185772

Cooper, B., & Naatus, M. K. (2014). LinkedIn as a learning tool in business education. American Journal of Business Education (AJBE), 7 (4), 299–306. https://doi.org/10.19030/ajbe.v7i4.8815

Cossu, R., Awidi, I., & Nagy, J. (2022). Can we use online technology to rejig the traditional laboratory experience to improve student engagement? Higher Education Pedagogies, 7 (1), 1–19. https://doi.org/10.1080/23752696.2022.2068155

Crouch, C. H., & Mazur, E. (2001). Peer instruction: Ten years of experience and results. American Journal of Physics, 69 (9), 970–977. https://doi.org/10.1119/1.1374249

Dahlstrom, E., & Bichsel, J. (2014). ECAR study of undergraduate students and information technology, 2014 . Educause. Retrieved July 28, 2024, from https://library.educause.edu/~/media/files/library/2014/10/ers1406-pdf.pdf?la=en

Dalsgaard, C., & Paulsen, M. F. (2009). Transparency in cooperative online education. International Review of Research in Open and Distributed Learning , 10 (3). https://doi.org/10.19173/irrodl.v10i3.671

Daniel, J., Kanwar, A., & Uvalić-Trumbić, S. (2009). Breaking higher education’s iron triangle: Access, cost, and quality. Change: The Magazine of Higher Learning, 41 (2), 30–35. https://doi.org/10.3200/CHNG.41.2.30-35

Davis, K., Sridharan, H., Koepke, L., Singh, S., & Boiko, R. (2018). Learning and engagement in a gamified course: Investigating the effects of student characteristics. Journal of Computer Assisted Learning, 34 (5), 492–503. https://doi.org/10.1111/jcal.12254

Del Barrio-Garcia, S., Arquero, J. L., & Romero-Frías, E. (2015). Personal learning environments acceptance model: The role of need for cognition, e-learning satisfaction and students’ perceptions. Journal of Educational Technology & Society, 18 (3), 129–141.

Delello, J. A., McWhorter, R. R., & Camp, K. M. (2015). Using social media as a tool for learning: A multi-disciplinary study. International Journal on E-Learning, 14 (2), 163–180.

Dennen, V. P. (2005). From message posting to learning dialogues: Factors affecting learner participation in asynchronous discussion. Distance Education, 26 (1), 127–148. https://doi.org/10.1080/01587910500081376

Di Natale, A. F., Repetto, C., Riva, G., & Villani, D. (2020). Immersive virtual reality in K-12 and higher education: A 10‐year systematic review of empirical research. British Journal of Educational Technology, 51 (6), 2006–2033. https://doi.org/10.1111/bjet.13030

Diug, B., Kendal, E., & Ilic, D. (2016). Evaluating the use of twitter as a tool to increase engagement in medical education. Education for Health, 29 (3), 223–230. https://doi.org/10.4103/1357-6283.204216

Dixson, M. D., Greenwell, M. R., Rogers-Stacy, C., Weister, T., & Lauer, S. (2017). Nonverbal immediacy behaviors and online student engagement: Bringing past instructional research into the present virtual classroom. Communication Education, 66 (1), 37–53. https://doi.org/10.1080/03634523.2016.1209222

Dong, J. J., Hwang, W. Y., Shadiev, R., & Chen, G. Y. (2017). Pausing the classroom lecture: The use of clickers to facilitate student engagement. Active Learning in Higher Education, 18 (2), 157–172. https://doi.org/10.1177/1469787417707617

Douglas, T., James, A., Earwaker, L., Mather, C., & Murray, S. (2020). Online discussion boards: Improving practice and student engagement by harnessing facilitator perceptions. Journal of University Teaching & Learning Practice, 17 (3). https://doi.org/10.53761/1.17.3.7

Dragseth, M. R. (2020). Building student engagement through social media. Journal of Political Science Education, 16 (2), 243–256. https://doi.org/10.1080/15512169.2018.155042

Dumford, A. D., & Miller, A. L. (2018). Online learning in higher education: Exploring advantages and disadvantages for engagement. Journal of Computing in Higher Education, 30 (3), 452–465. https://doi.org/10.1007/s12528-018-9179-z

Dyson, B., Vickers, K., Turtle, J., Cowan, S., & Tassone, A. (2015). Evaluating the use of Facebook to increase student engagement and understanding in lecture-based classes. Higher Education, 69 (2), 303–313. https://doi.org/10.1007/s10734-014-9776-3

Edmonds, R., & Smith, S. (2017). From playing to designing: Enhancing educational experiences with location-based mobile learning games. Australasian Journal of Educational Technology, 33 (6), 41–53. https://doi.org/10.14742/ajet.3583

Educause Learning Initiative. (2011). Seven things you should know about gamification. Educause. Retrieved July 28, 2024 from: https://library.educause.edu/-/media/files/library/2011/8/eli7075-pdf.pdf

Elford, D., Lancaster, S. J., & Jones, G. A. (2021). Stereoisomers, not stereo enigmas: A stereochemistry escape activity incorporating augmented and immersive virtual reality. Journal of Chemical Education, 98 (5), 1691–1704. https://doi.org/10.1021/acs.jchemed.0c01283

Evans, C. (2014). Twitter for teaching: Can social media be used to enhance the process of learning? British Journal of Educational Technology, 45 (5), 902–915. https://doi.org/10.1111/bjet.12099

Evans, T., Kensington-Miller, B., & Novak, J. (2021). Effectiveness, efficiency, engagement: Mapping the impact of pre-lecture quizzes on educational exchange. Australasian Journal of Educational Technology, 37 (1), 163–177. https://doi.org/10.14742/ajet.6258

Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13 (11). https://doi.org/10.3390/educsci13111109

Fischer, C. M., Hoffman, M. S., Casey, N. C., & Cox, M. P. (2015). Software-based student response systems: An interdisciplinary initiative. Journal of Learning in Higher Education, 11 (2), 33–39.

Francescucci, A., & Foster, M. (2013). The VIRI (virtual, interactive, Real-Time, Instructor-Led) Classroom: The impact of blended synchronous online courses on student performance, engagement, and satisfaction. Canadian Journal of Higher Education, 43 (3), 78–91.

Francescucci, A., & Rohani, L. (2019). Exclusively synchronous online (VIRI) learning: The impact on student performance and engagement outcomes. Journal of Marketing Education, 41 (1), 60–69. https://doi.org/10.1177/0273475318818864

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74 (1), 59–109. https://doi.org/10.3102/00346543074001059

Fukuzawa, S., & Boyd, C. (2016). Student engagement in a large classroom: Using technology to generate a hybridized problem-based learning experience in a large first year undergraduate class. Canadian Journal for the Scholarship of Teaching and Learning, 7 (1), 1–17. https://doi.org/10.5206/cjsotl-rcacea.2016.1.7

Funnell, P. (2017). Using audience response systems to enhance student engagement and learning in information literacy teaching. Journal of Information Literacy , 11 (2). https://doi.org/10.11645/11.2.2238

Garrison, D. R. (1985). Three generations of technological innovations in distance education. Distance Education, 6 (2), 235–241. https://doi.org/10.1080/0158791850060208

Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7 (2), 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001

Gibbings, P., Lidstone, J., & Bruce, C. (2015). Students’ experience of problem-based learning in virtual space. Higher Education Research and Development, 34 (1), 74–88. https://doi.org/10.1080/07294360.2014.934327

Gleason, B., & Greenhow, C. (2017). Hybrid learning in higher education: The potential of teaching and learning with robot-mediated communication. Online Learning, 21 (4), 159–176. https://doi.org/10.24059/olj.v21i4.1276

Glowatz, M., & Bofin, L. (2014). Enhancing student engagement through social media. A school of business case study. EAI Endorsed Transactions on e-Learning , 1 (4). https://doi.org/10.4108/el.1.4.e4

Godsk, M., & Elving, P.R. (2024). ChatGPT for Learning in Higher Education: Benefits, Downsides, and Implications. Journal of Interactive Learning Research, 35 (1), 31-69. Waynesville, NC: Association for the Advancement of Computing in Education (AACE). Retrieved July 28, 2024 from https://www.learntechlib.org/primary/p/222471/

Godsk, M., Kristiansen, B., & Møller, K. L. (2021). Digital læringsteknologis potentiale for studerendes engagement. Pædagogisk indblik 12. DPU. Aarhus University Press. Retrieved July 28, 2024 from https://unipress.dk/media/18273/12-digital-laeringsteknologis-potentiale-for-studerendes-engagement.pdf

Gnaur, D., & Hüttel, H. (2014). How a flipped learning environment affects learning in a course on theoretical computer science. Advances in Web-Based Learning – ICWL 2014 (pp. 219–228). Springer International Publishing. https://doi.org/10.1007/978-3-319-09635-3_25

Chapter   Google Scholar  

Graham, M. (2014). Social media as a tool for increased student participation and engagement outside the classroom in higher education. Journal of Perspectives in Applied Academic Practice, 2 (3), 16. https://doi.org/10.14297/jpaap.v2i3.113

Grant, N. S., & Bolin, B. L. (2016). Digital storytelling: A method for engaging students and increasing cultural competency. Journal of Effective Teaching, 16 (3), 44–61.

Gray, J. A., & DiLoreto, M. (2016). The effects of student engagement, student satisfaction, and perceived learning in online learning environments. International Journal of Educational Leadership Preparation, 11 (1), 1–20.

Greenhalgh, T., & Peacock, R. (2005). Effectiveness and efficiency of search methods in systematic reviews of complex evidence: Audit of primary sources. Bmj, 331 (7524), 1064–1065. https://doi.org/10.1136/bmj.38636.593461.68

Gregory, P. L., Gregory, K. M., & Eddy, E. R. (2016). Factors contributing to student engagement in an instructional facebook group for undergraduate mathematics. Journal of Computers in Mathematics and Science Teaching, 35 (3), 249–268.

Gross, B., Marinari, M., Hoffman, M., DeSimone, K., & Burke, P. (2015). Flipped @ SBU: Student satisfaction and the college classroom. Educational Research Quarterly, 39 (2), 36–52.

Habel, C., & Stubbs, M. (2014). Mobile phone voting for participation and engagement in a large compulsory law course. Association for learning technology journal. Research in Learning Technology, 22 , 1–15. https://doi.org/10.3402/rlt.v22.19537

Han, J. H., & Finkelstein, A. (2013). Understanding the effects of professors’ pedagogical development with clicker assessment and feedback technologies and the impact on students’ engagement and learning in higher education. Computers & Education, 65 (2013), 64–76. https://doi.org/10.1016/j.compedu.2013.02.002

Hartley, J., & Davies, I. K. (1978). Note-taking: A critical review. Programmed Learning and Educational Technology, 15 (3), 207–224. https://doi.org/10.1080/0033039780150305

Harvey, T., Carlson, J., Struck, M., & Moroz, K. (2018). Feeling real: Social presence within online discussions. Distance Learning, 15 (4), 13–27.

Hayes, A., Daughrity, L. A., & Meng, N. (2021). Approaches to integrate virtual reality into K-16 lesson plans: An introduction for teachers. TechTrends, 65 , 394–401. https://doi.org/10.1007/s11528-020-00572-7

Heaslip, G., Donovan, P., & Cullen, J. G. (2014). Student response systems and learner engagement in large classes. Active Learning in Higher Education, 15 (1), 11–24. https://doi.org/10.1177/1469787413514648

Henderson, M., & Phillips, M. (2015). Video-based feedback on student assessment: Scarily personal. Australasian Journal of Educational Technology, 31 (1), 51–66. https://doi.org/10.14742/ajet.1878

Henrie, C. R., Bodily, R., Manwaring, K. C., & Graham, C. R. (2015a). Exploring intensive longitudinal measures of student engagement in blended learning. International Review of Research in Open and Distributed Learning, 16 (3), 131–155. https://doi.org/10.19173/irrodl.v16i3.2015

Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015b). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90 , 36–53. https://doi.org/10.1016/j.compedu.2015.09.005

Herodotou, C., Muirhead, D. K., Aristeidou, M., Hole, M. J., Kelley, S., Scanlon, E., & Duffy, M. (2020). Blended and online learning: A comparative study of virtual microscopy in higher education. Interactive Learning Environments, 28 (6), 713–728. https://doi.org/10.1080/10494820.2018.1552874

Holbrey, C. E. (2020). Kahoot! Using a game-based approach to blended learning to support effective learning environments and student engagement in traditional lecture theatres. Technology Pedagogy and Education, 29 (2), 191–202. https://doi.org/10.1080/1475939X.2020.1737568

Holmes, N. (2015). Student perceptions of their learning and engagement in response to the Use of a continuous E-Assessment in an undergraduate module. Assessment & Evaluation in Higher Education, 40 (1), 1–14. https://doi.org/10.1080/02602938.2014.881978

Holmes, N. (2018). Engaging with assessment: Increasing student engagement through continuous assessment. Active Learning in Higher Education, 19 (1), 23–34. https://doi.org/10.1177/1469787417723230

Irby, S. M., Borda, E. J., & Haupt, J. (2018). Effects of implementing a hybrid wet lab and online module lab curriculum into a general chemistry course: Impacts on student performance and engagement with the chemistry triplet. Journal of Chemical Education, 95 (2), 224–232. https://doi.org/10.1021/acs.jchemed.7b00642

Jozwiak, J. (2015). Helping students to succeed in general education political science courses? Online assignments and in-class activities. International Journal of Teaching and Learning in Higher Education, 27 (3), 393–406.

Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38 (5), 758–773. https://doi.org/10.1080/03075079.2011.598505

Karaksha, A., Grant, G., Anoopkumar-Dukie, S., Nirthanan, S. N., & Davey, A. K. (2013). Student engagement in pharmacology courses using online learning tools. American Journal of Pharmaceutical Education, 77 (6). https://doi.org/10.5688/ajpe776125

Kay, R. H., & LeSage, A. (2009). Examining the benefits and challenges of using audience response systems: A review of the literature. Computers & Education, 53 (3), 819–827. https://doi.org/10.1016/j.compedu.2009.05.001

Kebble, P. G. (2017). Assessing online asynchronous communication strategies designed to enhance large student cohort engagement and foster a community of learning. Journal of Education and Training Studies, 5 (8), 92–100. https://doi.org/10.11114/jets.v5i8.2539

Kent, M. (2013). Changing the conversation: Facebook as a venue for online class discussion in higher education. Journal of Online Learning and Teaching, 9 (4), 546–565.

Khan, K. S., Kunz, R., Kleijnen, J., & Antes, G. (2003). Five steps to conducting a systematic review. Journal of the Royal Society of Medicine, 96 (3), 118–121. https://doi.org/10.1177/014107680309600304

Kim, H. J., Hong, A. J., & Song, H. D. (2019). The roles of academic engagement and digital readiness in students’ achievements in university e-learning environments: Revista De Universidad Y Sociedad Del Conocimiento. International Journal of Educational Technology in Higher Education, 16 (1), 1–18. https://doi.org/10.1186/s41239-019-0152-3

Kirkwood, A., & Price, L. (2014). Technology-enhanced learning and teaching in higher education: What is ‘enhanced’ and how do we know? A critical literature review. Learning Media and Technology, 39 (1), 6–36. https://doi.org/10.1080/17439884.2013.770404

Kunka, B. A. (2020). Twitter in higher education: increasing student engagement. Educational Media International, 57 (4), 316–331. https://doi.org/10.1080/09523987.2020.1848508

Lawrence, J., Brown, A., Redmond, P., & Basson, M. (2019). Engaging the disengaged: Exploring the use of course-specific learning analytics and nudging to enhance online student engagement. Student Success, 10 (2), 47–58. https://doi.org/10.5204/ssj.v10i2.1295

Lee, E., & Harris, R. (2018). The effects of online glossary quizzes and student autonomy on domain vocabulary learning in business law. Journal of Computing in Higher Education, 30 (2), 326–343. https://doi.org/10.1007/s12528-018-9183-3

Lillejord, S., Børte, K., Nesje, K., & Ruud, E. (2018). Learning and teaching with technology in higher education–a systematic review (Vol. 2, pp. 40–64). Knowledge Centre for Education.

Lim, W. N. (2017). Improving student engagement in higher education through mobile-based interactive teaching model using socrative. In 2017 IEEE Global Engineering Education Conference (EDUCON) (pp. 404–412). IEEE.

Lin, S. Y., Aiken, J. M., Seaton, D. T., Douglas, S. S., Greco, E. F., Thoms, B. D., & Schatz, M. F. (2017). Exploring physics students’ engagement with online instructional videos in an introductory mechanics course. Physical Review Physics Education Research, 13 (2), 020138–020131. https://doi.org/10.1103/PhysRevPhysEducRes.13.020138

Linnenbrink-Garcia, L., Rogat, T. K., & Koskey, K. L. (2011). Affect and engagement during small group instruction. Contemporary Educational Psychology, 36 (1), 13–24. https://doi.org/10.1016/j.cedpsych.2010.09.001

Littell, J. H., Corcoran, J., & Pillai, V. (2008). Systematic reviews and meta-analysis. Oxford University Press . https://doi.org/10.1093/acprof:oso/9780195326543.001.0001

Luo, H., Li, G., Feng, Q., Yang, Y., & Zuo, M. (2021). Virtual reality in K-12 and higher education: A systematic review of the literature from 2000 to 2019. Journal of Computer Assisted Learning, 37 (3), 887–901. https://doi.org/10.1111/jcal.12538

Lupinski, K., & Kaufman, M. (2023). Exploring student perceptions, Engagement, and satisfaction with instructor made videos in online courses. International Journal on E-Learning , 43–73. Retrieved July 28, 2024, from https://www.learntechlib.org/primary/p/219870/ . Association for the Advancement of Computing in Education (AACE).

MacKnight, C. B. (2000). Teaching critical thinking through online discussions. Educause Quarterly, 23 (4), 38–41.

Makransky, G., & Lilleholt, L. (2018). A structural equation modelling investigation of the emotional value of immersive virtual reality in education. Educational Technology Research and Development, 66 (5), 1141–1164. https://doi.org/10.1007/s11423-018-9581-2

Makransky, G., & Petersen, G. B. (2019). Investigating the process of learning with desktop virtual reality: A structural equation modeling approach. Computers & Education, 134 , 15–30.

Marques, M. A., Viegas, M. C., Costa-Lobo, M. C., Fidalgo, A. V., Alves, G. R., Rocha, J. S., & Gustavsson, I. (2014). How remote labs impact on course outcomes: Various practices using VISIR. IEEE Transactions on Education, 57 (3), 151–159.

Mathany, C., & Dodd, J. (2018). Student-generated interview podcasts: An assignment template. Collected Essays on Learning and Teaching . https://doi.org/10.22329/celt.v11i0.4971

Matthew, A., & Butler, D. (2017). Narrative, Machinima and cognitive realism: Constructing an authentic real-world learning experience for law students. Australasian Journal of Educational Technology, 33 (1), 148–162. https://doi.org/10.14742/ajet.2846

Mayer, R. E. (2008). Applying the science of learning: Evidence-based principles for the design of multimedia instruction. American Psychologist, 63 (8). https://doi.org/10.1037/0003-066X.63.8.760

McCallum, S., Schultz, J., Sellke, K., & Spartz, J. (2015). An examination of the flipped classroom approach on college student academic involvement. International Journal of Teaching and Learning in Higher Education, 27 (1), 42–55.

McCarthy, J. (2015). Evaluating written, audio and video feedback in higher education summative assessment tasks. Issues in Educational Research, 25 (2), 153–169.

McDaniel, P. N. (2022). Teaching, learning, and exploring the geography of North America with virtual globes and geovisual narratives. Journal of Geography, 121 (4), 125–140. https://doi.org/10.1080/00221341.2022.2119597

McGuinness, C., & Fulton, C. (2019). Digital literacy in higher education: A case study of student engagement with e-Tutorials using blended learning. Journal of Information Technology Education: Innovations in Practice, 18 , 1–28. https://doi.org/10.28945/4190

McKechnie, L. E. F. (2008). Reactivity. In L. M. Given (Ed.), The SAGE Encyclopedia of Qualitative Research Methods . SAGE Publications, Inc.

McKenzie, M., & Ziemann, M. (2020). Assessment of the web-based audience response system socrative for biomedical science revision classes. International Journal of Educational Research Open, 1 ,. https://doi.org/10.1016/j.ijedro.2020.100008

McKenzie, W. A., Perini, E., Rohlf, V., Toukhsati, S., Conduit, R., & Sanson, G. (2013). A blended learning lecture delivery model for large and diverse undergraduate cohorts. Computers & Education, 64 , 116–126. https://doi.org/10.1016/j.compedu.2013.01.009

Megele, C. (2015). eABLE: Embedding social media in academic curriculum as a learning and assessment strategy to enhance students learning and E-Professionalism. Innovations in Education and Teaching International, 52 (4), 414–425. https://doi.org/10.1080/14703297.2014.890951

Mello, L. V. (2016). Fostering postgraduate student engagement: Online resources supporting self-directed learning in a diverse cohort. Research in Learning Technology, 24 , 1–16. https://doi.org/10.3402/rlt.v24.29366

Mihret, D. G., Abayadeera, N., Watty, K., & McKay, J. (2017). Teaching auditing using cases in an online learning environment: The role of ePortfolio Assessment. Accounting Education, 26 (4), 335–357. https://doi.org/10.1080/09639284.2017.1292466

Mirmotahari, O., Berg, Y., Fremstad, E., & Damsa, C. (2019). Student engagement by employing student peer reviews with criteria-based assessment. In IEEE Global Engineering Education Conference (EDUCON) (pp. 1152–1157). Dubai, United Arab Emirates. https://doi.org/10.1109/EDUCON.2019.8725174

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Prisma Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS med, 6 (7), e1000097. https://doi.org/10.1136/bmj.b2535

Mokoena, S. (2013). Engagement with and participation in online discussion forums. Turkish Online Journal of Educational Technology-TOJET, 12 (2), 97–105.

Mooney, M., Southard, S., & Burton, C. H. (2014). Shifting from obligatory discourse to rich dialogue: Promoting student interaction in asynchronous threaded discussion postings. Online Journal of Distance Learning Administration, 17 (1), 1–12.

Morena, M. A., Smith, S., & Talbert, R. (2019). Video made the calculus star. Primus, 29 (1), 43–55. https://doi.org/10.1080/10511970.2017.1396568

Muir, T., Milthorpe, N., Stone, C., Dyment, J., Freeman, E., & Hopwood, B. (2019). Chronicling engagement: Students’ experience of online learning over time. Distance Education, 40 (2), 262–277. https://doi.org/10.1080/01587919.2019.1600367

Murphy, C. A., & Stewart, J. C. (2015). The impact of online or F2F lecture choice on student achievement and engagement in a large lecture-based science course: Closing the gap. Online Learning, 19 (3), 91–110. https://doi.org/10.24059/olj.v19i3.536

Nesenbergs, K., Abolins, V., Ormanis, J., & Mednis, A. (2020). Use of augmented and virtual reality in remote higher education: A systematic umbrella review. Education Sciences, 11 (1). https://doi.org/10.3390/educsci11010008

Neustifter, R., Kukkonen, T., Coulter, C., & Landry, S. (2016). Introducing backchannel technology into a large undergraduate course. Canadian Journal of Learning and Technology, 42 (1), 1–22.

Newmann, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). The significance and sources of student engagement. In F. Newmann (Ed.), Student engagement and achievement in American secondary schools (pp. 11–39). Teachers College Press.

Ng, K. (2018). Implementation of new communication tools to an online chemistry course. Journal of Educators Online, 15 (1), 1–6. https://doi.org/10.9743/JEO2018.15.1.8

Noetel, M., Griffith, S., Delaney, O., Sanders, T., Parker, P., del Pozo Cruz, B., & Lonsdale, C. (2021). Video improves learning in higher education: A systematic review. Review of Educational Research, 91 (2), 204–236. https://doi.org/10.3102/0034654321990713

Northey, G., Bucic, T., Chylinski, M., & Govind, R. (2015). Increasing student engagement using asynchronous learning. Journal of Marketing Education, 37 (3), 171–180. https://doi.org/10.1177/0273475315589814

O’Brien, M., & Freund, K. (2018). Lessons learned from introducing social media use in undergraduate economics research. International Journal of Education and Development Using ICT , 14 (1). https://www.learntechlib.org/p/183552/

O’Callaghan, F. V., Neumann, D. L., Jones, L., & Creed, P. A. (2017). The use of lecture recordings in higher education: A review of institutional, student, and lecturer issues. Education and Information Technologies, 22 , 399–415. https://doi.org/10.1007/s10639-015-9451-z

O’Shea, S. E., Stone, C., & Delahunty, J. (2015). I ‘Feel’ like I am at University even though I am online. Exploring how students narrate their engagement with higher education institutions in an online learning environment. Distance Education, 36 (1), 41–58. https://doi.org/10.1080/01587919.2015.1019970

Oh, E. G., & Kim, H. S. (2016). Understanding cognitive engagement in online discussion: Use of a scaffolded, audio-based argumentation activity. International Review of Research in Open and Distributed Learning, 17 (5), 28–48. https://doi.org/10.19173/irrodl.v17i5.2456

Orcutt, J. M., & Dringus, L. P. (2017). Beyond being there: Practices that establish presence, engage students and influence intellectual curiosity in a structured online learning environment. Online Learning, 21 (3), 15–35. https://doi.org/10.24059/olj.v21i3.1231

Osman, S. Z. M. (2022). Combining synchronous and asynchronous learning: Student satisfaction with online learning using learning management systems. Journal of Education and E-Learning Research, 9 (3), 147–154. https://doi.org/10.20448/jeelr.v9i3.4103

Page, L., Hullett, E. M., & Boysen, S. (2020). Are you a robot? Revitalizing online learning and discussion boards for today’s modern learner. The Journal of Continuing Higher Education, 68 (2), 128–136. https://doi.org/10.1080/07377363.2020.1745048

Paiva, R. C., Ferreira, M. S., & Frade, M. M. (2017). Intelligent tutorial system based on personalized system of instruction to teach or remind mathematical concepts. Journal of Computer Assisted Learning, 33 (4), 370–381. https://doi.org/10.1111/jcal.12186

Pallas, J., Eidenfalk, J., & Engel, S. (2019). Social networking sites and learning in international relations: The impact of platforms. Australasian Journal of Educational Technology, 35 (1), 16–27. https://doi.org/10.14742/ajet.3637

Papanastasiou, G., Drigas, A., Skianis, C., Lytras, M., & Papanastasiou, E. (2019). Virtual and augmented reality effects on K-12, higher and tertiary education students’ twenty-first century skills. Virtual Reality, 23 , 425–436. https://doi.org/10.1007/s10055-018-0363-2

Patton, M. Q. (2015). Qualitative designs and data collection (4th ed.). Sage Publications.

Payne, L. (2019). Student engagement: Three models for its investigation. Journal of Further and Higher Education, 43 (5), 641–657. https://doi.org/10.1080/0309877X.2017.1391186

Pearson, J. (2018). Engaging practical students through audio feedback. Practitioner Research in Higher Education, 11 (1), 87–94.

Pellas, N., & Kazanidis, I. (2015). On the value of second life for students’ engagement in blended and online courses: A comparative study from the higher education in Greece. Education and Information Technologies, 20 (3), 445–466. https://doi.org/10.1007/s10639-013-9294-4

Pellas, N., Mystakidis, S., & Kazanidis, I. (2021). Immersive virtual reality in K-12 and higher education: A systematic review of the last decade scientific literature. Virtual Reality, 25 (3), 835–861. https://doi.org/10.1007/s10639-013-9294-4

Pepple, D. G. (2022). An ecological perspective of student engagement through digital technology: Practical application and implications. British Educational Research Journal, 48 (6), 1216–1231.  https://doi.org/10.1002/berj.3823

Pickering, J. D., & Swinnerton, B. J. (2019). Exploring the dimensions of medical student engagement with technology-enhanced learning resources and assessing the impact on assessment outcomes. Anatomical Sciences Education, 12 (2), 117–128. https://doi.org/10.1002/ase.1810

Plump, C. M., & LaRosa, J. (2017). Using Kahoot! In the classroom to create engagement and active learning: A game-based technology solution for eLearning novices. Management Teaching Review, 2 (2), 151–158. https://doi.org/10.1177/2379298116689783

Porcaro, P. A., Jackson, D. E., McLaughlin, P. M., & O’Malley, C. J. (2016). Curriculum design of a flipped classroom to enhance haematology learning. Journal of Science Education and Technology, 25 (3), 345–357. https://doi.org/10.1007/s10956-015-9599-8

Pourdana, N. (2022). Impacts of computer-assisted diagnostic assessment on sustainability of L2 learners’ collaborative writing improvement and their engagement modes. Asian-Pacific Journal of Second and Foreign Language Education, 7 (1), 11. https://doi.org/10.1186/s40862-022-00139-4

Prestridge, S. (2014). A focus on students’ Use of Twitter – their interactions with each other, content and interface. Active Learning in Higher Education, 15 (2), 101–115. https://doi.org/10.1177/1469787414527394

Price, L., & Kirkwood, A. (2011). Enhancing professional learning and teaching through technology: A synthesis of evidence-based practice among teachers in higher education. Higher Education Academy . Retrieved from: http://oro.open.ac.uk/30686/1/1_PLATP_main_report.pdf . https://doi.org/10.1080/17439884.2012.696543

Puentedura, R. (2010). SAMR and TPCK: Intro to Advanced Practice. http://hippasus.com/resources/sweden2010/SAMR_TPCK_IntroToAdvancedPractice.pdf . Accessed 30 July 2013.

Rafiq, A. A., Triyono, M. B., & Djatmiko, I. W. (2022). Enhancing student engagement in vocational education by using virtual reality. Waikato Journal of Education, 27 (3), 175–188. https://doi.org/10.15663/wje.v27i3.964

Rasi, P., & Vuojärvi, H. (2018). Toward personal and emotional connectivity in mobile higher education through asynchronous formative audio feedback. British Journal of Educational Technology, 49 (2), 292–304. https://doi.org/10.1111/bjet.12587

Ravenscroft, B., & Luhanga, U. (2018). Enhancing student engagement through an institutional blended learning initiative: A case study. Teaching & Learning Inquiry, 6 (2), 97–114. https://doi.org/10.20343/teachlearninqu.6.2.8

Ravishankar, J., Epps, J., & Ambikairajah, E. (2018). A flipped mode teaching approach for large and advanced electrical engineering courses. European Journal of Engineering Education, 43 (3), 413–426. https://doi.org/10.1080/03043797.2017.1383974

Reeve, J., & Tseng, C. M. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36 (4), 257–267. https://doi.org/10.1016/j.cedpsych.2011.05.002

Remón, J., Sebastián, V., Romero, E., & Arauzo, J. (2017). Effect of using smartphones as clickers and tablets as digital whiteboards on students’ engagement and learning. Active Learning in Higher Education, 18 (2), 173–187. https://doi.org/10.1177/1469787417707618

Ribiero, S. P. M. (2016). Developing intercultural awareness using digital storytelling. Language and Intercultural Communication, 16 (1), 69–82. https://doi.org/10.1080/14708477.2015.1113752

Roberts, J. C. (2015). Evaluating the effectiveness of lecture capture: Lessons learned from an undergraduate political research class. Journal of Political Science Education, 11 (1), 45–60. https://doi.org/10.1080/15512169.2014.985104

Robson, D., & Basse, B. (2018). Advantages and disadvantages of an innovative tablet technology learning activity: A ten year case study in small tertiary mathematics classrooms. Journal of Information Technology Education: Innovations in Practice, 17 , 225–239. https://doi.org/10.28945/4165

Russell, J. E., Van Horne, S., Ward, A. S., Bettis, I. I. I., Sipola, E. A., Colombo, M., & Rocheford, M. K. (2016). Large lecture transformation: Adopting evidence-based practices to increase student engagement and performance in an introductory science course. Journal of Geoscience Education, 64 (1), 37–51. https://doi.org/10.5408/15-084.1

Salmon, G. (2000). E-Moderating: The key to teaching and learning online (1st ed.). Routledge.

Sawang, S., O’Connor, P., & Ali, M. (2017). IEngage: Using technology to enhance students’ engagement in a large classroom. Journal of Learning Design, 10 (1), 11–19. https://doi.org/10.5204/jld.v9i3.292

Scagnoli, N. I., Choo, J., & Tian, J. (2019). Students’ insights on the Use of video lectures in online classes. British Journal of Educational Technology, 50 (1), 399–414. https://doi.org/10.1111/bjet.12572

Schindler, L. A., Burkholder, G. J., Morad, O. A., et al. (2017). Computer-based technology and student engagement: A critical review of the literature. International Journal of Educational Technology in Higher Education, 14 (25), 1–28. https://doi.org/10.1186/s41239-017-0063-0

Scott, O. K. M., & Stanway, A. R. (2015). Tweeting the lecture: How social media can increase student engagement in higher education. Sport Management Education Journal (Human Kinetics) , 9 (2). https://doi.org/10.1123/SMEJ.2014-0038

Seery, M. K. (2015). ConfChem conference on flipped classroom: Student engagement with flipped chemistry lectures. Journal of Chemical Education, 92 (9), 1566–1567. https://doi.org/10.1021/ed500919u

Selwyn, N. (2016). Digital downsides: Exploring University Students’ negative engagements with digital technology. Teaching in Higher Education, 21 (8), 1006–1021. https://doi.org/10.1080/13562517.2016.1213229

Sharma, P., & Tietjen, P. (2016). Examining patterns of participation and meaning making in student blogs: A case study in higher education. American Journal of Distance Education, 30 (1), 2–13. https://doi.org/10.1080/08923647.2016.1119605

Shaw, C. S., & Irwin, K. C. (2017). Forum quality or quantity: What is driving student engagement online? Online Journal of Distance Learning Administration, 20 (3).

Shaw, J., Kominko, S., & Terrion, J. L. (2015). Using lecturetools to enhance student-instructor relations and student engagement in the large class. Research in Learning Technology, 23 , 1–14. https://doi.org/10.3402/rlt.v23.27197

Sobocan, M., & Klemenc-Ketis, Z. (2017). Medical students’ attitudes towards the use of virtual patients. Journal of Computer Assisted Learning, 33 (4), 393–402. https://doi.org/10.1111/jcal.12190

Song, D., Oh, E. Y., & Glazewski, K. (2017). Student-generated questioning activity in second language courses using a customized personal response system: A case study. Educational Technology Research and Development, 65 (6), 1425–1449. https://doi.org/10.1007/s11423-017-9520-7

Speicher, O., & Stollhans, S. (2015). Feedback on feedback: does it work? Proceedings of the 2015 EUROCALL Conference , Padova, Italy, (pp. 507–511). Dublin: Research-publishing.net.

Steadman, R. G. (2015). Establishing an atmosphere for critical thinking in the online classroom. Journal of Instructional Research, 4 , 3–11.

Steele, J. P., Robertson, S. N., & Mandernach, B. J. (2018). Beyond content: The value of instructor-student connections in the online classroom. Journal of the Scholarship of Teaching and Learning, 18 (4), 130–150. https://doi.org/10.14434/josotl.v18i4.23430

Steen-Utheim, A. T., & Foldnes, N. (2018). A qualitative investigation of student engagement in a flipped classroom. Teaching in Higher Education, 23 (3), 307–324. https://doi.org/10.1080/13562517.2017.1379481

Subhash, S., & Cudney, E. A. (2018). Gamified learning in higher education: A systematic review of the literature. Computers in Human Behavior, 87 , 192–206. https://doi.org/10.1016/j.chb.2018.05.028

Sullivan, D., & Watson, S. (2015). Peer assessment within hybrid and online courses: Students’ view of its potential and performance. Journal of Educational Issues, 1 (1), 1–18. https://doi.org/10.5296/jei.v1i1.7255

Sun, J. C. Y., Martinez, B., & Seli, H. (2014). Just-in-time or plenty-of-time teaching? Different electronic feedback devices and their effect on Student Engagement. Journal of Educational Technology & Society, 17 (2), 234–244.

Szabo, Z., & Schwartz, J. (2011). Learning methods for teacher education: The use of online discussions to improve critical thinking. Technology Pedagogy and Education, 20 (1), 79–94. https://doi.org/10.1080/1475939X.2010.534866

Thomas, M. P., Türkay, S., & Parker, M. (2017). Explanations and interactives improve subjective experiences in Online Courseware. International Review of Research in Open and Distributed Learning, 18 (7), 213–241. https://doi.org/10.19173/irrodl.v18i7.3076

Tiernan, P. (2014). A study of the use of twitter by students for lecture engagement and discussion. Education and Information Technologies, 19 (4), 673–690. https://doi.org/10.1007/s10639-012-9246-4

Trenholm, S., Hajek, B., Robinson, C. L., Chinnappan, M., Albrecht, A., & Ashman, H. (2019). Investigating undergraduate Mathematics learners’ cognitive engagement with recorded lecture videos. International Journal of Mathematical Education in Science and Technology, 50 (1), 3–24. https://doi.org/10.1080/0020739X.2018.1458339

Trowler, V. (2010). Student engagement literature review. The Higher Education Academy, 11 (1), 1–15.

Truhlar, A. M., Walter, M. T., & Williams, K. M. (2018). Student engagement with course content and peers in synchronous online discussions. Online Learning, 22 (4), 289–312. https://doi.org/10.24059/olj.v22i4.1389

Vayre, E., & Vonthron, A. M. (2017). Psychological Engagement of students in Distance and Online Learning: Effects of Self-Efficacy and Psychosocial processes. Journal of Educational Computing Research, 55 (2), 197–218. https://doi.org/10.1177/0735633116656849

Veluvali, P., & Surisetti, J. (2022). Learning management system for greater learner engagement in higher education—a review. Higher Education for the Future, 9 (1), 107–121. https://doi.org/10.1177/23476311211049855

Vercellotti, M. L. (2018). Do interactive learning spaces increase student achievement? A comparison of Classroom Context. Active Learning in Higher Education, 19 (3), 197–210. https://doi.org/10.1177/1469787417735606

Viswanathan, S., & Radhakrishnan, B. (2018). A novel ’Game Design’ methodology for STEM Program. International Journal of Game-Based Learning, 8 (4), 1–17.

Wdowik, S. (2014). Using a synchronous online learning environment to promote and enhance transactional engagement beyond the classroom. Campus-Wide Information Systems, 31 (4), 264–275. https://doi.org/10.1108/CWIS-10-2013-0057

Weller, M. (2007). Virtual learning environments: Using, choosing and developing your VLE . Routledge.

Book   Google Scholar  

Willis, J., Davis, K., & Chaplin, S. (2013). Sociocultural affordances of online peer Engagement. Journal of Learning Design, 6 (1), 34–45.

Willis, R., Yeigh, T., Lynch, D., Smith, R., Provost, S., Sell, K., & Turner, D. (2018). Towards a strategic blend in education: A review of the blended learning literature . Oxford Global Press.

Wimpenny, K., & Savin-Baden, M. (2013). Alienation, agency and authenticity: A synthesis of the literature on student engagement. Teaching in Higher Education, 18 (3), 311–326. https://doi.org/10.1080/13562517.2012.725223

Wood, R., & Shirazi, S. (2020). A systematic review of audience response systems for teaching and learning in higher education: The student experience. Computers & Education, 153 ,. https://doi.org/10.1016/j.compedu.2020.103896

Wyatt, B. (2021). Insights into student participation in a soil physics course during COVID-19 emergency online learning. Natural Sciences Education, 50 (1). https://doi.org/10.1002/nse2.20036

Article   MathSciNet   Google Scholar  

Yates, A., Brindley-Richards, W., & Thistoll, T. (2014). Student engagement in distance-based vocational education. Journal of Open Flexible and Distance Learning, 18 (2), 29–44. https://doi.org/10.61468/jofdl.v18i2.228

Yilmaz, O. (2017). Learner centered classroom in Science instruction: Providing feedback with technology integration. International Journal of Research in Education and Science, 3 (2), 604–613. https://doi.org/10.21890/ijres.328091

Yousuf, B., & Conlan, O. (2018). Supporting student engagement through explorable visual narratives. IEEE Transactions on Learning Technologies, 11 (3), 307–320.

Zanjani, N., Edwards, S. L., Nykvist, S., & Geva, S. (2017). The important elements of LMS design that affect user engagement with E-learning tools within LMSs in the higher education sector. Australasian Journal of Educational Technology, 33 (1), 19–31. https://doi.org/10.14742/ajet.2938

Zepke, N. (2015). Student engagement research: Thinking beyond the mainstream. Higher Education Research & Development, 34 (6), 1311–1323. https://doi.org/10.1080/07294360.2015.1024635

Zhan, Y. (2023). Beyond technology: Factors influencing the effects of teachers’ audio feedback on students’ project-based learning. Technology Pedagogy and Education, 32 (1), 91–103. https://doi.org/10.1080/1475939X.2022.2093965

Download references

Acknowledgements

The authors would like to thank Bente Kristiansen for her contribution to screening articles in the early version of the literature review as well as Jens Laurs Kærsgaard and Birthe Aagesen for feedback on earlier versions of this article.

Open access funding provided by Aarhus Universitet

Author information

Authors and affiliations.

Centre for Educational Development, Aarhus University, Aarhus C, DK-8000, Denmark

Mikkel Godsk & Karen Louise Møller

You can also search for this author in PubMed   Google Scholar

Contributions

The authors have contributed equally to the research and the article.

Corresponding author

Correspondence to Mikkel Godsk .

Ethics declarations

Financial interests, ethics approval.

Not applicable.

Confirmation that the content of the manuscript has not been published, or submitted for publication elsewhere (see our Duplicate publication policy)

The results of the initial study from 2021 and its preliminary findings have been published in a Danish e-book (Godsk et al., 2021 ). Since this publication was based on narrow searches, only included results from before COVID-19), and did not include a specific research question or similar focus, everything has been completely revised, extended, and updated. Consequently, only very few elements from this e-book can be found in this article. Therefore, we consider the submitted article to be original and not published. Engaging students in higher education with educational technology.

Competing interests

Additional information, publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Literature search

The literature review was guided by a PRISMA process (Khan et al., 2003 ; Littell et al., 2008 ; Moher et al., 2009 ; Appendix Fig.  2 ) and follow-up hand searches. The review identified eight clusters (henceforth referred to as “types”) of educational technologies, leading to focused follow-up hand searches on each technology type. This minimised the risk of overlooking key publications due to a single protocol-driven search strategy (Greenhalgh & Peacock, 2005 ). Figure  2 provides an overview of the process.

The PRISMA flow diagram

1.1 Search procedure and identification

The search in the first PRISMA process was carried out in January 2019 in four international databases: ERIC, Education Database, Australian Education Index, and British Education Index; eight Scandinavian databases: Bibliotek.dk, Forskningsdatabasen, Libris, Swepub, DIVA Portal, Oria, Christin, and Norart; and 12 Scandinavian knowledge-producing institutions’ databases and publications (see Godsk et al., 2021 ). The search combined three concepts and their synonyms: (1) educational technology or technology-enhanced learning (the means), (2) student engagement (the effect), and (3) university or higher education (the context) (see protocol for synonyms).

The second round of searches was conducted in 2022–2023 in Google Scholar and ERIC, combining each of the identified clusters of educational technologies, the student engagement concept, and higher education. The reason for using ERIC in the second round was that it, besides being one of the most comprehensive educational databases, also indexes other kinds of publication types such as theses, books, and reports. The reason for using Google Scholar was to compensate for the low effectiveness associated with protocol-driven searches on standard electronic databases (Greenhalgh & Peacock, 2005 ).

1.2 Screening and selection

The identified articles in the first round were imported into EPPI reviewer and screened. Studies of the wrong document type, year, country, educational level, language, or focus were excluded (see Fig.  2 ; Table  1 ). During the screening of articles, however, it became clear that most publications before 2013 were dated and thus not applicable for students today due to contextual factors such as the technologies’ stage of development, ethical and legal perspectives such as GDPR and privacy, and students’ technological skills and competencies. The first round identified 135 relevant articles, of which 112 were included in the review.

The second round was less exclusive in terms of publication type. It included any kind of publication, including theses, books, and reports, as long as it was relevant to the research question, scientifically robust, and directly or indirectly based on empirical data. This round involved the screening of 618 publications and resulted in the inclusion of 60 additional articles and other publications.

1.3 Data coding and analysis

The articles included in the first round were coded and negotiated by three researchers according to subject area, educational level, modality, educational technology, conceptualisation of student engagement, and research question/aim. The first round revealed eight clusters of educational technologies: learning management systems, discussion forums and weblogs, audience response systems, online quizzes, social media, video, audio and multimedia, games and gamification, and virtual reality and simulation. The round also revealed that student engagement was often not explicitly defined, often used as a synonym for students’ participation in the teaching (i.e., a form of behavioural engagement), and often measured on a single indicator and that the three-perspective conceptualisation by Fredricks et al. ( 2004 ) and the related indicators identified by Bond, Bedenlier and others (Bond & Bedenlier, 2019 ; Bond et al., 2020 ) were utilised for analysing the data and classifying the engagement potential.

The included studies were subsequently revisited, discussed, and organised according to the engagement potential of the specific educational technology together with the findings phrased as recommendations in Table  2 . Studies that did not analyse a specific educational technology are only included in the discussion, and technologies addressed only by individual studies are excluded from the article due to the limited extent of evidence.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Godsk, M., Møller, K.L. Engaging students in higher education with educational technology. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12901-x

Download citation

Received : 28 February 2024

Accepted : 03 July 2024

Published : 06 August 2024

DOI : https://doi.org/10.1007/s10639-024-12901-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Student engagement
• Educational technology
• Technology-enhanced learning
• Higher education
• Recommendations for engagement
• Literature review
• Find a journal
• Publish with us
• Track your research

• Share full article

Advertisement

The Morning

Schools have a tech problem.

We explore some of the tech challenges faced by educators.

By Natasha Singer

I cover technology in schools.

As the new school year begins, school districts across the United States are cracking down on cellphones in classrooms. Teachers are tired of constantly pressing students to stop watching TikTok and messaging friends during class. In many schools, students have also used phones to threaten or bully their classmates.

As a result, as I note in a story today , at least eight states, including Indiana and Pennsylvania, have adopted measures this year to limit cellphones in schools.

But the phone crackdowns illustrate a larger issue. Technology rules and safeguards in schools often lag far behind student use and abuse of digital tools.

And it’s not just phones — school-issued laptops, tablets and classroom apps can also become sources of distraction and bullying. In today’s newsletter, I’ll highlight some of the tech challenges schools are facing.

Student cellphone bans

Schools have been trying to limit student phone use for decades. Maryland banned students from bringing pagers and “cellular telephones” to school in the late 1980s as illegal drug sales boomed. In the 1990s, as mobile phones gained traction, some schools barred the devices to stop the chirping from disrupting class.

Since the 2000s, though, it’s also gone the other way. As school shootings became more common, many districts began allowing mobile phones as a safety measure. And, after the rise of iPhones, some schools that had barred cellphones reversed the bans in part because some lower-income students who did not own laptops used them for schoolwork.

We are having trouble retrieving the article content.

Please enable JavaScript in your browser settings.

Thank you for your patience while we verify access. If you are in Reader mode please exit and  log into  your Times account, or  subscribe  for all of The Times.

Thank you for your patience while we verify access.

Already a subscriber?  Log in .

Want all of The Times?  Subscribe .