study habits in distance education effects on students' academic performance

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Relationship between study habits and academic achievement in students of medical sciences in Kermanshah-Iran

Haleh jafari.

1 Clinical Research Development Center of Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah, Iran

Abbas Aghaei

2 Social Determinants of Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran

Alireza Khatony

3 Health Institute, Social Development and Health Promotion Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran

Study habits have been the most important predictor of academic performance and play a special role in the academic achievement of students. The aim of this study was to investigate the status of study habits and its relationship with academic achievement in medical sciences students in Kermanshah-Iran.

Materials and methods

This cross-sectional study was carried out on 380 medical sciences students at Kermanshah University of Medical Sciences. The samples were randomly assigned to the study. The Palsane and Sharma study Habit Inventory was the tool used for data collection. Data were analyzed by descriptive and inferential statistics.

The mean of students’ grade point average was 15.73±1.5 out of 20 and the mean of total status of study habits was 45.70±11.36 out of 90. The status of study habits in 81.3% of the students was at moderate level. There was a direct and significant relationship between study habits and academic achievement.

The status of study habits was at moderate level for most students. Therefore, it is recommended to consider and assess students’ study habits at the time of entry into university, in addition, specific training should be offered to students in order to help them learn or modify study habits to increase their academic achievements.

Introduction

Academic performance of students is one of the main indicators used to evaluate the quality of education in universities. 1 , 2 Academic performance is a complex process that is influenced by several factors, such as study habits. 2 Study habit is different individual behavior in relation to studying 3 and is a combination of study method and skill. 4 In other words, study habits include behaviors and skills that can increase motivation and convert the study into an effective process with high returns, which ultimately increases the learning. 5 This skill is also defined as any activity that facilitates the process of learning about a topic, solving the problems or memorizing part or all of the presented materials. 3 Study habits are in fact the gateway to success and differ from person to person. 4

According to previous studies, good study habits include studying in a quite place, studying daily, turning off devices that interfere with study (such as TV and mobile phones), taking notes of important content, having regular rests and breaks, listening to soft music, studying based on own learning style, and prioritizing the difficult contents. 6 Some of the worst study habits include procrastination, evading the study, studying in inappropriate conditions, and loud sound of music and television during studying. 7

Study habits are the most important predictor of academic performance and global research has revealed that study habits affect academic performance. 8 In this regard, medical students are faced with a large amount of information that is difficult to organize and learn, and requires knowledge and application of study skills. 5 , 9 Evidence suggests that learners who do not have enough information about study strategies do not attain effective and stable learning, and therefore will not have an appropriate level of academic achievement. 3 In other words, students with better academic achievement use these skills more than those with lower academic achievement. 10

Given the important role of study skills in a student’s academic achievement, today, many prestigious universities such as York University in Canada and University of Berkeley in California teach study skills to newly-enrolled students. 11 In different studies, study skills and habits and their relationship with students’ academic achievement have been studied and different results have been reported. 1 , 3 , 12 Also, various studies have reported the study habits of students from weak to desirable levels. 5 , 7 , 10 In this regard, a study conducted on study habits of students in 21 medical universities in Iran showed that 32% of the students suffered from a severe lack of study skills and habits. 10 In many studies, a positive and significant correlation has been found between students’ study habits and their academic achievement. 4 , 6 , 7 , 10 However, in Lawrence’s study, no significant relationship was found between these two variables. 1

Considering the importance of study skills and habits of students, and the important role they play in the academic achievement of students, and taking into account that study habits vary from person to person and from place to place, and also as the results of related studies are different from each other, the present study was designed and implemented. Our goal was to investigate the relationship between study habits and academic achievement of medical sciences students in Kermanshah University of Medical Sciences (KUMS), Iran.

Study design

The present study had a descriptive-analytical and cross-sectional design and was conducted between November 2017 and April 2018.

Study questions

We sought to answer the following questions: 1) what is the status of students’ study habits in terms of variables such as; faculty, place of study, academic degree, history of probation, status of residence, and gender? 2) what is the status of students’ academic achievement in terms of variables such as; faculty, place of study, academic degree, history of probation, status of residence, and gender? and 3) what is the relationship between the status of study habits and students’ academic achievement?

Sample and sampling method

PASS/11 software was used to calculate the sample size. For this purpose, according to the results of Nourian et al's study (2011), in which the highest standard error rate was 0.96, 11 the minimum sample size was calculated to be 328 individuals with the first type error of 0.05, and the accuracy limitation of estimated mean of 1 unit. Considering the 15% probability of not responding, 380 students were enrolled in the study. The samples were selected randomly from different faculties of KUMS, which included the faculties of medicine, nursing and midwifery, dentistry, paramedicine, pharmacology, and health. The sampling classes were formed by the faculties of the university. In each class, proportional to the size of students, numbers of samples were selected randomly using a random table of numbers. Accordingly, the sample size for each faculty was as follows: medical school =130 students, dentistry =20, pharmacology =30, health =50, nursing and midwifery =50, and paramedicine =100 students. Inclusion criteria included willingness to participate in the study and studying at the second term and above. Exclusion criteria were absence on sampling day and failure to answer all questionnaire questions.

Measurement instruments

The study tools consisted of individual data collection form and the Palsane and Sharma Study Habit Inventory (PSSHI). The individual information forms included questions about age, gender, marital status, faculty of study, academic degree, history of probation, being native or non-native, and the grade point averages (GPAs) of the previous term(s).

The PSSHI is a standard tool designed by Palsane and Sharma in India (1989) 10 and its reliability is higher than that of other study habits questionnaires. 13 Validity and reliability of the original version of this questionnaire have been confirmed in previous studies. 10 , 14 , 15 Siahi and Maiyo (2015) reported the reliability coefficient of 0.88 for the PSSHI. 7 The reliability coefficient of the Persian version of this tool has also been reported as 0.88. 10 In the current study, content validity analysis was used to determine the validity of the instrument. For this purpose, the questionnaire was distributed among 12 panels of experts at KUMS. They were asked to review the questionnaire in terms of fluency, clarity, and relevance. It was then modified based on their opinions. Test-retest method was used to examine the reliability of the PSSHI. In this regard, the questionnaire was distributed among 20 students, and after a 2-week interval, they were asked to answer the questionnaire. Correlation coefficient of the pre-test and post-test scores was 0.87, which was acceptable.

PSSHI has 45 questions and measures the study habits of students in eight areas, including time management (five items), eg, “I study at a specific time of the day.”; physical conditions (six items), eg, “I get disappointed by the noise around me.”; learning motivation (six items), eg, “if I do not understand something, I get help from others.”; reading ability (eight items), eg, “before reading the intended chapter, I read its main points.”; note-taking (three items), eg, “I take notes while reading the text.”; memory (four items), eg, “I read some materials without sufficient understanding.”; taking tests (ten items), eg, “before responding to the test questions, I read all the questions first.” and health of study (three items), eg, “if the result of the test is not good, I feel disappointed.” Responses are based on a three-option Likert scale that includes: “always or most of the time”, “sometimes”, and “rarely or never” which are graded from two to zero, respectively. Questions 6, 9, 13, 15, 24, 26, 34, 36, 37, 41, and 42 are scored inversely. The score range of the questionnaire is between 0 and 90, and a score of 60 and above reflects a desirable level of study habits, a score of 31–60 indicates relatively good or moderate level of study habits, and a score of 30 or below refers to an undesirable level of study habits. The score range for each of the sub-categories is as follows: time management: 0–10; physical conditions: 0–12; learning motivation: 0–12; reading ability: 0–16; note-taking: 0–6; memory: 0–8; taking tests: 0–20, and health of study: 0–6. The achieved score for each sub-category was computed using the three-part spectrum method. To do this, the lowest score was subtracted from the highest score and the resulting number was divided by 3. The resulting number was the distance of three grades which indicates the desirable, relatively desirable, and undesirable levels of each sub-category.

To assess academic achievement, the GPA(s) of the previous term(s) was used, which in the Iranian educational system is from 0–20. For this purpose, a GPA of 17 or higher was considered as “good academic achievement”, 14–16.99 as “moderate educational achievement”, and a GPS of less than 13.99 was considered as “poor educational achievement”.

Data collection method

First, permission to conduct the study was obtained from the KUMS Deputy for Research and Technology, and was presented to the authorities of the affiliated faculties. In the next step, the list of students in each faculty was taken from the Department of Education and samples from each faculty were selected. Then, according to the classroom schedules, the selected samples were approached and after explaining the purpose of the study to them, a copy of the questionnaire was given to those who agreed to take part in the study. If any of the samples did not want to continue participating in the study, he/she was replaced by a person above or below him/her in the list. The questionnaires were collected by the researcher after completion.

Data analysis

Data were analyzed using 18th version of the Statistical Package for Social Sciences (SPSS v.18.0; SPSS Inc., Chicago, IL, USA). Descriptive and inferential statistics were used to analyze the data. At first, Kolmogorov-Smirnov test was used to assess the normality of the data, which showed that academic achievement of students did not have a normal distribution, but the rating of study habits had a normal distribution. Mann–Whitney U test was used to compare academic achievement in terms of dual-mode qualitative variables (such as gender and marital status), and Kruskal-Wallis H test was used to compare academic achievement in terms of multi-mode qualitative variables (such as academic degree and faculty of study). Pearson correlation coefficient was used to evaluate academic achievement in terms of quantitative variables. The t -test was used to compare the mean of study habits in terms of dual-mode qualitative variables (gender and marital status) and ANOVA was used to compare the mean of study habits in terms of multi-mode qualitative variables (such as academic degree and faculty of study). Pearson correlation coefficient was used to investigate the relationship between academic achievement and study habits. p -values less than 0.05 were considered as significant.

The study was approved by the Ethical Review committee of the Kermanshah University of Medical Science with code: KUMS.REC.1395.292. Objectives of the study were explained to the participants and they were assured about the confidentiality of their information and their responses. Iinformed written consent was also obtained from all participants.

Of the 380 students participating in this study, 65.3% (n=248) were male and 34.3% (n=132) were female. The mean age of the students was 22.26±2.9 years. Most of the students were single (91.1%, n=346), and had no history of probation (92.1%, n=350). The majority of the students were from faculties of medicine (34.2%, n=130) and paramedicine (26.3%, n=100). Most students were studying at doctoral (47.4%, n=180) and undergraduate (45.5%, n=173) levels. They were also mainly native students (59.2%, n=225), ( Table 1 ).

Demographic variables and comparison of the academic performance and study habits based on underlying variables

Notes: * Independent t -test; ** ANOVA; *** Mann-Whitney U test; **** kruskal-Wallis H test; † non-significant.

The mean score of students’ study habits was 45.7±11.36 out of 90. In terms of study habits, only 10% (n=38) were on a desirable level and 81.3% (n=309) were on a moderate level. Also, 8.7% (n=33) of them were on an undesirable level. In terms of the eight areas of study habits, the status of most students was undesirable in the areas of taking notes (50.2%, n=191) and well-being (48%, n=182), and was desirable in the area of time (27.3%, n=104). The status of most students in the other areas was moderate ( Table 2 ).

Frequency of subcategories of students’ study habits

The mean of students’ total GPA of the term(s) was considered as an indicator of academic achievement, which was 15.73±1.5 out of 20. The highest and lowest levels of academic achievement were respectively for the students in faculties of health and medicine with a mean and SD of 16.27±1.48 and 15.08±1.39 respectively, which showed a statistically significant difference ( p <0.001). The highest and lowest levels of academic achievement were respectively related to the MSc and doctoral students with a mean and SD of 16.98±1.61 and 15.19±1.38 respectively, which showed a statistically significant difference ( p <0.001). Academic achievement in students without history of probation was significantly higher than those with history of probation with a mean and SD of 15.86±1.42 and 14.20±1.67, respectively ( p <0.001). Female students had better academic achievement compared to male students with a mean and SD of 16.15±1.34 and ±15.5±1.5, respectively. This difference was statistically significant ( p <0.001), ( Table 1 ).

The results showed that, students of the faculty of nursing and midwifery and the faculty of dentistry had the highest and the lowest mean of study habits with mean and SD of 48.66±9.63 and 43.20±12.58 respectively, which was not statistically significant. In terms of academic degree, MSc and undergraduate students had the highest and lowest average of study habits, with a mean and SD of 57±11.34 and 44.52±11.99 respectively, which was not statistically significant. Students without history of probation had a significantly better status of study habits compared to students with probation history ( p <0.001), with a mean and SD of 46.16±11.04 and 40.33±13.69, respectively. The results showed that the status of study habits of female students was better than that of male students respectively, with a mean and SD of 46.68±9.91 and 45.18±12.05 respectively, but this difference was not statistically significant. Native students had significantly better status of study habits compared to dormitory students ( p <0.001), with a mean and SD of 46.98±10.67 and 43.58±12.23, respectively.

Pearson correlation test showed a direct and significant relationship between academic achievement and study habits (r=0.235, p <0.001).

In our study, the status of study habits of most students was at moderate level and only one tenth of the students were at the desirable level. Mendezabal (2013), in a study that investigated the study habits of 239 Filipino students, reported their study habits to be at moderate level, which indicated insufficient and ineffective study skills. 12 On the other hand, the results of a study conducted on librarian students in Iran indicated the general level of students’ study habits to be 60.5 out of 100. 5 Although the level of study habits in this study was moderate, this level was higher in our study, which may be due to the differences in the nature of medical sciences and librarian academic programs. In another study that Garner (2013) conducted on 59 undergraduate chemistry students in West Indies, the level of study habits was at desirable level in 59.2% of the students, and this level was poor in the rest. 16 The difference between the results of this study and our study could be due to the low numbers of participants in Garner’s study and the differences in the tool used to measure study habits, because the tool used in Garner’s study classified study habits into two good and poor level and eliminated the intermediate level, which might have reduced the accuracy of data and comparative capability of the study.

In our study, in terms of eight areas of study habits, the status of study habits in most students was undesirable in the areas of taking notes and well-being, and was desirable in the area of time. The status of study habits in most students in the other areas was at moderate level. Regarding the different areas of study habits, the results of studies are varied. In this regard, the result of a study conducted on 150 nursing students in Iran showed that most of the students’ problems were related to taking notes, reading ability, time management, well-being, memory, motivation, learning, physical condition, and taking tests. 22 In some studies, time management has been described as one of the major problems for medical students. 17 , 18 Mendezabal (2013) also referred to problems such as ineffective time management, lack of planning and concentration, poor study skills, and inadequate examination techniques. 12 The differences in the areas of study habits can be attributed to the individual differences between the samples and their previous educational systems.

In our study, the students in the faculties of nursing and midwifery and dentistry had the highest and the lowest mean study habits, respectively. This difference was not statistically significant. Despite the fact that this variable has not been discussed in most studies, this finding reflects the relatively similar level of study habits in the students of various medical sciences academic programs.

In our study, there was no significant difference between different educational levels in terms of the mean study habits. In other words, the level of study habits in different educational levels was equal. Our results are in line with the study of Fereydoonimoghadam and Cheraghian. 19 According to the authors of the present article, every student of medical sciences, regardless of what degree level he/she is studying at, should be aware of study skills and habits and how to apply them.

In the present study, students with no history of probation had significantly better status of study habits compared to the students with a history of probation. Despite the fact that many studies have not addressed this variable, Rezaie and Nourian in their studies, have pointed to a meaningful relationship between probation and poorer academic performance, and have considered study habits as an important factor influencing these variables. 10 , 11 In this regard, Khan (2016) described poor study habits as the most important reason for students’ academic failure. 20 In our view, students with poor academic performance, by utilizing the proper skills and study habits, can improve their academic performance and thereby prevent the emergence of educational problems, such as dropping academic unit/credits and probability of probation.

In our study, the status of study habits in male and female students did not differ from each other significantly, in other words, in terms of skills and study habits, male and female students were at the same level. Oli (2018), Hashemian (2014), and Torabi (2014) also did not find any significant difference between the students’ gender and study habits, 5 , 21 , 22 which can be due to the same educational environment for male and female students. In our view, every student, whether male or female, should be aware of study skills and habits and use them.

We found that native students had significantly better study habits compared to dormitory students. However, some studies did not report statistical significance between study habits and place of residence. 14 In our opinion, the conditions of place of residence, especially the place of study, play an important role in the study habits of students. Failure to observe the necessary standards in dormitories and the lack of suitable environment and conditions can have a negative effect on students’ performance.

We found a positive and significant correlation between academic performance and study habits, which is consistent with the results of studies by Fereydoonimoghadam and Cheraghian (2009), Alimohamadi (2018), and Rabia (2017). 13 , 19 , 23 However, Lawrence (2014) and Torabi (2014) did not find any significant statistical relationship between study habits and academic performance. 1 , 21 We believe that the utilization of study skills and habits can play a positive role in improving academic performance of students. Academic achievement and achieving educational goals require the existence of several factors, the most important of which is the study habits of individuals, 13 since the use of various and effective methods of study improves academic performance of students. Strengthening each of the eight areas of study skills can help to improve the academic performance of students, thus it is necessary to pay attention to these areas. Since academic performance is considered as a predictor of success in a person's career, it is important to pay attention to this issue and apply appropriate strategies to improve the study habits of students. Meanwhile, because of the high sensitivity of future professions in medical students, and the need for comprehensive learning of the curriculum, paying attention to the status of study habits and its promotion is critically important.

There are some limitations to this study. First, this was a cross-sectional study and according to the nature of cross-sectional studies, it is not possible to determine the causal relationships between study variables. Another limitation in this study was related to the data collection method, which was self-reporting. Despite reassuring the samples about the confidentiality of their responses, this approach might have had an impact on the accuracy of our results.

In our study, the academic performance and study habits of most students were at moderate level, which is not satisfactory considering the nature and importance of medical sciences. There was a significant relationship between study habits and academic achievement of students. Considering the important role of study habits in academic achievement and future careers of students, and since the majority of study habits can be taught and corrected, it is recommended that students’ study habits should be measured at the time of their entry to university, and during their studies, so they can receive training in order to learn or modify study habits. The present study was conducted on students of medical sciences. It is recommended that similar studies are conducted on students of other scientific fields. Conducting qualitative studies to examine the factors affecting students’ study skills and habits may also be beneficial.

Acknowledgments

This work was supported by the deputy of research and technology of KUMS (grant number 95306)]. The authors would like to thank the president and co-workers of deputy of research and technology of KUMS, and all the students who patiently participated in our study. We also extend our thanks to the clinical research development center of Imam Reza Hospital affiliated to KUMS for their kind help.

The authors report no conflicts of interest in this work.

Exploring the Factors Affecting Student Academic Performance in Online Programs: A Literature Review

• First Online: 14 September 2017

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• Fotios Misopoulos 3 ,
• Maria Argyropoulou 4 &
• Dionisia Tzavara 5  

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Online education has been receiving an increasing interest, and there are several studies focusing on student satisfaction with fully online or blended learning models. This paper has been written with a view to explore the recent developments and literature in the field of online courses and e-learning education, in general, focusing on pertinent published research. Aiming at an understanding of the factors that have an impact on student performance in the online education, a literature review of the pertinent publications has been conducted. Sixty papers have been carefully reviewed to provide a synthesis of previous and recent findings. To our understanding, this is the first systematic review focusing on student performance in the online setting, and this work will help teachers and institutions develop an understanding of what drives academic performance of online students so that they can create the appropriate e-environment for e-teaching and e-learning.

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• Published: 31 July 2024

Quality of instructor, fear of COVID-19, and students’ anxiety as predictors of student satisfaction and academic effort in online classes

• Irena Pandža Bajs   ORCID: orcid.org/0000-0002-9191-8719 1 ,
• Vanda Bazdan 2 &
• Irena Guszak 2  

Humanities and Social Sciences Communications volume  11 , Article number:  984 ( 2024 ) Cite this article

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The online form of education has been intensively used worldwide for many years and gains additional importance in emergencies such as the COVID-19 pandemic, natural disasters, and wars, when it becomes the dominant form of class delivery. Besides the recent pandemic, the world is now facing wars and the threat of their spread, making the research on the impact of fear and anxiety on human behavior relevant. The student population already faces increased depression and anxiety, which affect their behavior; therefore, it is crucial to investigate their impact on academic behavior in an online context during disruptions to the educational process. Consequently, this research focuses on analyzing student satisfaction and their academic effort and performance with online education during the COVID-19 pandemic. The empirical part of the study involves investigating the impact of instructor quality, fear of COVID and students’ anxiety on student satisfaction with online classes and students’ effort and performance at the higher education institutions in Croatia during the COVID-19 pandemic. A quantitative study was conducted on a sample of 359 respondents, students from two universities in Croatia. Results showed that the quality of instructor has a positive effect on student satisfaction and that student satisfaction positively affected students’ academic effort. Results also suggested that emotional reaction in fear of COVID-19 affected anxiety reported by students, but emotional fear reactions to COVID-19, and anxiety alike, did not affect academic effort of students, nor their satisfaction with online classes.

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Introduction.

Research in the field of higher education indicates that student satisfaction is increasingly recognized as crucial by higher education institutions. As competition in higher education intensifies, adopting a student-centric approach becomes necessary, emphasizing customer satisfaction based on basic marketing principles (Vojnić and Stojčić, 2012 ). Therefore, student satisfaction serves as an important measure of service quality, playing a significant role in achieving the vision and mission of higher education (Muhsin et al. 2019 ). In the context of online education, student satisfaction is also a central component in identifying success factors (Soffer and Nachmias, 2018 ). For higher education institutions as providers of higher education services, student satisfaction research is important as it serves as a source of information for identifying the main determinants that affect student satisfaction. These findings can later serve as guidelines for improving quality and strengthening the competitive advantage of higher education institutions.

The COVID-19 pandemic has resulted in significant changes across various aspects of life, including education. In many countries around the world safety measures were introduced in March 2020 and in-class teaching at all levels was suspended, leading higher education institutions to adopt distance learning, disrupting traditional learning methods. Consequently, assessing and enhancing the quality of online teaching necessitated a survey of student satisfaction with online education (Gačal and Zlatić, 2020 ).

In the past years research on online teaching was intense in order to analyze student satisfaction, e-learning acceptance, and differences between offline and online teaching modalities (Lee, 2010 ; Yen et al. 2018 ). Also, due to the disruption that the COVID-19 pandemic brought upon the education system and process, a significant number of studies were focused on analyzing technological and institutional aspects of online classes influencing student satisfaction and success in online classes during the COVID-19 pandemic (Butt et al. 2022 ; Gopal et al. 2021 ; Keržič et al. 2021 ). On the other side, there is scarce insight on the influence of human factors—the quality of instructor and students’ psychological state during the COVID-19 pandemic in the context of student satisfaction with online classes they attended during the COVID-19 pandemic and students’ behavior and success.

According to Gopal et al. ( 2021 ), effective instructors are pivotal to ensuring a favorable learning experience and enhancing student satisfaction in online classes. At the same time, numerous research established the deterioration of the mental well-being of students during the COVID-19 pandemic, increased occurrences of anxiety and higher need for psychological counseling of students (e.g. Azmi et al. 2022 ; Marshall and Wolanskyj‐Spinner, 2020 ). A series of authors found that the pandemic has affected all aspects of our lives, including the educational continuity and process for students (Marshall and Wolanskyj‐Spinner, 2020 ). Even before the pandemic, students around the world were susceptible to higher levels of anxiety, depression, and other psychosomatic problems (Azmi et al. 2022 ). Nowadays, situations that cause fear and a feeling of insecurity frequently arise, which contribute to the occurrence of anxiety and other psychosomatic issues. Besides the pandemic, the world is facing wars and the threat of their spread, making research that examines the impact of fear and anxiety on human behavior very important. Online learning is increasingly being used in the educational process in a regular environment and during the COVID-19 pandemic it showed its value as an alternative to in-person class delivery. Hence it should be in the focus of all deciding on online learning as well as further contingency planning on ensuring the education process continuity. Research on impact of fear and anxiety on student behavior is scarce, so it would be beneficial to explore it further. An interesting perspective is the contribution of the fear of COVID-19 to the students’ mental well-being and their academic success (Ahorsu et al. 2022 ; Kumar and Nayar, 2021 ).

The aim of this paper is to investigate the interaction of these variables: the impact of quality of instructor, students’ fear of COVID-19 and students’ psychological state on the satisfaction with online classes and academic performance of students during the COVID-19 pandemic.

Theoretical framework and hypothesis development

Theoretical framework.

The quality of the instructor in online classes is crucial for a positive learning experience and student satisfaction (Gopal et al. 2021 ). Strong communication skills are very important for online instructor, since communication during online classes has a greater impact on improving learning and student satisfaction than communication that takes place during traditional forms of classes (Lee, 2010 ). Good online instructors possess strong pedagogical skills, effectively use technology for instruction, communicate clearly, provide timely feedback, and create engaging learning environments. They adapt to the online format, foster student engagement and interaction, and support students’ academic progress through their expertise and effective teaching strategies. Due to the drastic shift of all classes to an online platform during the COVID-19 pandemic many instructors were challenged with a lack of time to prepare for online classes, a lack of teaching experience in an online environment, and insufficient technical support (Thaheem et al. 2022 ). Despite those challenges, instructors were forced to adapt quickly to the new online form of teaching, in order to ensure student satisfaction throughout the online teaching and learning process (Selvanathan et al. 2022 ). Various factors such as student attitudes, prior knowledge, elements of the online teaching process, environment, and learning outcomes have an impact on student satisfaction (Rahman et al. 2021 ). Student satisfaction influences student motivation, class attendance, attracting future students, and increasing revenue (Vranešević et al. 2007 ). For this reason, student satisfaction is a significant indicator of the quality of provided service and has a significant role in achieving the vision and mission of higher education institutions (Muhsin et al. 2019 ). Research on satisfaction by Wei and Chou ( 2020 ) concluded that student satisfaction with online education is based on multiple factors, with the most significant being instructor competence and student support during course delivery, speed of feedback, the format of online instruction, the overall functioning of the university’s support system during online education, and the syllabus or course curriculum. The multiple factors influencing student satisfaction with online learning were relevant pre-pandemic and were only more accentuated during the time of extreme circumstances. A precondition for online learning to expand further is student satisfaction.

The COVID-19 pandemic did not start the decline of mental health in the younger population. Prior to the onset of the pandemic, students across the globe have consistently encountered heightened levels of anxiety, depressive symptoms, psychosomatic issues, and a notable lack of self-confidence (Holm-Hadulla and Koutsoukou-Argyraki, 2015 ). The overnight switch of the instruction mode to exclusively online in the early 2020 only heightened that effect. While researchers confirmed an overall increase in mental disorders, in particular depression and anxiety in the general population since the pandemic onset (Hauck et al. 2022 ; Marshall and Wolanskyj‐Spinner, 2020 ), some research has pointed that the regression of psychological well-being during the COVID-19 pandemic was higher in the younger age group (Azmi et al. 2022 ).

Another factor that had a significant impact on students’ psychological state was fear. Fear is a complex construct that has a strong negative impact on the deterioration of a population’s mental health and well-being (Kumar and Nayar, 2021 ). The World Health Organization (WHO) was concerned during the pandemic over the general population mental health (WHO, 2020 ). Measures like isolation and quarantine have affected everyday activities and can lead to an increase in loneliness, anxiety, depression or other extreme behaviors (WHO, 2020 , in Kumar and Nayar, 2021 ).

Increased anxiety during the pandemic and fear of COVID-19 and its effects are expected to affect students’ success. Much connected with student satisfaction, students’ success is actually an ultimate expectation of each learning system. Alongside statements that “the student academic performance directly influences the country’s socio-economic development” (Singh et al. 2016 ), according to Narad and Abdullah ( 2016 ), the students’ academic performance even determines academic institutions’ success and failure. At the same time, academic performance is an important but very complex construct influenced by many circumstances that should be explored with multiple approaches (Vaculíková, 2018 ). Various research studies have identified diverse factors that have an impact on the students’ academic performance, including learning facilities, age, gender, communication skills, proper guidance from parents (Singh et al. 2016 ), and particularly in the online environment during the COVID-19 pandemic, quality of instructor, students’ expectation, prompt feedback, and effective course design (Gopal et al. 2021 ).

In order to measure students’ performance, some studies approached the students’ performance from the perspective of students’ perception (Gopal et al. 2021 ). However, researchers often measured the academic performance/results using the semestral grade point average (GPA) (e.g. Dokuka et al. 2020 ; Kusurkar et al. 2013 ; Yao et al. 2019 ; York et al. 2015 ). On the other side, El Ansari et al. ( 2020 ) used the importance that students attach to achieving good grades and students’ subjective comparative appraisal of their overall academic performance (in comparison with their peers) to measure the students’ performance. Another team of researchers was investigating the predictors of academic performance and distinguished between the effort that students exerted preparing for the exams and actual performance, measured with effectiveness and efficiency (Konradt et al. 2021 ). Tolken ( 2011 ) defined the actual academic behavior “as students’ self‐reported personal academic behavior”, consisting of academic effort and academic performance.

Hypothesis development

Quality of instructor and student satisfaction with online classes.

Given the psychological effect of the COVID-19 pandemic caused by physical distance, the assistance from instructor is needed in activities, which encourage interaction between the instructor and students, and students with each other. Interaction between instructor and students as one of the elements of the quality of the instructor is a key variable in online classes that significantly contributes to student satisfaction (Ali and Ahmad, 2011 ). Some researchers concluded that the aforementioned instructor-student communication is the second most powerful predictor that contributes to student satisfaction (Gray and DiLoreto, 2016 ). Instructor’s reactions and feedback during online classes are crucial, and students place great emphasis on them as such feedback shows whether they are going in the right direction (Alqurashi, 2016 ). When the instructor delivers the classes effectively and encourages the students to perform better in their studies that leads to a higher student satisfaction (Gopal et al. 2021 ). Also Gopal et al. ( 2021 ) concluded that the quality of instructor is the most important factor that influences student satisfaction with online classes.

H1: the quality of instructor during online classes during the COVID-19 pandemic has a positive effect on student satisfaction with online classes.

Student satisfaction and students’ academic effort and performance

Student satisfaction reflects how they perceive their learning experience and the quality of educational services (She et al. 2021 ). In online classes delivery, student satisfaction is seen as one of the main components in the process of identifying success factors in online classes (Soffer and Nachmias, 2018 ). So, it is very important to achieve student satisfaction in order to successfully implement online education (Butt et al. 2022 ). According to Rono ( 2013 ), the other critical element of education is student academic performance. All educational activities should be prepared and designed in order to achieve good academic performance. In their study, authors Keržič et al. ( 2021 ) have confirmed a strong relationship between students’ perception of their academic performance and their satisfaction with online learning. In addition, various studies showed a significant relationship between the student satisfaction with the learning process and the learning outcome (Fawaz and Samaha, 2021 ). As mentioned, previous research has used various approaches to describe or measure students’ success and the learning outcome (Dokuka et al. 2020 ; El Ansari et al. 2020 , Gopal et al. 2021 ). Intrigued by the concepts of effectiveness and efficiency in learning (Konradt et al. 2021 ), authors followed Tolken’s ( 2011 ) approach and explored the students’ behavior and success as academic effort and performance. Taking that into account, the influence of student satisfaction on students’ academic effort and performance was analyzed.

H2: Student satisfaction with online classes during the COVID-19 pandemic positively affects the academic effort and performance of students.

Student satisfaction as a mediator between quality of instructor and academic effort and performance

According to Gopal et al. ( 2021 ), student satisfaction partially mediates the positive relationship between the quality of instructor and students’ academic performance. That means that the quality of instructor is important for student satisfaction but also has some influence on students’ academic performance. Researches show that the quality of instructor has a significant influence on student satisfaction (Gopal et al. 2021 ; Keržič et al. 2021 ) so it would be useful to investigate if the quality of instructor affects students’ academic behavior during the COVID-19 pandemic through the student satisfaction with online classes.

H3: Student satisfaction with online classes during the COVID-19 pandemic mediates the relationship between the quality of instructor and students’ academic effort and performance (Quality of instructor affects students’ academic effort and performance during online classes during the COVID-19 pandemic through student satisfaction).

Fear of COVID-19 and students’ anxiety

During the pandemic, individuals experienced different fears, fear of getting infected, fear of infecting others, fear of interaction with other people, even fear of death, and that has impacted many of their decisions (Kumar and Nayar, 2021 ). Experiencing high levels of fear, individuals may not be able to think clearly and act rationally (Ahorsu et al. 2022 ). Recognizing that reactions to COVID-19 didn’t focus on fear of COVID-19 primarily due to a lack of an appropriate measurement instrument, Ahorsu et al. ( 2022 ) developed The Fear of COVID-19 scale (FCV-19S). They found significant positive correlations between fear of COVID-19 and depression and anxiety, applicable to males and females as well as individuals of all ages (Ahorsu et al. 2022 ). Following results of previous research, the authors set out to examine the effect of fear of COVID-19 on anxiety in students during the COVID-19 pandemic.

H4: Fear of COVID-19 positively affects the anxiety in students during the COVID-19 pandemic.

Students’ anxiety and students’ academic effort and performance

Just like for all generations, the impaired mental health of students due to the COVID-19 pandemic influenced all aspects of their lives, including learning environments and plans for the future (Marshall and Wolanskyj‐Spinner, 2020 ). According to Hadwin et al. ( 2022 ), psychological stressors related to the pandemic, such as challenges in maintaining an effective learning process and actively engaging in learning in an online environment, can weaken students’ academic performance. Approaching the students’ academic performance in terms of the academic effort and performance (Tolken, 2011 ), authors investigated the effect of anxiety on students’ academic effort and performance during the COVID-19 pandemic.

H5: Anxiety negatively affects students’ academic effort and performance during online classes during the COVID-19 pandemic.

Students’ anxiety as a mediator between fear of COVID-19 and students’ academic effort and performance

Following hypothesized relationships between fear of COVID-19 and anxiety, and between anxiety and students’ academic effort and performance, potential mediated impact of anxiety on the relationship between fear of COVID-19 and students’ academic effort and performance should be explored. Hadwin et al. ( 2022 ) also found that challenges in maintaining an effective learning process and actively engaging in learning in an online environment fully mediated the impact of COVID distress on students’ academic success. Using the FCV-19S developed by Ahorsu et al. ( 2022 ), Zolotov et al. ( 2022 ) established that a greater concern and anxiety of the respondents about the effects of COVID-19 on their university studies corresponded to a higher fear of COVID-19. Hence the following hypothesis was formulated.

H6: Anxiety mediates the relationship between fear of COVID-19 and students’ academic effort and performance during online classes during the COVID-19 pandemic (Fear of COVID-19 affects students’ academic effort and performance during online classes during the COVID-19 pandemic through anxiety).

Students’ anxiety and students’ satisfaction with online classes

Next to a regular stress associated with college life, students have experienced additional social and emotional hardships during the pandemic (Hadwin et al. 2022 ). The instant transition to online learning required students to cope with online classes, new learning platforms, equipment and internet availability, no in-person contact with instructor and peers, as well as no college social life (Azmi et al. 2022 ). Besides their academic life, they had to cope with uncertainties of the social isolation, decreased family income, future employment, their own and health of their dear ones (e.g. Aristovnik et al. 2020 ; Jiao et al. 2020 ), and they had to master it all very quickly. Impact of the pandemic was reflected in students’ increased depressive and anxious symptoms (Fruehwirth et al. 2021 ), as well as overall lower psychological well-being (Dodd et al. 2021 ). Various challenges that resulted from the COVID-19 pandemic spurred the higher need for psychological counseling of students, as noted in various research (e.g. Azmi et al. 2022 ; Marshall and Wolanskyj‐Spinner, 2020 ).

Next to all identified factors of student satisfaction with online classes during the COVID-19 pandemic, including the quality of instructor, course design, quick feedback, student expectations (Gopal et al. 2021 ), student factors, course evaluation, and system quality (Mohammed et al. 2022 ), literature indicated that personal mental health also impacts student satisfaction (Al-Nasa’h et al. 2021 ). Fawaz and Samaha ( 2021 ) found in their research about mental health of Lebanese university students during COVID-19 quarantine a highly significant negative relationship between the student satisfaction with online learning and the prevalence of depression, anxiety, and stress symptoms. Al-Nasa’h et al. ( 2021 ) found in their study that high anxiety minimized student satisfaction with online classes. Hence, authors set to test the relationship between the anxiety and student satisfaction with online classes during the COVID-19 pandemic.

H7: Anxiety negatively affects student satisfaction with online classes during the COVID-19 pandemic.

The proposed hypotheses form the model as suggested in Fig. 1 .

Proposed research model: Relationships between quality of instructor, fear of COVID-19, anxiety, student satisfaction and academic effort and performance.

Participants and data screening

This cross-sectional descriptive study was conducted in 2022, at the time when COVID-19 restrictions were still in place in Croatia. The questionnaire was administered to a total sample of students from two colleges participating in online course delivery during COVID-19 restrictions. The selection of colleges was justified for several reasons. In terms of distribution of students across various fields of study in Croatia, both colleges offer programs in the most popular field, and, in terms of sociodemographic characteristics have student population of typical gender and age distribution. To control for the potential impact of differences in delivery in public and private educational institutions colleges of otherwise congenial profiles were chosen to avoid the confounding effects which might arise from variations across a larger number of educational institutions.

The questionnaire was administered through a data collection through a data collection platform, targeting a sample size of 340, i.e., ten times the number of observed variables (cf. Bentler and Chou, 1987 ). All incomplete questionnaires were excluded from analysis (i.e., after the removal of respondents with more than 15% of missing data, the remaining questionnaires had no missing values, hence there was no need to impute missing values). Additionally, six unengaged responses were detected and excluded through data screening, resulting in a total of 359 valid questionnaires used in the analysis. The sociodemographic characteristics of participants are presented in Table 1 .

The questionnaire was administered in both English and Croatian language (depending on the language of program delivery). A sample group of representative students was consulted in the process of adjusting the questionnaire materials beforehand, to address potential issues in terms of clarity and comprehensibility. The instrument consisted of six sections. The first section included sociodemographic nominal variables. The remaining sections were five sets of empirically validated scale items adapted from previous research.

To measure ‘quality of instructor’ eight items were used from Gopal et al. ( 2021 ) study, using a five-point Likert scale. Originally, ‘quality of instructor’ scale included 7 items, and the present study incorporated an additional item on prompt feedback from the instructor, also from Gopal et al. ( 2021 ). Widely used Spielberg Trait and State Anxiety Scale was utilized to measure ‘anxiety’ trait, specifically 10 items from trait anxiety inventory (Spielberger, 1983 ) were incorporated into the present questionnaire. ‘Student satisfaction’ was assessed by using a validated 7-item scale from previous studies (Bangert, 2004 ; Gopal et al. 2021 ; Wilson et al. 1997 ). To measure ‘fear of COVID-19’ five items were used from a validated seven-item scale (Ahorsu et al. 2022 ; Bitan et al. 2020 ; Zolotov et al. 2022 ), with two items removed after pre-test and interviews with a sample group of students (see above, cf. Yang, et al. 2022 ). To measure students’ ‘academic effort and performance,’ the questionnaire originally included a four-item scale adapted from Tolken ( 2011 ). Further adjustment of the measurement instrument is explained below. The questionnaire was approved by the internal Institutional Review Board prior to administration. Scale data, including alpha coefficients in the present study, is presented in Table 2 .

Exploratory Factor Analysis [EFA] was used to assess the structure, utilizing maximum-likelihood extraction with Promax rotation (using IBM SPSS software). Upon initial analysis, given factor matrix, and content review, items with low extraction values (<0.40, ACEP 3, ACEP 4, STAI 2, STAI 7, STAI 9) were removed, as well as cross-loading items (SA3) (cf. Guvendir and Ozkan, 2022 ; Samuels, 2017 ). For ACEP items 3 and 4 content conflation of grade (result) and the actual academic behavior (putting in the effort) may have resulted in separate factor extraction with low factor loadings for each item. Upon further statistical and conceptual consideration, researchers decided to focus on the less investigated, and better suited, variable of ‘academic effort’ (retaining ACEP 1 and ACEP 2). In case of STAI, there is a history of studies revealing the drawbacks of reverse scored items, with these loading onto different factors (extensive list available in Zsido et al. 2020 , pp. 2–3). Similarly, in the present study, EFA run on STAI items yielded a two-factor solution, accounting for 50% of the total variance, with low factor loadings for reverse-scored items in factor 2. Additionally, the STAI 1 item was removed due to a low factor loading, considering all relevant statistics in EFA, and comparative α values for the 7-item and 6-item scale, as well as the α values deemed appropriate for other short-scale approaches in STAI (cf. Marteau and Bekker, 1992 ). For the cross-loading item, SA 3, ‘The online classes improved my understanding of course material’, as noted, a more stringent approach in item removal was applied to ensure scale validity, and the same approach was used in the removal of SA 5 item ‘We were generally given enough time to understand the things we had to learn’ in subsequent analysis. The results of the EFA run without the noted items (KMO = 0.902, χ 2  = 6065.16, df  = 325, p  < 0.001) confirmed the theoretically predefined five-factor structure, i.e., rotation accounted for 62.23% of the total scale variance, with five factors having an eigenvalue higher than 1. Loading of the remaining items ranged from 0.616 to 0.920.

Initial confirmatory factor analysis [CFA] used (using IBM SPSS AMOS) to estimate the validity of the measurement structures revealed an overall inadequate fit ( χ 2  = 700.996 ; df  = 289 ; p  < 0.001 ; χ 2 /df  = 2.426 ; TLI  = 0.922 ; CFI  = 0.930 ; RMSEA  = 0.063, 90% CI [0.057, 0.069], p ε 0 ≤0.05  < 0.01 ; SRMR  = 0.049, cf. cutoff criteria in Hooper et al. 2008 ). Specifications search reveled high e22-e24 modification indices, both related to the same latent variable ‘Fear of COVID’. While it would have been convenient to simply covary these errors, based on the same factor consideration, as well as the proximity of the items in the questionnaire, a more cautious approach was applied, taking the high correlation between two residuals as potential evidence of “a cause of both variables, not represented in the model” (cf. Landis et al. 2009 , as cited in Hermida, 2015 , p. 7). Although EFA of FCOV variables yields a one-factor solution, explaining 59% of the variance, and there was theoretical background guiding the use of uni-dimensional model of the scale (cf. Ahorsu et al. 2022 ), there were previous studies calling for a two-factor structure model of the scale (cf. Bitan et al. 2020 ), corresponding to the structure revealed in the present study, distinguishing between variables which measure ‘emotional fear reactions’ (FCOV1, FCOV2, FCOV4) and ‘symptomatic expressions of fear’ (FCOV3, FCOV5). The model was adjusted based on conceptual and statistical considerations. Primarily, in terms of the noted variable, the aim of the present study was to assess the impact of fear of COVID-19 on dependent variables. In this, the symptomatic expression of fear was deemed of less interest than the very emotional reaction. Additionally, a forced 6-factor solution for the measurement model yields a sixth factor loadings for ‘symptomatic expression’ fear variables, yet factor loadings scores do not average above 0.70. Hence, in further model adjustment, variables FCOV3 and FCOV5 were removed, and the latent variable was renamed into ‘Emotional fear reactions to COVID-19’.

Exploratory Factor Analysis was used to assess the structure of the new model, utilizing maximum-likelihood extraction with Promax rotation. The results of the EFA run without the noted items (KMO = 0.907, χ 2  = 5448.95, df  = 276, p  < 0.001) confirmed the theoretically predefined five-factor structure, i.e., rotation accounted for 63.14% of the total scale variance, with five factors having an eigenvalue higher than 1. Loading of the remaining items ranged from 0.615 to 0.935. Results of CFA, computed using AMOS, yielded a good fit for the data ( χ 2  = 458.649; df  = 242 ; p  < 0.001; χ 2 /df  = 1.895; TLI  = 0.953; CFI  = 0.959; RMSEA  = 0.050, 90% CI [0.043, 0.057], p ε 0≤0.05  < 0.49; SRMR  = 0.047, cf. Schermelleh-Engel and Moosbrugger, 2003 . p. 33 for not relying on significant χ 2 in assessing adequacy; Hooper et al. 2008 , p. 53, for cutoff criteria used refer to Table 5 below). Results of the Exploratory Factor Analysis are presented in Table 3 .

In terms of construct reliability, Cronbach’s alpha for each construct is above 0.70 (Nunnally and Bernstein, 1994 ), and composite reliabilities were above the 0.70 limit as well (Hair et al. 2010 ), as reported in Table 3 . Convergent validity was confirmed using average variance extracted (AVE) values, all above the benchmark 0.50 value (Fornell and Larcker, 1981 ), and discriminant validity was assessed using the Fornell and Larcker Criterion, i.e., square root of AVE for each construct is greater than its correlation with other latent constructs. Model validity measures are presented in Table 4 .

It should be noted that Cook’s distance method was used to check for outliers in the dataset, and no responses were detected above the threshold of 1, and skewness and kurtosis measures were within the threshold values (cf. Cook and Weisberg, 1982 ; Pituch and Stevens, 2016 ). Additionally, Harman’s single factor test was applied to ensure that data is free of common method bias. Covariance explained by one factor was 33.31%, thus fitting the criteria of Harman’s single-factor test (i.e., a single factor does not account for more than 50% of the variance). No multicollinearity issues were detected, with variance inflation factor values for each construct being less than 4.00 (cf. Pituch and Stevens, 2016 ; O’brien, 2007 ).

Given the adjustment of the measurement model (reduction of the latent variables measured), hypotheses were revised accordingly (Fig. 2 ):

Adjusted research model: Relationships between quality of instructor, emotional fear reactions to COVID-19, anxiety, student satisfaction and academic effort.

H1: The quality of instructor during online classes during the COVID-19 pandemic has a positive effect on student satisfaction with online classes.

H2: Student satisfaction with online classes during the COVID-19 pandemic positively affects students’ academic effort.

H3: Student satisfaction with online classes during the COVID-19 pandemic mediates the relationship between the quality of instructor and students’ academic effort. (Quality of instructor affects students’ academic effort through student satisfaction during online classes during the COVID-19 pandemic.)

H4: Emotional fear reactions to COVID-19 positively affect the anxiety in students during the COVID-19 pandemic.

H5: Anxiety negatively affects students’ academic effort during online classes during the COVID-19 pandemic.

H6 (mediation): Emotional fear reactions to COVID-19 affect students’ academic effort during online classes during the COVID-19 pandemic through anxiety. (Anxiety mediates the relationship between emotional fear reactions to COVID-19 and students’ academic effort during online classes during the COVID-19 pandemic).

Fit indices for measurement and SEM models

To assess hypothesized relationships structural equation modeling (SEM) was applied, using AMOS. Fit indices for SEM model have been found appropriate, χ 2 /df  = 1.923 ; TLI  = 0.952 ; CFI  = 0.958 ; RMSEA  = 0.051, 90% CI [0.044, 0.058], p ε0≤0.05  < 0.42 ; SRMR  = 0.062 (Table 5 ).

Hypotheses testing

The present study assessed the relationship between ‘quality of instructor’, and ‘student satisfaction’ with online class delivery and ‘academic effort’, as well as the impact of ‘emotional response to COVID-19’ (fear) and ‘anxiety’ on ‘student satisfaction’ and ‘academic effort’ of students in noted online class delivery. Results presented in Table 6 reveal that the impact of ‘quality of instructor’ on ‘satisfaction’ with online classes was positive and significant ( β  = 0.695, t  = 12.550, p  < 0.001), supporting H1. The impact of ‘satisfaction’ on ‘academic effort’ was also revealed as positive and significant ( β  = 0.373, t  = 4.711, p  < 0.001), in support of H2. As expected, the impact of ‘fear of COVID (emotional reactions)’ on ‘anxiety’ state of students was also revealed as positive and significant ( β  = 0.368, t  = 5.744, p  < 0.001), supporting hypothesis H4. Impact of ‘anxiety’ on ‘academic effort’ was negative and not statistically significant, as was the impact of ‘anxiety’ on ‘satisfaction’ with online classes, not supporting H5 and H6 (Table 6 ).

The assessed mediating role of ‘satisfaction’ (i.e., satisfaction of students with online classes) between ‘quality of instructor’ and ‘academic effort’ was found to be significant ( β  = 0.369, t  = 4.011, p  < 0.001), supporting H3. As the direct effect of ‘quality of instructor’ on ‘academic effort’ in presence of mediators is not statistically significant ( β  = 0.215, p  > 0.05), ‘satisfaction’ fully mediates the relationship between ‘quality of instructor’ and ‘academic effort’. Results suggest an insignificant indirect effect of ‘fear of COVID (emotional responses)’ through ‘anxiety’ on ‘academic effort’ ( β  = −0.017, t = −0.654, p  > 0.05), not supporting H6. Mediation analysis results are presented in Table 7 .

Discussion and conclusion

During the COVID-19 pandemic educational institutions underwent a major change in teaching practices on a global scale, through a shift from onsite to online course delivery. For most instructors this was the first engagement in online course delivery, and in a context in which heightened anxiety levels and a new kind of fear were factors expected to affect students’ effort and performance alike. The present study investigated the impact of perceived quality of instructor, students’ fear of COVID-19, and students’ anxiety on their satisfaction with online classes and their academic effort.

The findings expectedly corroborate that the quality of the instructor has a positive effect on student satisfaction [H1] (cf. Gopal et al. 2021 ; Keržič et al. 2021 ), i.e., students, who perceived classes to be valuable, and to have increased their interest in the subject matter, reported being satisfied with the quality, rated online learning as the best experience, which has fulfilled expectations when they perceived instructors as quality hires. This is consistent with earlier research results, both from the pandemic period (cf. Gopal et al. 2021 ) and before the pandemic (cf. Holm-Hadulla and Koutsoukou-Argyraki, 2015 ). Hence, regardless of the environment, higher education institutions must ensure quality instructors to ensure student satisfaction. This conclusion is straightforward for the normal environment. For future disruption situations, it is very helpful as it provides guidance to higher education institution leadership what their priorities should be, as well as in the process of risk management and contingency planning.

The findings further suggest that student satisfaction positively affected students’ academic effort [H2], namely, the extent to which students worked on coursework (tests, projects, assignments, tutorials). This confirms findings from previous studies, in which student satisfaction was found to impact student engagement in online learning (cf. Baloran et al. 2021 for impact on students’ participation, among other factors), and underlines, or potentially explains the impact of student satisfaction on students’ performance (cf. Gopal et al. 2021 ; Baloran et al. 2021 ).

Another important finding of the present study is that the quality of instructor, i.e., their effective communication, prompt feedback, enthusiasm, concern about student learning, respectfulness, how accessible they are to students, and willing to provide personalized interactions if needed, influenced students’ academic effort through student satisfaction alone [H3]. In other words, student satisfaction fully mediated the effect of rated quality of instructors on academic effort.

In investigating the interaction between instructors and students in an online learning context during the pandemic, the present study further investigated the impact of emotional fear reactions to COVID-19 and anxiety on the students’ academic effort, acknowledging that the major shift to online learning happened in a context in which such personal factors were recognized as having a prevalent impact on decisions and actions of individuals, in academia and beyond (cf. Ahorsu et al. 2022 ; Al-Nasa’h et al. 2021 ; Hadwin et al. 2022 ; Kumar and Nayar, 2021 ; Marshall and Wolanskyj‐Spinner, 2020 ; Zolotov et al. 2022 ). Expectedly, emotional fear reactions to COVID-19 affected anxiety reported by students [H4]. Interestingly, the findings of the present study suggest that emotional fear reactions to the COVID-19, and anxiety alike, did not affect the academic effort of students [H5, H6], nor their satisfaction with online classes [H7]. These findings counter the results of studies, which indicated that the mental state of students during the pandemic significantly impacted student satisfaction with online learning experiences and their academic performance (cf. Fawaz and Samaha, 2021 ; Hadwin et al. 2022 ; Tang and He, 2023 ). To explain the discrepancy in noted findings one might look at the time in which the present study was conducted, as the population adjusted to the context of the pandemic with each new wave of infection, with even at-risk individuals reporting lower levels of fear of COVID-19 at later dates (cf. Ueland, et al. 2022 ). Also, some studies found a positive correlation between age and levels of fear during the pandemic (cf. Iversen et al. 2022 ; Ueland et al. 2022 ; small, yet significant correlation among the Croatian population was noted between COVID-19 concerns and age, cf. Lauri Korajlija and Jokic-Begic, 2020 ), and there are studies reporting lesser levels of fear of COVID-19 among university students (cf. Martìnez-Lorca et al. 2020 ; cf. Wang et al. 2022 , for a comprehensive list and a meta-analysis of studies suggesting that the mean of fear of COVID-19 score was lower among university students than in the general population). Additionally, students seem to have adjusted to the online learning experience and reported being more motivated and optimistic regarding their academic experience as the pandemic progressed (cf. Cindrić, 2022 ). While results of the listed studies suggest potential explanations, a further meta-analysis is called for to understand a lack of significant effect of emotional reactions in fear of COVID-19 on academic effort in the present study. Similarly, there were studies prior and during the pandemic which failed to find significant association between anxiety levels and academic performance at the university level (e.g. Waqas, et al. 2015 ; Moreira de Sousa et al. 2018 ; Awadalla et al. 2020 , for anxiety absent other factors), with students in instances noting that anxiety inspired them to put in more effort (cf. Barbosa-Camacho et al. 2022 ). Inasmuch, the present study contributes to the debate, further outlining the complexity and the need for further study of anxiety in academic settings.

Research on student mental health is of great importance since studies indicate the issue of anxiety and depression affecting their functioning, and consequently their academic behavior (Holm-Hadulla and Koutsoukou-Argyraki, 2015 ). Accordingly, it was hypothesized that fear-inducing situations affect the onset of anxiety, which in turn influences students’ academic behavior. However, this study has shown that although the fear of a current threat affects the onset of anxiety in students, it may not have a significant impact on their satisfaction and performance related to online learning. This may be explained by the fact that, in a situation with reduced mobility and direct contact with others, the possibility of online communication helps in coping with the new, stressful situation.

Implications, limitations and further research

Online learning will continue to evolve, and the present study has practical implications for educators and managers in educational settings in contexts of risk-management circumstances as well as class activities in a non-disrupted or regular environment. The noted effect of the quality of instructor on student satisfaction, and finally, academic effort put in, strengthens, and potentially further explains the findings of studies, which link these factors to the academic performance of students (e.g., Gopal et al. 2021 ). These results could be easily used in faculty orientation and socialization practices, with emphasis placed on developing or encouraging effective communication practices, prompt feedback, enthusiasm, concern about student learning, respectfulness, importance of practices of being accessible to students, and willing to provide personalized interactions if needed, especially in an online setting (similar suggestions, with different factors noted in Baloran et al. 2021 ). As noted, students got adjusted to online learning during the pandemic (cf. Cindrić, 2022 ), yet, the present and similar studies suggest that their academic effort, engagement, and performance, were related to their satisfaction levels, inspired by the amount of effort and enthusiasm instructors contributed. Just like environment conditions, students’ abilities and needs evolve, hence continuously monitoring factors of student satisfaction in online learning is an imperative for educators. Further, longitudinal research is called for to examine the causal relationship between these factors in regular environment conditions, in the absence of pandemic-driven context, looking into the effect of other personal attributes of students and instructors which might impact the noted relationship. Better understanding of these relationships will also enable better contingency planning and preparedness for the future disruptions to the educational process, minimizing the impact on its continuity.

In terms of the measurement materials, the present study supports the two-factor structure model of the fear of COVID-19 scale (cf. Bitan et al. 2020 ; also in Reznik et al. 2021 ; Yang et al. 2022 ), and, similarly to other studies conducted among student population, revealed some of the potential difficulties in applicability of specific items of this scale for student population cross-culturally (cf. Yang et al. 2022 ). Previously noted drawbacks of reverse-scored items in the Spielberg Trait and State Anxiety scale were also supported in this study (cf. Zsido et al. 2020 ). As the present study did not detect an effect of anxiety or emotional state of students related to COVID-19 on their academic effort, further research is called for, in form of follow-up interviews and meta-analyses to attempt to explain and define factors impacting diverse results in multiple studies and settings (see above).

The present study had several limitations which should be noted. The first limitation is that, given its cross-sectional design, it does not allow for causal inference, nor does it, in itself, offer an overview of the change in examined attributes over time. Secondly, much like similar studies looking into the variables of interest during the COVID-19 pandemic, the present study is limited in scope. The questionnaire was administered to the student population only, leaving out perspective of other relevant stakeholders, which could be included in further research in pandemic-absent context (cf. Gopal et al. 2021 ). Additionally, the questionnaire was administered to student population of universities in Croatia, and regardless of the international students’ participation in the study programs investigated, the results cannot be generalized. The results can be used in further cross-cultural comparison, and meta-analysis of data gathered in congenial studies conducted during and after the COVID-19 pandemic. Thirdly, given the study design, further socio-demographic data related to, or impacting the attitudes and coping capacity within the pandemic-driven context (e.g., students’ physical health, and mental health related medical history), were not investigated.

According to Azmi et al. ( 2022 ), the levels of stress, anxiety, and depression vary among countries as each society has its own specific characteristics. Based on our empirical research, during the COVID-19 pandemic, students in Croatia did not experience a significant level of fear or anxiety, nor did these variables have a significant impact on their satisfaction with online classes and their academic behavior. Since the research was conducted only among students in Croatia, on a relatively small sample, these results can be considered indicative but also provide a significant framework for further research both in this and other markets.

Data availability

The datasets generated during the current research are available from the Harvard Dataverse, at the link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/U5WUSU . Datasets include model data for quality of instructor, fear of COVID-19, and students’ anxiety as predictors of student satisfaction and academic effort in online classes.

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Home > Books > E-Learning and Digital Education in the Twenty-First Century

The Impact of Online Learning Strategies on Students’ Academic Performance

Submitted: 01 September 2020 Reviewed: 11 October 2020 Published: 18 May 2022

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Higher education institutions have shifted from traditional face to face to online teaching due to Corona virus pandemic which has forced both teachers and students to be put in a compulsory lockdown. However the online teaching/learning constitutes a serious challenge that both university teachers and students have to face, as it necessarily requires the adoption of different new teaching/learning strategies to attain effective academic outcomes, imposing a virtual learning world which involves from the students’ part an online access to lectures and information, and on the teacher’s side the adoption of a new teaching approach to deliver the curriculum content, new means of evaluation of students’ personal skills and learning experience. This chapter explores and assesses the online teaching and learning impact on students’ academic achievement, encompassing the passing in review the adoption of students’ research strategies, the focus of the students’ main source of information viz. library online consultation and the collaboration with their peers. To reach this end, descriptive and parametric analyses are conducted in order to identify the impact of these new factors on students’ academic performance. The findings of the study shows that to what extent the students’ online learning has or has not led to any remarkable improvements in the students’ academic achievements and, whether or not, to any substantial changes in their e-learning competence. This study was carried out on a sample of University College (UAEU) students selected in Spring 2019 and Fall 2020.

• online learning environment
• content-based research
• process-based research
• success factors assessment

Author Information

Khaled hamdan *.

• UAEU-University College, UAE

Abid Amorri

*Address all correspondence to: [email protected]

1. Introduction

With the advent of COVID-19 pandemic and the shutdown of universities worldwide for fear of contamination due to the spread of the coronavirus, higher educational institutions have deemed necessary to adopt new teaching strategies, exclusively online, to deliver their curriculum content and keep from the Corona virus widespread at bay [ 1 ]. Technology was called upon to play this pivotal teaching/learning online role, as it has influenced people’s task accomplishment in various ways. It has become a part of our ever changing lives. It is an important part of e-learning to create relationship-involving technology, course content and pedagogy in learning/teaching environment. Therefore, e-learning is becoming unavoidable in a virtual teaching environment where students can take control of their learning and optimize it in a virtual classroom and elsewhere. So, learning today has shifted from the conventional face to face learning to online learning and to a direct access to information through technologies available as e-learning has proven to be more beneficial to students in terms of knowledge or information acquisition. Online teaching promotes learning by encouraging the students’ use of various learning strategies at hand and increases the level of their commitment to studying their majors. Virtual world represents an effective learning environment, providing users with an experience-based information acquisition. Instructors set up the course outcomes by creating tasks involving problem or challenge-based learning situations and offering the learner a full control of exploratory learning experiences. However, there are some challenges for instructors such as the selection of the most appropriate educational strategies and how best to design learning tasks and activities to meet learners’ needs and expectations. Various approaches can lead towards strong students’ behavioral changes especially when combined with ethical principles. However, with careful selection of the learning environment, pedagogical strategies lining up with the concrete specifics of the educational context, the building of learners’ self-confidence and their empowerment during the learning process becomes within reach. Another benefit of using online teaching/learning is that here is a need to explore new teaching strategies and principles that positively influence distance education, as traditional teaching/learning methods are becoming less effective at engaging students in the learning process. Finally, e-learning can solve many of the students’ learning issues in a conventional learning environment, as it helps them to attend classes for various reasons, as it has made the communication/interaction between them and their instructors much easier and the access to lectures much more at hand. Students can attend online university courses and at the same time meet other social obligations. Therefore, the circumstances in a learner’s life, and whatever problems or distraction he/she may have such as family problems or illnesses, may no longer be an impediment to his education. Learners can practice in virtual situations and face challenges in a safe environment, which leads to a more engaged learning experience that facilitates better knowledge acquisition.

The work presents the educational processes as a modern strategy for teaching/learning. e-learning tends to persuade the users to be virtually available to act naturally. There are a few factors affecting the outcomes such as learning aims and objectives, and different pedagogical choices. Instructors use various factors to measure the learning quality like Competence, Attitude, Content Delivery, Reliability, and Globalization [ 2 , 3 , 4 ]. In this work, we are going to pass in review positive and negative impacts of online learning followed by recommendations to increase awareness regarding online learning and the use of this new strategic technology. Modern teaching methods like brainstorming, problem solving, indirect-consultancy, and inquiry-based method have a significant effect in the educational progress [ 5 ].

The aim of this research is to examine the effect of using modern teaching methods, such as teacher-student interactive and student-centered methods, on students’ academic performance. Factors that may affect students’ performance and success- the technology used, students’ collaboration/teamwork, time management and communication skills are taken into consideration [ 6 ]. It also attempts to identify and to show to what extent online learning environment, when well integrated and adapted in course planning and objectives, can cater for students’ needs and wants. Does online teaching make a significant improvement in students’ academic performance and their personal skills such as organizations, communications, responsibilities, problem-solving tasks, engagement, learning interest, self-evolution, and abilities to reach their potential? Is students’ struggle is not purely academic, but rather related to the lack of personal skills?

2. Online learning experience

There are many motives behind the implementation of the online learning experience. The online learning is mandatory nowadays to all audience due to COVID −19 pandemic, which forced the higher educational authorities to start the online teaching [ 1 ]. We believe that we reached a tipping point where making changes to the current learning process is inevitable for many reasons. Today learners have instant access to information through technology and the web, can manage their own acquisition of knowledge through online learning. As a result, traditional teaching and learning methods are becoming less effective at engaging students, who no longer rely exclusively on the teacher as the only source of knowledge. Indeed, 90% of the respondents use internet as their major source of information. So the teacher is new role is to be a learning facilitator, a guide for his students. He should not only help his students locate information, but more importantly question it and reflect upon it and formulate an opinion about it. Another reason for the adoption of the online learning is that higher institution did not hesitate one moment to integrate it as a primary tool of education. So, it transformed the conventional course and current learning process into e-learning concept. The integration of the online teaching into the curriculum resulted in several issues to instructors, curriculum designer and administrators, starting from the infrastructure to online teaching and assessment. Does the current IT infrastructure support this integration? What course content should the instructor teach and how it should be delivered? What effective pedagogy needs to be adopted? How learning should be assessed? What is the direct effect of the online learning on students’ performance? [ 7 ].

With reference to the survey findings, the majority of students were among the staunch supporters of online learning taking into consideration the imposed COVID-19 lockdown circumstances, as they expressed their full support and confidence in computer skills to share digital content, using online learning and collaboration platforms with their peers, and expressed their satisfaction with the support of the online teaching and learning [ 8 ].

However, a small percentage of the survey respondents, expressed their below average satisfaction when higher educational institutions have invested in digital literacy and infrastructure, as they believe they should provide more flexible delivery methods, digital platforms and modernized user-friendly curricula to both students and teachers [ 9 ]. On the same lines, the higher education authorities regard the quick and unexpected development of the UAE’s higher education landscape, ICT infrastructure, and advanced online learning/teaching methods, imposed by COVID-19, have had a tremendous adverse impact on the students’ culture, thus leading to students’ social seclusion from their peers, imposing new social norms and behavior regarding plagiarism, affecting students’ cultural ethics and learning and collaboration with their peers, when adopting the digital culture [ 10 ].

A current study emphasized the need for adoption of technology in education as a way to lessen the effects of Coronavirus pandemic lockdown in education to palliate the loss of face- to- face teaching/learning which has more beneficial aspects of learning for students than online learning as it offers more interactive learning opportunities.

We recommend that all these questions should be taken into consideration when designing a new course i.e. the e-learning strategies, the learners’ and instructor’s new roles, course content and pedagogy and students’ performance/achievement assessment ( Figure 1 ). In this experience, we focus only on the implementation of new learning academic objectives- how they are infused into the curriculum and how they are assessed. The ultimate objective of implementing a new learning process is to design a curriculum conveyed by a creative pedagogy and oriented towards the cultivation of a creative person yearning for the exploration of new ideas [ 11 ]. The afore-mentioned objectives lead to design a comprehensive learning experience with new learning outcomes where instructors infuse new practical skills - Critical thinking and Problem-Solving Tasks, Creativity and Innovation, Communication and Collaboration. Other skills are implicitly infused into the curriculum such as, self-independent learning, interdependence, lifelong learning, flexibility, adaptability, and assuming academic learning responsibilities. Online learning is defined as virtual learning using mobile and wireless computing technologies in a way to promote learners’ learning abilities [ 12 ]. In ( Figure 2 ), each component of the e-learning process is defined clearly below [ 13 ].

E-learning approach.

E-learning process.

2.1 Active instructor

His role is to facilitate learning process in the virtual classroom, to engage students in the learning process, to allow them to participate in designing their own course content and to contribute to design learning assessment parameters.

2.2 Active learner

He can access course content anytime and from anywhere, engage with his peers in a collaborative environment, formulate his opinions continuously, interact with other learning communities, communicate effectively, share and publish their findings with others in online environment.

2.3 Creative pedagogy

Both instructors and learners decide on what to learn online and how it should be learned. This experience is designed to promote an inquiry and challenge-based learning models where teachers and students work together to learn about compelling issues, propose solutions to real problems and take actions [ 11 ]. The approach involves students to reflect on their learning, on the impact of their actions and to publish their solutions to a worldwide audience [ 14 ].

2.4 Flexible curriculum

A core curriculum is designed, but the facilitator has the freedom to innovate and customize course content accordingly up to the aspiration of the learners; this means that the learner’s knowledge of the material will mainly come from his own online research (formal and informal content), and from his own creativity and collaboration with his peers (teamwork).

2.5 Communities outreach

This allows a group of students to formulate real-world context research question, connect with local learning and global communities to find creative solutions to their problems, create opportunities to connect themselves with international communities. These opportunities will foster students’ social and leadership skills [ 15 ].

According to students’ observation, more than 70% of instructors found that the online learning using Blackboard ultra-collaboration boosts students’ learning interest, engagement and motivation. 84% of teachers use required to use interactive tools in order to engage students in presenting and sharing a five minutes presentation to their classmates, write a reflective essay on their experience, be involved in a collaborative project (interest- based learning project). 97% of students contributed to self and peer assessments, and 97% interacted using online management systems. Students were also encouraged to interact with their peers using blackboard group collaborate. Thanks to the online teaching strategy, 70% of students were able to deliver on time their work.

For the study purpose, several assessments components incorporate both individual and group work. For the individual work, each student was required to make an individual presentation on any subject of his own interest, write a reflective essay, self -assessment, class peer assessment, midterm and final exams. For the collaborative work, students were assigned teams and each student should contribute to the project delivered every two weeks in the form of a final presentation and a final project. Rubrics were designed and all students were well instructed to use them. Teachers were trained to monitor and facilitate the experience and the internal learning management systems such as Blackboard.

The subsequent ( Figure 3 ) shows the feedback loop of content mapping of factors and their relationships in relation to students’ performance and intake. The first feedback loop begins at the node called “Students”. The second one begins at the node entitled “Teacher”. There are two major positive feedback loops. For instance, a good team improves co-operation and creativity which increase the team’s learning experience. Setting clear goals and interactive strategies will enhance online learning and performance results. The E-learning process and the project outcomes are influenced by technology use [ 13 ].

Conceptual model of students’ E-learning environment parameters.

3. Research methodology

We studied the impact of online learning using technology in virtual classrooms and the effect of performance factors on students’ learning behavior and achievement. The study focused on a sample of 6045 students, collected from the enrolment of University College students in spring 2020, at United Arab Emirates University has used online teaching strategy in comparison to fall 2019 teaching/learning experience, which used conventional teaching strategy involving 7369 students (See Table 1 ). The study shows the learning outcomes are similar for both virtual and conventional learning, although the assessment methods are different. They include students’ learning outcomes assessment, testing (assessing prior and post knowledge acquisition) and quantitative versus conventional research. The findings of the survey are discussed below. Descriptive statistics were obtained to summarize the sample characteristics and performance variables. Pearson Correlation was used to evaluate the association between the learning outcomes dimensions. Independent Samples t-test was used to compare the mean overall performance of the online learning. Linear Regression was used to determine the impact of the learning characteristics (Critical thinking, Creativity, Communication and Collaboration) on the overall performance score. Factor Analysis was used to study the inter-relationships among the learning characteristics and compare the online methods.

Students’ population.

The objectives of the learning process consist of providing a diversified learning environment. The positive impact of this diversity is reflected in the students’ performance. Students in various represented colleges have similar passing grades as high (80–98%) for both Online Approach (OLA) and Conventional learning -Face-to-Face (FoF). The University College is the largest college in the University with more than 4000 students. Most of UAEU students start their study in UC; they take English, Arabic, IT and Math ( Figure 4 ).

University college percentage passing rate.

This study was limited to GEIL101 foundation students. Surveys were sent out to all information literacy sections at the end of the first semester 2019/2020, but there were only 87 respondents. The survey had 2 parts, one part is about students’ achievement/performance, and the second part use is about online learning in a virtual classroom. All sessions were conducted online by trained instructors in tandem with the University library delivered by professional librarians. In this report, fall 2019 students’ data are used as the sample for the study ( Table 2 ).

GEIL students.

Overall, the results indicate the online learning was beneficial for students as it shown in their academic achievements and in tables below. A significant number of students reported high comfort levels of attending online courses in virtual classroom instead of conventional learning. Results indicated students have a positive reception to online approach rather than traditional classrooms. Additionally, qualitative data identified a clearconsiderations for the integration of new technology into the new teaching and learning experience.

4. E-learning results and analyses

Table 3 shows the IL students’ pre and post tests performance. The analysis on the pre and post-tests, using the means comparison and one sample test, shows an increase of students’ performance by 84%, the mean of the pre-test is around 7.5 and the post test is 13.85, a significant difference of 6.35. 65% of students score above 60% (passing rate for the course) in the post-test, only 2.4% of students scored above 60% in the pre-test. This means that 97.6% of students did not have basic information literacy knowledge, but after going through intensive 12 week learning under e-learning conditions, 65% achieved the course outcomes with higher scores.

Students’ academic performance.

The following tables ( Tables 3 and 4 ) shows the students’ performance by each learning activity:

Students’ learning activity.

The scores in the post-test ranged between 11 and 20, whereas it ranged between 6 to 9 in the pre-test ( Figure 5 ).

Pre and post-tests comparison distribution.

The above results show that OLA students scored higher than the FoF in the majority of the learning activities. There is an important performance of online students in the midterm and final exams though both approaches where offered the similar assessments criteria under the same test conditions. In the next section, the online learning process validity, the learning activities, and the learning outcome achievements, will be discussed in greater details. Several statistical models, qualitative and quantitative analysis have been applied for this purpose.

5. Impact analysis of the learning activities

It is important for an educator to evaluate which type of learning activity that has an important impact on students’ performance. It will help the curriculum designers to adjust and improve the syllabus content accordingly. Two types of analyses are conducted quantitatively and qualitatively; the first analysis relies on the learning activities grades and course final scores. The second one relies on students’ feedback through reflective essays and teachers’ perception towards their students’ learning progress.

5.1 Quantitative analysis

5.1.1 impact of the learning activities on students’ performance.

To analyze the significance of each learning activity on students’ performance, a regression linear model was used to analyze the impact of each learning skill on students’ performance. According to the output report, the model is significant at 95% (p < 0.000), and there is a strong correlation between 95.8% of the learning skills and students’ performance (r2 = 0.919).

Overall, all learning skills strategies have a significant impact on students’ performance. Each student’s learning skills and their impact will be analyzed. The following graph shows that individual contribution has less impact on the student’s performance, but the course component is very important where students demonstrate their interaction with the course content. The quality of the students’ online participation, their assiduity and interaction with others and their contribution in the projects are different from class participation. Therefore, statistically speaking, it has a lower impact. So, it is highly recommended to review how this component is graded.

5.1.2 Impact of each learning skill on students’ achievement

The following table describes the impact of each individual learning skill on students’ performance. To do this analysis, we used Pearson Correlation Coefficient to measure the strength of the linear relationship between the learning skills. The following figure shows the relationship between the learning skills.

From the table below, the test 1 (Midterm Exam) and test 2 (Final Exam) have the strongest impact (754 and 758) respectively on the final grades, even though students scored lower in these activities compared to other assessed learning activities. They are still the most efficient assessment methods to evaluate students’ achievement. The projects, individual presentation and reflective essays have also a significant impact on students’ performance. The only learning activity with the lowest impact is the individual participation and engagement in the class, which is an important learning activity, and it needs a review on how to assess it in an effective way.

6. Teachers’ observations

Students’ e-learning performance data is processed and presented. The six characteristic attributes are identified. Each characteristic is divided into further sub-items that are rated from 1 to 5 by the respondents. Then, for each of the six main characteristics, the average of the sub-items rating is calculated. The box plot (see Figure 6 ) shows a detailed distribution of each response. This is made up of the results, comparing the responses given to the different factors affecting learning. The result shows that the teachers rating of the effect of online learning in the following table. Example: 50% of teachers think that 70% of students improved their creativity skills.

Using e-learning in the virtual classroom.

Descriptive statistics for the learning variables are shown below in Table 5 . In general, the mean and median of all the characteristics are quite high-around 3.5 ( Table 6 ). Regarding correlations between learning parameters, the results show that almost all characteristics are highly inter-correlated (p < 0.001) (See Table 7 ).

Regression model on learning skill of students’ performance.

Dependent Variable: FinalGrades.

Correlation between the learning skills on students’ academic performance.

. Correlation is significant at the 0.01 level (2-tailed).

E-learning characteristics.

Correlation is significant at the 0.05 level (2-tailed).

7. Students’ results and analysis

The survey was to collect feedback from students after they started using online learning courses. The effects of this methods on students’ learning and understanding A scale of 1–5 range from strongly agree (5) to strongly disagree (1). Different dimensions of online approach are analyzed and Eighty-seven UAE College Students coming from different Universities were asked to give their perception on different aspects of online learning methods.

For the question (1), “Do you like online learning technology?” 84 respondents representing 97.6% of the students said they do. As for the question (2), “Do you feel ready to use online environment?”, 61 students representing 71.2% said they do.

While 7 students or 8% said, they do not. Only 19 student or 21.8% were neutral (see Table 8 ).

Ready for online transformation.

As for question (3), “whether students have all the required technology tools for online learning”, 71 of the respondents representing 83.53% agreed but only 4 students disagreed (See Table 9 ).

Do students have the required tools for online learning?

Regarding the question (4), as to “whether students have reliable internet connection for online learning, 56 of the respondents representing 64% said that they agreed, while 7 students said that they disagree (See Table 10 ).

Do students have the reliable internet connection for online learning?

For question (5), “Did Online learning help your study when you have flexible schedule?” 53 students representing 63% of the respondents said it helped them because of time restriction. On the other hand, 31 students representing 37% said that time was not visible (See Table 11 ).

Did you have a flexible schedule when online learning was used?

For question (6), “Did online learning help you to be more productive?” 38 students representing 45% of the respondents said that online class helped them to be more organized and productive. On the other hand, 19 students representing 23% said that it was not productive for them (See Table 12 ).

Did online learning help you be more productive?

For question (7), “How do rate your experience with your team online” 58 students representing 60% of the respondents said that online learning class is like normal class. On the other hand, 9 students representing 10% said that they were not satisfied with online learning (See Table 13 ).

How do you rate your online experience with your team?

For question (7), “How do rate your internet connectivity and how often problems occurred?” 37 students representing 43% of the respondents said that online class runs into technical issues which lead to reduce their productivity and confidence. On the other hand, 42 students representing 48% said that there were no issues with their internet connections (See Table 14 ).

How often do you face technical problems?

For question (8), “Did you develop any health issues since the start of online learning? 41 students representing 48% of the respondents said that online class causes health issues which lead to reduce their productivity and confidence. On the other hand, 25 students representing 29% said that there were no health issues using online learning (See Table 15 ).

Did you develop any health issues since the start of online learning?

For question (9), “Rate the distractions you have had online”, 31 students representing 37% of the respondents said that online class did not face distractions. On the other hand, 23 students representing 27% said that there were not issues concerning online distraction (See Table 16 ).

Rate the distractions you have had at home.

8. Conclusion

The ultimate purpose of this investigation was to explore the impact of online learning on students’ academic achievement as the demand has increased in recent times for online courses among institutions and college students who solely rely on flexible and comfortable education. We tried to measure in quantifiable terms the students’ final academic performance after their exposure to online learning during this pandemic lockdown. The final results obtained in this study were quite self-eloquent, as they unequivocally show the tremendous impact of e- learning on students’ academic performance and achievements, as it can benefit students in many ways, including enhancing and maximizing their learning independence and classroom participation. It is a good experience for students’ transitional preparation to pursue college education and seek employment. Students were more engaged in the learning process than in conventional teaching, and online learning experience has revealed that didactic teaching style is no longer effective. They no longer regard teachers as the only source of information, but as learning facilitator and online learning from different internet sources as their main source of information. They have proved that they can assume their responsibilities, contribute to course design assessment and learning process personalization. Online learning also helped overcome time and space constraints imposed by the convention learning process and helped students to effectively communicate their findings and share their ideas with their peers locally and globally. The introduction of a new technology such as the online learning will undoubtedly have more impact on the learning outcomes only if we reconsider the delivery mode, content redesign, new assessment system. A suitable pedagogy and an appropriate content are the most important sources of students’ learning motivation. Finally, e-learning has a bright future, tremendous learning potentialities and excellent organizational culture. Universities will incontrovertibly use many of the lessons learned during this pandemic lockdown period of this forced online teaching to adjust curriculum contents, teaching methods/lesson delivery, and assessment tools.

E-learning is here to stay and can make a much stronger contribution to higher education in the years to come. However, there are some negative effects of online class as it does not offer real a face to face contact and interaction with instructors and imposes time commitment and less accountability on students. There are also many online struggles that students face such as the impossibility to stay motivated all the time, as they sometimes feel that they are completely isolated. In addition, instructors feel impotent to control students’ cheating, impose classroom discipline. In addition to that, poor students struggle to get the necessary electronic equipment to access this new mode of learning to interact in due time with their instructor, make necessary comments and raise questions to clear ambiguities and any equivocal statements and get appropriate feedback from their instructor.

There are other academic issues that need to be investigated deeply such as the perspectives of higher education quality focusing on the study of cultural, emotional, technological, ethical, health, financial or academic achievements. Furthermore, more academic research should be done about e-learning theories/distance learning to truly improvise a new and adequate teaching/learning approach.

• 1. Bao, W. (2020). COVID-19 and online teaching in higher Education: A case of Peking University. Wiley Online Library,2(2),113-115
• 2. Zheleva M., Tramonti M., “Use of the Virtual World for Educational Purposes”, in Electronic Journal for Computer Science and Communications, n. Issue 4(2), Burgas Free University, pp. 106-125, 2015
• 3. Usoro, A., & Majewski, G. (2009). Measuring Quality e-Learning in Higher Education international Journal of Global Management Studies (2), 1-32
• 4. Rossing, J. P., Miller, W. M., Cecil, A. K., & Stamper, S. E. (2012). iLearning: The Future of Higher Education? Student Perceptions on Learning with Mobile Tablets. Journal of the Scholarship of Teaching and Learning, 12(2), 1-26
• 5. MacTeer, C. F. (2011). Distance education (Ser. Education in a competitive and globalizing world). Nova Science
• 6. Nathan, S. (2020). AL-FANAR MEDIA covering Education, Research and Culture, Retrieved from https://www.al - fanarmedia.org/2020/05/future-higher-education-go-from-here /
• 7. Joshi, H. (2012). Towards Transformed Teaching: Engaging Learners Anytime, Anywhere. UAE Journal of Education Technology and Learning v3, pp. 3:5
• 8. Onyema,E. Eucheria, N Dr. Obafemi, F. , Fyneface, S. Atonye, G. Sharma, A. Alsayed, O. (2020), Impact of Coronavirus Pandemic on Education, Journal of Education and Practice, Vol.11, No.13, 2020
• 9. Aristovnik, A.(2020),How Covid-19 pandemic affected higher education students’ lives globally and in the United States
• 10. Aman, S. (2020). Flexible learning in UAE: a case for e-lessons post COVID-19 too. Gulf News
• 11. Hamdan, K., Al-Qirim, N., Asmar, M. (2012) The Effect of Smart Board on Students Behavior and Motivation, IEEE, 2012, pp. 162-166. International Conference on Innovations in Information Technology (IIT)
• 12. Carmozzino, E., Corvello, V., & Grimaldi, M. (2017). Entrepreneurial learning through online social networking in high-tech startups. International Journal of Entrepreneurial Behavior & Research, 23(3), 406-425
• 13. Hamdan,K and Asmar, M (2012), Improving Student Performance Using Interactive Smart Board Technology, Innovations 9TH International Conference in Information Technology, UAEU
• 14. O’Malley, C., Vavoula, G., Glew, J.P., Taylor, J., Sharples, M., & Lefrere, P., (2004). Guidelines for learning/teaching/tutoring in a mobile environment. [Online] Available http://www.mobilearn.org/download/results/ guidelines.pdf
• 15. Walker, A. A. (2017). Why education practitioners and stakeholders should care about person fit in educational assessments. Harvard Educational Review , 87 (3), 426-443

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Study Habits and Academic Performance among Students: A Systematic Review

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Study habits and academic performance among public junior high school pupils in the ekumfi district: investigating the controlling effect of learning styles, analysis of learning and academic performance of education students before and during the coronavirus disease pandemic, 32 references, factors affecting the student’s study habit, study habits, skills, and attitudes: the third pillar supporting collegiate academic performance, relationship among self-concept , study habits and academic achievement of prence students in zamfara state college of education , nigeria, influence of study habits on the academic performance of physics students in federal university of agriculture makurdi, nigeria, academic stress and self efficacy in relation to study habits among adolescents, a handbook of educational technology, handbook of college reading and study strategy research, impact of family climate, mental health, study habits and self confidence on the academic achievement of senior secondary students, influence of study habits on academic achievement of senior secondary school students in relation to gender and community, a study of emotional intelligence school adjustment and study habits of secondary school students in relation to their academic achievement in social science, related papers.

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Effects of Distance Learning on Students' Academic Performance and Health

Tuesday Aug. 31st, 2021

Reliance on Alternative Academic Resources

After the switch to online learning, students lost access to libraries and other school-provided academic resources. Some schools attempted to provide online resources to fill this void, but it was largely insufficient. As a result, most students had to look for alternative resources to keep up with school work. Students also had to rely on YouTube videos and other educational apps to help make the transition to distance learning more manageable.

Moreover, most students pay someone to write my paper and essays to keep up with assignments and school work in general. This has ultimately improved the performance of most students.

Lack of Physical Contact with Peers

A significant reason why students want to return to in-person classes is because of the physical interactions.  Teachers have attempted to use interactive ed-tech tools to make online classes engaging, but the fact remains that it's not a substitute for actually being surrounded by classmates.  More than 50% of students who switched to distance learning still find the lack of personal interaction with peers distressing. And this possibly affects their concentration levels and overall performance.

The Absence of Pressure to Perform

Research has shown that academic pressure is the most significant challenge students encounter nowadays, placing it above peer pressure. Given the structure of distance learning and the fact that the transition might be difficult for some students, schools have become more lenient with grading and coursework. Some schools have entirely modified their grading system to lessen the burden on pupils by implementing a 'less judgmental' system. Unfortunately, the long-term effects of this lax grading system are still unknown. 

Struggles with Mental Health

If you've heard the term "Zoom fatigue," you'll have an idea of the mental strain that online learning places on pupils. Zoom fatigue refers to the exhaustion that people experience after spending a lengthy period on video conferencing classes. Although there is no formal diagnosis, it is still a common concern for students today. Besides, sitting in front of a screen for long hours is emotionally tasking. Students experience emotions of loneliness, isolation, anxiety, and depression due to a lack of social engagement. This all goes back to that lack of physical contact with peers. And while adults can cope to some extent, most students struggle to manage with this enforced loneliness. 

Adaptation to New Learning Methods

Distance learning focuses on digital tools, most of which are relatively new to academia. Learning platforms like WolframAlpha and other virtual labs have become part of the regular curriculum. And as a result, students have to adapt to these services in a very short time. Adapting to distance learning was quite a challenge at the onset of the pandemic because it was entirely new to most students. However, few activities like orientations and seminars have helped students adapt better. Before the pandemic, most students did not have functional study spaces, but the transition to online learning has emphasized the need for one. 

Better Time Management with Self-Paced Learning

Online learning may be a new learning curve for students, but adapting to new learning methods has its advantages. Students can self-pace themselves and better manage their time, resulting in more efficient learning.  Rather than sitting in class all day, students are now taking more breaks to exercise and unwind before returning to work, which has proven highly advantageous to their grades.

Decline in Cases of Abuse and Discrimination

Some students might miss physical learning and school in general, but the switch to online learning has been a blessing to others. Students who get picked on and abused in school have had a far better time learning from home and are reluctant to return to the status quo. Although cyberbullying is on the rise, students can easily report the bullies without fear of retribution. Also, teachers can identify and punish bullies, thereby making the classroom safer for students.

Lack of Motivation to Study

One of the factors that motivate students to study is peer relationships. In every class, there's always a healthy sense of competition that drives students to perform better to get bragging rights. But the forced isolation and switch to online learning has deprived students of that extra bit of motivation. Besides, the leniency in grading has also contributed to this lack of motivation. Even the top performers no longer feel the urge to put in their best efforts in class. After all, there is no incentive to burn the midnight oil.

The long-term effects of the switch to distance learning should be a cause for concern. Although there are positives to distance learning, the well-being of students must take priority moving forward. School administrators must take measures to make sure students come out of this phase with good physical and psychological health.  As more information about the effects of online learning comes to light, parents and teachers must be proactive to mitigate the adverse effects. We should all work together to make online learning a safe and viable learning method until everything returns to normal.

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Modular Distance Learning: Its Effect in the Academic Performance of Learners in the New Normal

JETL (Journal of Education, Teaching and Learning)

Due to Covid-19 pandemic, schools, particularly in the rural areas employed Modular Distance Learning (MDL) to ensure education continuity. This study seeks to investigate the effects of MDL in the academic performance of learners whether there is a significant difference in their performance before and after the implementation of MDL. Mixed method was applied in this study; Quantitative using T-Test to compare the GWA of learners and Qualitative through the use of semi-structured interview to find out the perceived effect of MDL to 15 parents, 10 learners, and 7 teachers and their recommendations. The study revealed that the 2.25% decrease in the GWA of learners after the implementation of MDL denotes a significant difference in their academic performance. MDL strengthens family bonding, independent learning, and is cost-effective. However, it is an additional workload to working parents, there is limited teacher-learner interaction, learners lack socialization with other children ...

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Psychology and Education: A Multidisciplinary Journal

Psychology and Education , ROMEL LAGRIO

The education sector was greatly affected by the global health crisis of COVID-19, resulting in massive changes in our education setup , which contributed to various problems and challenges encountered during the implementation of the modular distance learning modality. This study aimed to determine the strategies and challenges encountered by teachers in implementing modular distance learning and its impact on students' academic performance. A descriptive research design was employed. The researchers utilized an online survey method for data gathering. A total of 60 teachers and 187 selected Grade 7 learners were the study's respondents utilizing total enumeration for teachers and stratified random sampling for learners.The study's findings show that teachers could employ strategies such as setting a submission schedule and creating a group chat with the learners. Moreover, establish the appropriate health and safety protocols and safety nets for learners against violence and abuse at home and in the community, and train school personnel for the Learning Delivery Modality (LMD).On the other hand, teachers professed that printing modules were time-consuming, the distance of the learner's home from the school hindered the teachers in providing technical assistance, and learners needed help following instructions. Parents answered the modules of the learners. The need for printing materials was a significant challenge.Most of the student's grades during the first quarter were within the range of 80-84, which was considered a satisfactory academic performance. Moreover, the results signified a negligible negative correlation between teachers' strategies in implementing modular distance learning and students' academic performance. The study suggests revisiting the school's plans for implementing modular distance learning and strengthening the partnership of the school, parents, and stakeholders.

IJMRAP Editor

During this pandemic, several schools opted for modular remote education. One of the elementary schools that selected Modular Distance Learning (MDL) as their primary mode of instruction for various reasons is Antipuluan Elementary School, a public elementary school in the Municipality of Narra, Palawan, the Philippines. However, the usage of this modality, which is unknown to many, has presented difficulties for everyone-including school staff, students, and their parents. Hence the conduct of this study. This quantitative research employed a Descriptive-Correlational Approach and involved 15 elementary teachers, 141 pupils, and 141 parents as the main data sources. A researcher-made questionnaire was used to collect data, which was then analyzed using mean, standard deviation, and Pearson product-moment correlation. The study found that the extent of Modular Distance Learning modality implementation was High, teachers', pupils', and parents' degrees of acceptance of the MDL implementation were High, and there was a strong relationship between the teachers' degree of acceptance of MDL implementation and the degree of its implementation. The perceived effects of MDL implementation have a direct relationship with the degree of their acceptance by teachers and parents.

Psychology and Education

This study investigated the limitations experienced by students, parents, and teachers in the implementation of Modular Distance Learning in Lagundi-CCL National High School during the school year 2021-2022. The researcher utilized the combination of quantitative and qualitative methods of research. An online research questionnaire utilizing Google Form was used to gather necessary information from the eighty (80) students, eighty (80) parents, and thirty-one (31) teachers who served as the respondents of this paper. Based on the results, the three major limitations experienced by students were: 1) insufficient knowledge of parents/ family members; 2) unavailability of gadgets; and 3) too many activities. In addition, parents' three major limitations were: 1) insufficient knowledge about the lessons; 2) difficulties in schedule of distribution and retrieval of modules; and 3) working parents. Furthermore, the identified limitations of teachers were: 1) too many additional tasks for teachers; 2) unavailability of self-learning modules; and 3) students who were lagging behind. From these limitations the respondents had given their suggestions. The students suggested that: 1) lessen the activities that are given to them; 2) conduct an online class even once a week; and 3) give additional time to answer the learning tasks. Meanwhile, parents' suggestions were: 1) enough information and examples in the modules should be given; 2) lessen the learning tasks; and 3) guide the parents on how to assist their children. Lastly, teachers' suggestions include: 1) proper dissemination of program, projects, and activities related to modular distance learning; 2) capacitate parents and students on MDL; and 3) distribution and retrieval should be done every other two weeks. The researchers crafted a process framework which may serve as basis in the modification of the implementation of modular distance learning which included seven (7) strategic dimensions.

International Journal of Applied Research in Social Sciences

Gerald Malabarbas

Face-to-face classes were temporarily suspended and shifted to modular print learning modality due to the COVID-19 pandemic. The study aimed to determine if there significant difference and relationship between parents’ involvement in modular distance learning and the academic performance of the Grade 6 learners in a public elementary school. The results revealed that parents’ involvement in their child&#39;s MDL varies substantially according to their educational attainment and family monthly income. Similarly, the educational attainment of parents and their family&#39;s monthly income are predictors of their children&#39;s academic achievement. Furthermore, it was disclosed that fathers were more likely than mothers to be involved in the learners&#39; MDL. The findings also revealed that there was a correlation between parents&#39; involvement in modular learning and their children&#39;s academic performance. Furthermore, the study supports prior results that parental involvement ...

Indonesian Journal of Educational Research and Review

Leomarich Casinillo

Due to the COVID-19 pandemic, schools, particularly in rural areas, employed Modular Distance Learning (MDL) to ensure educational continuity. Modular distance learning is the current learning modality of primary education, where parents serve as parent-teachers to their children. This study seeks to evaluate the experiences of students and teachers of Elementary School, on modular distance learning during the pandemic. This study used the qualitative method of interviewing nine students and six teachers to learn about their MDL experiences. Data process involves combining related concepts and themes to produce a more structured and organized picture of the data. MDL strengthens family bonding, promotes independent learning, and economizes money and time. However, it is an additional workload for working parents; there needs to be more teacher-student interaction, preventing pupils from socializing and gadget distractions. The article revealed that MDL has positive and negative experiences for teachers and students. Therefore, the impact may vary depending on individual circumstances and adaptability. The study suggests that suitable strategies should address any challenges during implementation and evaluation. Furthermore, teachers must undergo training related to MDL to address existing problems in delivering their lessons.

AJHSSR Journal

The general purpose of this study was to find out the level of extent on the parental involvement in the implementation of modular distance learning approach in Botolan District, Division of Zambales, Philippines during school year 2020-2021. The study revealed that the parent-respondent is a typical female in her early adulthood, married, high school graduate with part-time work and meagre income whose children are at primary grade level. The academic performance of the parent-respondents' children was assessed-Very Satisfactory‖. Perceived-Highly Involved‖ on Parent as a Teacher and Acceptance of the Self-Learning Module while-Involved‖ on Submission of the Self-Learning Module. There is significant difference when grouped according to highest educational attainment towards Parent as a Teacher, Acceptance and Submission of the Self-learning module respectively; significant when grouped according to family income towards Parent as Teacher and Acceptance of the Self-Learning Module; while significant on number of children studying in the elementary level towards Parent as Teacher and Submission of the Self-Learning Module respectively. There is significant difference on the perception towards dimensions on the level ofextent on the parental involvement in the implementation of modular distance learning approach. There is negatively weak or little relationship between the level of academic performance and the level ofextent on the parental involvement in the implementation of modular distance learning approach. Based on the summary of the investigations conducted and the conclusions arrived at, the researcher recommended that the parents are encouraged to be given orientation to heighten awareness on their respective limited roles in the implementation of the self-learning modular approach; that parents are encouraged to help children developed with high levels of self-directed learning, to have strong for learning.\

COVID-19 Pandemics have an impact on many aspects of life, including education. The Department of Education developed the Basic Education Learning Continuity Plan in response to the outbreak. This plan outlines learning delivery techniques such as blended learning, which is a combination of face-to-face, modular distance learning, and TV/radio-based education based on the learners' context. As a result, the DepEd permits schools to select a learning mode depending on available resources and student requirements. Thus, this study investigates the primary teachers' readiness, parental support towards modular distance learning, and its effects to the learners' performance. Based on the findings drawn from the study, the following conclusion drawn: The levels of Teachers readiness were very much ready. While in the level of parental support were much supportive. Lastly, the learner's performance was satisfactory. Moreover, it is concluded that teacher's readiness and leaners performance had no bearing with the way learners performed in class while the parental challenges and learners performance were associated with the way learners performed in class.

Psychology and Education , Maritis Magallanes Cagas , Myra A. Ambalong

Parents' engagement played an important role in parent-teacher partnership in educating the children to have a harmonious collaboration in motivating the children's learning. Due to this pandemic, parents were appreciated as facilitators in the learning process of the learners since children were not allowed to go to school for face-to-face interaction with the teacher. Modular Distance learning modalities were implemented most of the schools especially Dalamas Integrated School wherein internet connection was not available in the said area. This study aimed to assess the parents' engagement in modular distance learning and the learners' academic performance in the school year 2021-2022. The study utilized the descriptive-correlational research design. Findings revealed that the respondents' engagement in modular distance learning helped them realize that education was very important to their children and that they encouraged their children to do their homework. However, the data revealed that engagement of parents in modular distance learning did not necessarily affect the academic performance of the learners and that their engagement was not differentiated based on their socio-demographic profile.

Psychology and Education , Ehlz Marie N. Sacnanas

As educational system was hit by a global catastrophe, the introduction of modular distance learning outspread to sustain the quality of education towards the learners. To make this work, parents forced to embrace the new system of learning. With this, the parents were having a hard time on scheduling between their work and children's learning, and on facilitating the learning from home scheme. This study dug into the parents' involvement and attitude towards the modular distance learning system. The data were evaluated by interpretation and the method used in gathering data is qualitative. Content analysis allows researchers identify and analyze the correct words, topics, or concepts. The researchers conducted interviews to six parents from Badian, Cebu. The parents' involvement in this study were determined by purposive sampling technique. From the responses of the parents, the researchers developed three essential themes: (1) The Challenges, (2) The Time, (3) The Rating, (4) The Improvement, and (5) The Advantages and Disadvantages. These themes emphasized the lived experiences and battles of the parents in the distance learning system during the pandemic. The researchers were able to extract problems and meaning of consequences for parents' lived experience of MDL. Parents' talent in shaping their children's learning is not an easy job, rather it was found to be difficult. But additional colors were added to help shape it and made the children's future more worthwhile.

EPRA International Journal of Research &amp; Development (IJRD)

Emma Trovela

This research investigated the parents and learners’ perceptions on modular distance learning that they are experiencing during this time of pandemic as part contemporary new normal education setup. The main purpose of this study was to understand parents’ and learners’ perceptions on modular distance learning as contemporary teaching strategy and how they coped with the experiences and challenges of the new normal education settings. The participants of this study where five (5) senior high school learners and five (5) parents/guardians of senior high school learners of Sta. Catalina Integrated National High School. The research was conducted in Majayjay District from School Year 2020-2021. This study used Qualitative Research through Descriptive research where in-depth interviewing and storytelling was done to gather the narratives or accounts of the research participants. Using an interview protocol and with a strong collaboration with the participants, the researcher will manage...

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Students’ Engagement and Study Habits in the Online Distance Learning Environment

R.V. Caraig 1 , R.G. Fabro 2

Section:Research Paper, Product Type: Journal-Paper Vol.8 , Issue.9 , pp.25-31, Sep-2022

Online published on Sep 30, 2022

Copyright © R.V. Caraig, R.G. Fabro . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

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IEEE Style Citation: R.V. Caraig, R.G. Fabro, “Students’ Engagement and Study Habits in the Online Distance Learning Environment,” International Journal of Scientific Research in Multidisciplinary Studies , Vol.8, Issue.9, pp.25-31, 2022.

MLA Style Citation: R.V. Caraig, R.G. Fabro "Students’ Engagement and Study Habits in the Online Distance Learning Environment." International Journal of Scientific Research in Multidisciplinary Studies 8.9 (2022): 25-31.

APA Style Citation: R.V. Caraig, R.G. Fabro, (2022). Students’ Engagement and Study Habits in the Online Distance Learning Environment. International Journal of Scientific Research in Multidisciplinary Studies , 8(9), 25-31.

BibTex Style Citation: @article{Caraig_2022, author = {R.V. Caraig, R.G. Fabro}, title = {Students’ Engagement and Study Habits in the Online Distance Learning Environment}, journal = {International Journal of Scientific Research in Multidisciplinary Studies }, issue_date = {9 2022}, volume = {8}, Issue = {9}, month = {9}, year = {2022}, issn = {2347-2693}, pages = {25-31}, url = {https://www.isroset.org/journal/IJSRMS/full_paper_view.php?paper_id=2924}, publisher = {IJCSE, Indore, INDIA}, }

RIS Style Citation: TY - JOUR UR - https://www.isroset.org/journal/IJSRMS/full_paper_view.php?paper_id=2924 TI - Students’ Engagement and Study Habits in the Online Distance Learning Environment T2 - International Journal of Scientific Research in Multidisciplinary Studies AU - R.V. Caraig, R.G. Fabro PY - 2022 DA - 2022/09/30 PB - IJCSE, Indore, INDIA SP - 25-31 IS - 9 VL - 8 SN - 2347-2693 ER -

Abstract : Online Distance Learning has been the new trend in the education system worldwide since the COVID-19 spread started on 2020. The guidelines include a modified form of online learning aimed at facilitating student learning activities. This paper aims to measure the correlation between the students’ engagement and study habits in an online distance learning environment. The study facilitated the Input, Process, and Output paradigm. The aim is to answer the following questions, (1) what is the students’ engagement in Online Distance Learning Environment? (2) what are the Students’ study habits in Online Distance Learning Environment? and (3) What is the Correlation between Students’ Engagement and Study Habits in Online Distance Learning Environment? This study covers the undergraduate students from Diliman College as the primary respondents of the study. It is focused on assessing the respondents’ engagements and study habits in an online distance learning environment. It also tackled how their profile can affect their study habits. This study used a quantitative design and facilitated correlational descriptive survey research methods. It uses surveys to gather data on the two variables. These data aim to know the correlation between students’ engagement and study habits in an online distance learning environment. It involves 51 undergraduate students from different academic programs. Pearson-r value is 0.65, indicating a Moderate Uphill (positive) Correlation between students’ engagement and students study habits. Moderate Uphill (positive) Correlation means a high correlation between the two variables. This only means that the respondents are incorporating their engagement in their study habits even if the mode of modality is online.

Key-Words / Index Term : Online Distance Learning, CHED Philippines, Synchronous Class, Students’ Engagement, Study Habits

References : .[1] Ginbert Cuaton, “Philippines higher education institutions in the time of COVID-19 pandemic,” Revista Româneasc? pentru Educa?ie Multidimensional?, Vol.12, issue.1, pp.61-70, 2020. [2] Ryan Michael Oducado, “Faculty perception toward online education in a state college in the Philippines during the coronavirus disease 19 (COVID-19) pandemic,” Universal Journal of Educational Research, Vol.8, issue.10, pp.4736-4742, 2020. [3] Aris Alea Lapada, Miguel Frosyl Fabrea, Dave Arthur Roldan Robledo, Zeba Farooqi Alam, “Teachers` Covid-19 awareness, distance learning education experiences and perceptions towards institutional readiness and challenges,” International Journal of Learning, Teaching and Educational Research, Vol.19, issue.6, pp.127-144, 2020. [4] Gil Parentela, Danilo Varga, “Pandemic Era (COVID-19) and Higher Education in the Philippines against the World Perspective: A Literature Survey Analysis,” Social Science Research Network, 2021. [5] Cathy Mae Toquero, “Challenges and opportunities for higher education amid the COVID-19 pandemic: The Philippine context,” Pedagogical Research, Vol.5, issue.4, pp.1-5, 2020. [6] Miguel Fernandez Alvarez, Amanda Montes, “Student engagement in the online classroom: The student perspective,” Medios digitales y metodologías docentes: Mejorar la educación desde un abordaje integral, Vol.8, issue.10, pp.226-233, 2021. [7] Jose Tria, “The COVID-19 pandemic through the lens of education in the Philippines: The new normal,” International Journal of Pedagogical Development and Lifelong Learning, Vol.1, issue.1, pp.2-4, 2020. [8] Micaiah Andrea Gumasing Lopez, Christian Dave Francisco, Cristalyn Capinig, Jhoremy Alayan, “Amidst COVID-19 Pandemic: The Self-Efficacy and Academic Motivation of the College Students from the Private Higher Education Institutions in the Philippines,” International Journal of Advance Research And Innovative Ideas In Education. Vol.7, issue.3, pp.2230-2241, 2021. [9] Marcia Dixson, “Creating effective student engagement in online courses: What do students find engaging,” Journal of the Scholarship of Teaching and Learning, Vol.10, issue.2, pp.1-13, 2010. [10] Noel Entwistle, Jennifer Thomson, “Motivation and study habits,” The International Journal of Higher Education Research, Vol.3, issue.4, pp.379-396, 1974. [11] Jhoselle Tus, “The Influence of Study Attitudes and Study Habits on Academic Performance of the Students,” International Journal of All Research Writings, Vol.2, issue.4, pp.11-32, 2020. [12] Perante Wenceslao, Ediza Gomba Felisa, “Challenges to online engineering education during the Covid-19 pandemic in Eastern Visayas, Philippines,” International Journal of Learning, Teaching and Educational Research, Vol.20, issue.3, pp.84-96, 2021. [13] Rhona Sharpe, Greg Benfield, “The student experience of e-learning in higher education,” Brookes eJournal of Learning and Teaching, Vol.1, issue.3, pp.1-9, 2005. [14] Chin Choo Robinson, Hallett Hullinger, “New benchmarks in higher education: Student engagement in online learning,” Journal of Education for Business, Vol.84, issue.2, pp.101-109, 2008. [15] Bruce Ratner B, “The correlation coefficient: Its values range between + 1 / ? 1,” Journal of Targeting, Measurement, and Analysis for Marketing, Vol.17 issue.1 pp.142, 2019. [16] Abisola Oladeni Sakirudeen, Kudirat Bimbo Sanni, “Study habits and academic performance of secondary school students in mathematics: A case study of selected secondary schools in Uyo local education council,” Research in Pedagogy, Vol.7, issue.2, pp.283-297, 2017. [17] Jean Mandernach, Amber Dailey-Hebert, “Assessing course student engagement,” Promoting student engagement, Vol.1, pp.277-281, 2011. [18] Shanna Smith Jaggars, Di Xu, “How do online course design features influence student performance,” Computers & Education, Vol.95, pp.270-284, 2016. [19] Mitchell Handelsman, William Briggs, Nora Sullivan, Annette Towler, “A measure of college student course engagement,” The Journal of Educational Research, Vol.98, issue.3, pp.184-192, 2010.

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A new paper from Carnegie Mellon University indicates that giving students more autonomy leads to better attendance and improved performance. The research was published in the journal Science Advances .

In one experiment, students were given the choice to make their own attendance mandatory. Contradicting common faculty beliefs, 90% of students in the initial study chose to do so, committing themselves to attending class reliably or to having their final grades docked. Under this "optional-mandatory attendance" policy, students came to class more reliably than students whose attendance had been mandated.

Student choice in learning

The pattern has held true. In additional studies across five classes that included 60–200 students, 73%–95% opted for mandatory attendance, and at most 10% regretted their decision by the semester's end.

"Like Ulysses, students know they will face significant temptations. By making their attendance mandatory, they exercise self-control over their future behavior," said first author Simon Cullen, assistant professor in the Department of Philosophy and Dietrich College AI and Education Fellow.

"We are born curious, and we naturally enjoy mastering many challenging learning tasks, but controlling course policies like mandatory attendance can undermine that motivation."

The role of autonomy in academic success

According to Cullen, the findings challenge widely held beliefs about student behavior. He continues that many educators worry that given the choice, students would opt for the easiest path possible. However, this study paints a starkly different picture.

"Anytime in a class that you give freedom to choose, you give students the feeling of control over their education," said Danny Oppenheimer, professor in the Social and Decision Sciences and Psychology departments at CMU and co-author of the article. "It puts the learning in the students' hands and increases their motivation."

Preparing students for real-world challenges

A second experiment indicated that when given the option to switch to an easier homework stream at any time before midterms, 85%–90% of students chose to tackle the more challenging work. The "optional-mandatory homework" policy led students to spend more time on their assignments and to learn more over the semester compared to students who were compelled to complete the same work. Cullen gauged the improved understanding of the material by examining how well students did on the problem sets throughout the semester.

These findings suggest that the common practice of imposing strict rules on college students may be counterproductive. Cullen and Oppenheimer found that allowing students more autonomy could lead to better academic outcomes and prepare them more effectively for the real world.

"The thought was that giving them greater control over their own learning would prepare them for the real world," Cullen said. "Students can be driven to excel in our classes by the same sources of motivation that drive them to pursue countless projects and passions that require no external incentives. But only if we let them choose to learn."

Enhancing Engagement and Retention in Higher Education

The researchers note their findings also highlight a significant gap in current educational practices. Despite decades of research demonstrating the importance of autonomy to motivation, autonomy-promoting policies remain rare in higher education.

"It's as if we've been ignoring one of the most powerful tools in our educational toolkit," said Oppenheimer. "By harnessing students' intrinsic motivation to learn through increased autonomy, we achieve better results than through external pressure."

The researchers caution that their findings, while promising, have limitations. The study was conducted at a single university with a limited number of students, and more research is needed to determine if the results will replicate across different types of institutions and student populations. The authors are collaborating with a diverse set of institutions to test its broader applicability.

"We're super excited about these results, but we're also eager to see how our interventions work across a range of settings," Cullen said. "We're particularly interested in exploring how autonomy might benefit students from disadvantaged backgrounds and those with disabilities."

The study opens up new avenues for research and practical applications in higher education. The authors suggest that similar choice architectures could be applied to other aspects of college courses, such as deadlines, course materials and even exam formats.

"As colleges and universities grapple with issues of student engagement, retention and academic success, this research offers a fresh perspective," said Cullen. "By trusting students with more control over their education, institutions might not only improve academic outcomes but also foster a more positive and empowering learning environment."

Journal information: Science Advances

Provided by Carnegie Mellon University

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How the Education Department Wants to Police Online Education

The department says it needs more data about online education to hold those programs accountable. Institutions say the agency is overcorrecting.

By  Katherine Knott

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More than half of U.S. students took at least one online class in the 2022–23 academic year. The Education Department is proposing a number of changes to gather more data about how students fare in those courses.

Alena Butusava/Getty Images

The Education Department wants to collect much more information about distance education courses and the students enrolled in them as part of a broader effort to increase oversight of online programs.

The department’s proposal would require colleges and universities to take attendance in distance education classes, which include those offered online or via correspondence. Institutions also would have to provide more information to the agency about those classes’ enrollment. Additionally, the department proposes to end any asynchronous options for students in online clock-hour programs, which are typically workforce training programs that lead to a certificate.

The proposed changes worry some higher education groups, which say they could hamper innovation, unfairly target online classes and limit access for students who could benefit from the flexibility that online education provides. The department and advocates say the new regulations are needed to ensure oversight of online education—which increased during and following the pandemic—and track the outcomes of students in those programs. In the 2022–23 academic year, about 53 percent of U.S. students enrolled in at least one online course.

Edward Conroy, a senior policy manager at New America, a left-leaning think tank, said the additional data will shed light on whether the programs are effective—and for which students.

“Schools should want this information, because if it’s not proving to be effective, then we need to find ways to improve it,” he said. “I don’t think online education is going away, and so if it’s going to be part of our lives, then we need to make it good.”

The proposals are part of a package of draft regulations that also include provisions to open up a federal college-prep program to undocumented students. The regulations were posted on the Federal Register last week and are open for public comments until Aug. 23. If they are finalized and issued before Nov. 1, they would take effect by July 1 of next year.

With this package and other regulatory changes still in the works , the Biden administration is aiming to better protect students and give them greater control over how their financial aid is used, while increasing oversight regarding colleges. Critics say the changes reflect the Education Department’s growing skepticism of the quality of online education and whether these programs pay off for students.

Jordan DiMaggio, vice president of policy and digital strategy at UPCEA, the online and professional education association, said that the department’s goals are laudable, but this proposal and other actions raise questions about the agency’s motivations.

“There’s questions on whether the department is truly focused on protecting students’ outcomes and taxpayer dollars,” he said. “Or do they kind of reveal an antiquated bias against online education that’s framed by some suspicion and distrust of the field as a whole?”

He added that the department’s rationale for some of the changes seems to be rooted in the assumption that online education is bad—and is drawing from data from the early days of the pandemic, when universities quickly switched to remote instruction.

“It sort of feels like using last month’s weather forecast to plan today’s outfit,” he said. “We’re looking at the worst of the worst in a time when [some] institutions had no idea how to teach online … We’re in a vastly different place.”

What the Department Wants to Change

The department says it’s simply trying to ensure that students are getting what they pay for with distance education programs. The various changes will help the department “better measure and account for student outcomes, improve oversight over distance education and ensure students are receiving effective education,” according to the proposed regulations. One big change: Colleges would be required to create a virtual location to house all their programs that are offered entirely online or through correspondence, which would not have to be approved by accreditors or state officials. ( Note: This paragraph originally stated that accreditors and state officials have to approve new virtual locations, and has been corrected to reflect that they do not. )

In 2022–23, a little over 3,700 institutions of higher education offered at least one distance education course. But current federal reporting requirements don’t distinguish between on-campus programs and those offered online or in a hybrid format. The department also can’t tell how much federal financial aid is going specifically to distance education programs. To address that information gap, the department is proposing new reporting requirements related to distance education enrollment along with the virtual location.

The reporting requirements would require colleges to break down whether students enrolled in a distance education course are fully online or hybrid, though the specific details have yet to be determined.

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Next, all distance education courses will have to take attendance as part of a proposal to more accurately determine when a student withdraws from a program, except for doctoral dissertation research courses. That withdrawal date is key to calculating how much federal financial aid should be returned to the government by either the institution or the student. The department says the proposal will help students better pay down any balance owed after they withdraw while simplifying the calculation for institutions.

DiMaggio and others said that implementing the attendance requirement will be complicated and likely require more systematic changes to institutions’ learning management systems and other software. The department is underestimating the difficulty institutions will face in complying, they say.

The department expects an institution to spend about 10 hours to initially implement the attendance requirement and then about 10 minutes a day to capture the necessary information for their records. The agency estimates that about half of the institutions offering distance education courses are already taking attendance.

“Institutions can often easily determine when students stop attending because a school’s systems can often identify when students submit assignments or interact with instructors and students during lectures and course discussions,” officials wrote.

DiMaggio said he doesn’t think that’s the case. “And many of our institutions have indicated to us that that’s not the case,” he added.

Another key change in this package rolls back a 2020 rule change that allowed asynchronous learning activities—such as watching a prerecorded video—to count toward the required number of clock hours in a distance education course. Clock-hour programs tend to be shorter term and career focused, requiring hands-on instruction to prepare students for employment in a certain field.

The 2020 change “puts students and taxpayers at risk,” officials wrote in the proposed rule, citing its oversight and compliance activities. Officials added that “asynchronous learning in clock-hour programs has often consisted of playing videos, reading assignments or scrolling through pages,” which results in a “substandard education” for students. Additionally, students have told the agency that a lack of direct engagement with instructors “hampered their ability to obtain the skills necessary to pass certification exams or obtain a job in their field.”

The department believes that “very few institutions” would be affected, though it doesn’t have data about how many programs include asynchronous elements.

Conroy of New America said that the changes to the distance education regulations reflect the “huge shift in how people go through higher education.” That includes more students enrolling in a mix of in-person and online classes.

“If that’s going to be a big part of how higher education is delivered, we need to know what’s happening with it, and we need to be able to provide students who enroll solely online with similar or the same protections if something goes sideways, as we do for students who enroll in person,” he said.

‘Needs to Be a Better Solution’

Critics of the proposal say that the department is making unnecessary and sweeping decisions in response to some bad practices, particularly when it comes to the changes to asynchronous learning activities in clock-hour programs.

“They’re right that there’s some really bad practices out there, but they’ve also said themselves that there are institutions that have spent a lot of money and spent a lot of time and effort in order to make sure that they’re right,” said Russell Poulin, executive director of the Western Interstate Commission for Higher Education’s Cooperative for Educational Technologies, or WCET. “There needs to be a better solution than this one.”

Poulin and others at WCET say the proposed changes will make it more complicated for institutions to offer distance education rather than simplifying processes. For example, complying with the attendance requirement is more complicated than just “counting noses.” For distance education, it’s not just a question of whether a student showed up or logged in but also whether they participated in the class. That would have to be determined by the faculty member reviewing a student’s file, they said, and measures of academic engagement could vary depending on how the class is structured.

“There are loads of little processes that get put into this that’s far from simplification,” Poulin said.

Emmanual Guillory, senior director of government relations at the American Council on Education, said that eliminating asynchronous instruction in the clock-hour programs could hinder students considered nontraditional, such as parents.

“Because they can do it at their own pace,” he said. “They’re working two or three jobs. They’re trying to support their families in whatever ways, and they don’t have the luxury to have a carved-out time every week to go sit in the classroom with their peers and learn. What you’re doing is you are limiting the ability of these students to access postsecondary education by using student aid funding, and this could have a huge impact on low-income students.”

Guillory added that the other changes, from the attendance requirement to the virtual location, will likely mean that colleges—some of which are already underresourced—will have to expend more resources and manpower to comply.

“It just adds more stress and burden upon the men and women on our campuses that are really trying to best produce quality academic programming, ensure teaching and learning on campuses, and it’s just more red tape that they have to deal with,” he said.

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A Study on Study Habits and Academic Performance of Students

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