dissertation about academic achievement

ORIGINAL RESEARCH article

The importance of students’ motivation for their academic achievement – replicating and extending previous findings.

• 1 Department of Psychology, TU Dortmund University, Dortmund, Germany
• 2 Department of Psychology, Philipps-Universität Marburg, Marburg, Germany
• 3 Department of Psychology, Heidelberg University, Heidelberg, Germany

Achievement motivation is not a single construct but rather subsumes a variety of different constructs like ability self-concepts, task values, goals, and achievement motives. The few existing studies that investigated diverse motivational constructs as predictors of school students’ academic achievement above and beyond students’ cognitive abilities and prior achievement showed that most motivational constructs predicted academic achievement beyond intelligence and that students’ ability self-concepts and task values are more powerful in predicting their achievement than goals and achievement motives. The aim of the present study was to investigate whether the reported previous findings can be replicated when ability self-concepts, task values, goals, and achievement motives are all assessed at the same level of specificity as the achievement criteria (e.g., hope for success in math and math grades). The sample comprised 345 11th and 12th grade students ( M = 17.48 years old, SD = 1.06) from the highest academic track (Gymnasium) in Germany. Students self-reported their ability self-concepts, task values, goal orientations, and achievement motives in math, German, and school in general. Additionally, we assessed their intelligence and their current and prior Grade point average and grades in math and German. Relative weight analyses revealed that domain-specific ability self-concept, motives, task values and learning goals but not performance goals explained a significant amount of variance in grades above all other predictors of which ability self-concept was the strongest predictor. Results are discussed with respect to their implications for investigating motivational constructs with different theoretical foundation.

Introduction

Achievement motivation energizes and directs behavior toward achievement and therefore is known to be an important determinant of academic success (e.g., Robbins et al., 2004 ; Hattie, 2009 ; Plante et al., 2013 ; Wigfield et al., 2016 ). Achievement motivation is not a single construct but rather subsumes a variety of different constructs like motivational beliefs, task values, goals, and achievement motives (see Murphy and Alexander, 2000 ; Wigfield and Cambria, 2010 ; Wigfield et al., 2016 ). Nevertheless, there is still a limited number of studies, that investigated (1) diverse motivational constructs in relation to students’ academic achievement in one sample and (2) additionally considered students’ cognitive abilities and their prior achievement ( Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ). Because students’ cognitive abilities and their prior achievement are among the best single predictors of academic success (e.g., Kuncel et al., 2004 ; Hailikari et al., 2007 ), it is necessary to include them in the analyses when evaluating the importance of motivational factors for students’ achievement. Steinmayr and Spinath (2009) did so and revealed that students’ domain-specific ability self-concepts followed by domain-specific task values were the best predictors of students’ math and German grades compared to students’ goals and achievement motives. However, a flaw of their study is that they did not assess all motivational constructs at the same level of specificity as the achievement criteria. For example, achievement motives were measured on a domain-general level (e.g., “Difficult problems appeal to me”), whereas students’ achievement as well as motivational beliefs and task values were assessed domain-specifically (e.g., math grades, math self-concept, math task values). The importance of students’ achievement motives for math and German grades might have been underestimated because the specificity levels of predictor and criterion variables did not match (e.g., Ajzen and Fishbein, 1977 ; Baranik et al., 2010 ). The aim of the present study was to investigate whether the seminal findings by Steinmayr and Spinath (2009) will hold when motivational beliefs, task values, goals, and achievement motives are all assessed at the same level of specificity as the achievement criteria. This is an important question with respect to motivation theory and future research in this field. Moreover, based on the findings it might be possible to better judge which kind of motivation should especially be fostered in school to improve achievement. This is important information for interventions aiming at enhancing students’ motivation in school.

Theoretical Relations Between Achievement Motivation and Academic Achievement

We take a social-cognitive approach to motivation (see also Pintrich et al., 1993 ; Elliot and Church, 1997 ; Wigfield and Cambria, 2010 ). This approach emphasizes the important role of students’ beliefs and their interpretations of actual events, as well as the role of the achievement context for motivational dynamics (see Weiner, 1992 ; Pintrich et al., 1993 ; Wigfield and Cambria, 2010 ). Social cognitive models of achievement motivation (e.g., expectancy-value theory by Eccles and Wigfield, 2002 ; hierarchical model of achievement motivation by Elliot and Church, 1997 ) comprise a variety of motivation constructs that can be organized in two broad categories (see Pintrich et al., 1993 , p. 176): students’ “beliefs about their capability to perform a task,” also called expectancy components (e.g., ability self-concepts, self-efficacy), and their “motivational beliefs about their reasons for choosing to do a task,” also called value components (e.g., task values, goals). The literature on motivation constructs from these categories is extensive (see Wigfield and Cambria, 2010 ). In this article, we focus on selected constructs, namely students’ ability self-concepts (from the category “expectancy components of motivation”), and their task values and goal orientations (from the category “value components of motivation”).

According to the social cognitive perspective, students’ motivation is relatively situation or context specific (see Pintrich et al., 1993 ). To gain a comprehensive picture of the relation between students’ motivation and their academic achievement, we additionally take into account a traditional personality model of motivation, the theory of the achievement motive ( McClelland et al., 1953 ), according to which students’ motivation is conceptualized as a relatively stable trait. Thus, we consider the achievement motives hope for success and fear of failure besides students’ ability self-concepts, their task values, and goal orientations in this article. In the following, we describe the motivation constructs in more detail.

Students’ ability self-concepts are defined as cognitive representations of their ability level ( Marsh, 1990 ; Wigfield et al., 2016 ). Ability self-concepts have been shown to be domain-specific from the early school years on (e.g., Wigfield et al., 1997 ). Consequently, they are frequently assessed with regard to a certain domain (e.g., with regard to school in general vs. with regard to math).

In the present article, task values are defined in the sense of the expectancy-value model by Eccles et al. (1983) and Eccles and Wigfield (2002) . According to the expectancy-value model there are three task values that should be positively associated with achievement, namely intrinsic values, utility value, and personal importance ( Eccles and Wigfield, 1995 ). Because task values are domain-specific from the early school years on (e.g., Eccles et al., 1993 ; Eccles and Wigfield, 1995 ), they are also assessed with reference to specific subjects (e.g., “How much do you like math?”) or on a more general level with regard to school in general (e.g., “How much do you like going to school?”).

Students’ goal orientations are broader cognitive orientations that students have toward their learning and they reflect the reasons for doing a task (see Dweck and Leggett, 1988 ). Therefore, they fall in the broad category of “value components of motivation.” Initially, researchers distinguished between learning and performance goals when describing goal orientations ( Nicholls, 1984 ; Dweck and Leggett, 1988 ). Learning goals (“task involvement” or “mastery goals”) describe people’s willingness to improve their skills, learn new things, and develop their competence, whereas performance goals (“ego involvement”) focus on demonstrating one’s higher competence and hiding one’s incompetence relative to others (e.g., Elliot and McGregor, 2001 ). Performance goals were later further subdivided into performance-approach (striving to demonstrate competence) and performance-avoidance goals (striving to avoid looking incompetent, e.g., Elliot and Church, 1997 ; Middleton and Midgley, 1997 ). Some researchers have included work avoidance as another component of achievement goals (e.g., Nicholls, 1984 ; Harackiewicz et al., 1997 ). Work avoidance refers to the goal of investing as little effort as possible ( Kumar and Jagacinski, 2011 ). Goal orientations can be assessed in reference to specific subjects (e.g., math) or on a more general level (e.g., in reference to school in general).

McClelland et al. (1953) distinguish the achievement motives hope for success (i.e., positive emotions and the belief that one can succeed) and fear of failure (i.e., negative emotions and the fear that the achievement situation is out of one’s depth). According to McClelland’s definition, need for achievement is measured by describing affective experiences or associations such as fear or joy in achievement situations. Achievement motives are conceptualized as being relatively stable over time. Consequently, need for achievement is theorized to be domain-general and, thus, usually assessed without referring to a certain domain or situation (e.g., Steinmayr and Spinath, 2009 ). However, Sparfeldt and Rost (2011) demonstrated that operationalizing achievement motives subject-specifically is psychometrically useful and results in better criterion validities compared with a domain-general operationalization.

Empirical Evidence on the Relative Importance of Achievement Motivation Constructs for Academic Achievement

A myriad of single studies (e.g., Linnenbrink-Garcia et al., 2018 ; Muenks et al., 2018 ; Steinmayr et al., 2018 ) and several meta-analyses (e.g., Robbins et al., 2004 ; Möller et al., 2009 ; Hulleman et al., 2010 ; Huang, 2011 ) support the hypothesis of social cognitive motivation models that students’ motivational beliefs are significantly related to their academic achievement. However, to judge the relative importance of motivation constructs for academic achievement, studies need (1) to investigate diverse motivational constructs in one sample and (2) to consider students’ cognitive abilities and their prior achievement, too, because the latter are among the best single predictors of academic success (e.g., Kuncel et al., 2004 ; Hailikari et al., 2007 ). For effective educational policy and school reform, it is crucial to obtain robust empirical evidence for whether various motivational constructs can explain variance in school performance over and above intelligence and prior achievement. Without including the latter constructs, we might overestimate the importance of motivation for achievement. Providing evidence that students’ achievement motivation is incrementally valid in predicting their academic achievement beyond their intelligence or prior achievement would emphasize the necessity of designing appropriate interventions for improving students’ school-related motivation.

There are several studies that included expectancy and value components of motivation as predictors of students’ academic achievement (grades or test scores) and additionally considered students’ prior achievement ( Marsh et al., 2005 ; Steinmayr et al., 2018 , Study 1) or their intelligence ( Spinath et al., 2006 ; Lotz et al., 2018 ; Schneider et al., 2018 ; Steinmayr et al., 2018 , Study 2, Weber et al., 2013 ). However, only few studies considered intelligence and prior achievement together with more than two motivational constructs as predictors of school students’ achievement ( Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ). Kriegbaum et al. (2015) examined two expectancy components (i.e., ability self-concept and self-efficacy) and eight value components (i.e., interest, enjoyment, usefulness, learning goals, performance-approach, performance-avoidance goals, and work avoidance) in the domain of math. Steinmayr and Spinath (2009) investigated the role of an expectancy component (i.e., ability self-concept), five value components (i.e., task values, learning goals, performance-approach, performance-avoidance goals, and work avoidance), and students’ achievement motives (i.e., hope for success, fear of failure, and need for achievement) for students’ grades in math and German and their GPA. Both studies used relative weights analyses to compare the predictive power of all variables simultaneously while taking into account multicollinearity of the predictors ( Johnson and LeBreton, 2004 ; Tonidandel and LeBreton, 2011 ). Findings showed that – after controlling for differences in students‘ intelligence and their prior achievement – expectancy components (ability self-concept, self-efficacy) were the best motivational predictors of achievement followed by task values (i.e., intrinsic/enjoyment, attainment, and utility), need for achievement and learning goals ( Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ). However, Steinmayr and Spinath (2009) who investigated the relations in three different domains did not assess all motivational constructs on the same level of specificity as the achievement criteria. More precisely, students’ achievement as well as motivational beliefs and task values were assessed domain-specifically (e.g., math grades, math self-concept, math task values), whereas students’ goals were only measured for school in general (e.g., “In school it is important for me to learn as much as possible”) and students’ achievement motives were only measured on a domain-general level (e.g., “Difficult problems appeal to me”). Thus, the importance of goals and achievement motives for math and German grades might have been underestimated because the specificity levels of predictor and criterion variables did not match (e.g., Ajzen and Fishbein, 1977 ; Baranik et al., 2010 ). Assessing students’ goals and their achievement motives with reference to a specific subject might result in higher associations with domain-specific achievement criteria (see Sparfeldt and Rost, 2011 ).

Taken together, although previous work underlines the important roles of expectancy and value components of motivation for school students’ academic achievement, hitherto, we know little about the relative importance of expectancy components, task values, goals, and achievement motives in different domains when all of them are assessed at the same level of specificity as the achievement criteria (e.g., achievement motives in math → math grades; ability self-concept for school → GPA).

The Present Research

The goal of the present study was to examine the relative importance of several of the most important achievement motivation constructs in predicting school students’ achievement. We substantially extend previous work in this field by considering (1) diverse motivational constructs, (2) students’ intelligence and their prior achievement as achievement predictors in one sample, and (3) by assessing all predictors on the same level of specificity as the achievement criteria. Moreover, we investigated the relations in three different domains: school in general, math, and German. Because there is no study that assessed students’ goal orientations and achievement motives besides their ability self-concept and task values on the same level of specificity as the achievement criteria, we could not derive any specific hypotheses on the relative importance of these constructs, but instead investigated the following research question (RQ):

RQ. What is the relative importance of students’ domain-specific ability self-concepts, task values, goal orientations, and achievement motives for their grades in the respective domain when including all of them, students’ intelligence and prior achievement simultaneously in the analytic models?

Materials and Methods

Participants and procedure.

A sample of 345 students was recruited from two German schools attending the highest academic track (Gymnasium). Only 11th graders participated at one school, whereas 11th and 12th graders participated at the other. Students of the different grades and schools did not differ significantly on any of the assessed measures. Students represented the typical population of this type of school in Germany; that is, the majority was Caucasian and came from medium to high socioeconomic status homes. At the time of testing, students were on average 17.48 years old ( SD = 1.06). As is typical for this kind of school, the sample comprised more girls ( n = 200) than boys ( n = 145). We verify that the study is in accordance with established ethical guidelines. Approval by an ethics committee was not required as per the institution’s guidelines and applicable regulations in the federal state where the study was conducted. Participation was voluntarily and no deception took place. Before testing, we received written informed consent forms from the students and from the parents of the students who were under the age of 18 on the day of the testing. If students did not want to participate, they could spend the testing time in their teacher’s room with an extra assignment. All students agreed to participate. Testing took place during regular classes in schools in 2013. Tests were administered by trained research assistants and lasted about 2.5 h. Students filled in the achievement motivation questionnaires first, and the intelligence test was administered afterward. Before the intelligence test, there was a short break.

Ability Self-Concept

Students’ ability self-concepts were assessed with four items per domain ( Schöne et al., 2002 ). Students indicated on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree) how good they thought they were at different activities in school in general, math, and German (“I am good at school in general/math/German,” “It is easy to for me to learn in school in general/math/German,” “In school in general/math/German, I know a lot,” and “Most assignments in school/math/German are easy for me”). Internal consistency (Cronbach’s α) of the ability self-concept scale was high in school in general, in math, and in German (0.82 ≤ α ≤ 0.95; see Table 1 ).

Table 1. Means ( M ), Standard Deviations ( SD ), and Reliabilities (α) for all measures.

Task Values

Students’ task values were assessed with an established German scale (SESSW; Subjective scholastic value scale; Steinmayr and Spinath, 2010 ). The measure is an adaptation of items used by Eccles and Wigfield (1995) in different studies. It assesses intrinsic values, utility, and personal importance with three items each. Students indicated on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree) how much they valued school in general, math, and German (Intrinsic values: “I like school/math/German,” “I enjoy doing things in school/math/German,” and “I find school in general/math/German interesting”; Utility: “How useful is what you learn in school/math/German in general?,” “School/math/German will be useful in my future,” “The things I learn in school/math/German will be of use in my future life”; Personal importance: “Being good at school/math/German is important to me,” “To be good at school/math/German means a lot to me,” “Attainment in school/math/German is important to me”). Internal consistency of the values scale was high in all domains (0.90 ≤ α ≤ 0.93; see Table 1 ).

Goal Orientations

Students’ goal orientations were assessed with an established German self-report measure (SELLMO; Scales for measuring learning and achievement motivation; Spinath et al., 2002 ). In accordance with Sparfeldt et al. (2007) , we assessed goal orientations with regard to different domains: school in general, math, and German. In each domain, we used the SELLMO to assess students’ learning goals, performance-avoidance goals, and work avoidance with eight items each and their performance-approach goals with seven items. Students’ answered the items on a 5-point scale ranging from 1 (totally disagree) to 5 (totally agree). All items except for the work avoidance items are printed in Spinath and Steinmayr (2012) , p. 1148). A sample item to assess work avoidance is: “In school/math/German, it is important to me to do as little work as possible.” Internal consistency of the learning goals scale was high in all domains (0.83 ≤ α ≤ 0.88). The same was true for performance-approach goals (0.85 ≤ α ≤ 0.88), performance-avoidance goals (α = 0.89), and work avoidance (0.91 ≤ α ≤ 0.92; see Table 1 ).

Achievement Motives

Achievement motives were assessed with the Achievement Motives Scale (AMS; Gjesme and Nygard, 1970 ; Göttert and Kuhl, 1980 ). In the present study, we used a short form measuring “hope for success” and “fear of failure” with the seven items per subscale that showed the highest factor loadings. Both subscales were assessed in three domains: school in general, math, and German. Students’ answered all items on a 4-point scale ranging from 1 (does not apply at all) to 4 (fully applies). An example hope for success item is “In school/math/German, difficult problems appeal to me,” and an example fear of failure item is “In school/math/German, matters that are slightly difficult disconcert me.” Internal consistencies of hope for success and fear of failure scales were high in all domains (hope for success: 0.88 ≤ α ≤ 0.92; fear of failure: 0.90 ≤ α ≤ 0.91; see Table 1 ).

Intelligence

Intelligence was measured with the basic module of the Intelligence Structure Test 2000 R, a well-established German multifactor intelligence measure (I-S-T 2000 R; Amthauer et al., 2001 ). The basic module of the test offers assessments of domain-specific intelligence for verbal, numeric, and figural abilities as well as an overall intelligence score (a composite of the three facets). The overall intelligence score is thought to measure reasoning as a higher order factor of intelligence and can be interpreted as a measure of general intelligence, g . Its construct validity has been demonstrated in several studies ( Amthauer et al., 2001 ; Steinmayr and Amelang, 2006 ). In the present study, we used the scores that were closest to the domains we investigated: overall intelligence, numerical intelligence, and verbal intelligence (see also Steinmayr and Spinath, 2009 ). Raw values could range from 0 to 60 for verbal and numerical intelligence, and from 0 to 180 for overall intelligence. Internal consistencies of all intelligence scales were high (0.71 ≤ α ≤ 0.90; see Table 1 ).

Academic Achievement

For all students, the school delivered the report cards that the students received 3 months before testing (t0) and 4 months after testing (t2), at the end of the term in which testing took place. We assessed students’ grades in German and math as well as their overall grade point average (GPA) as criteria for school performance. GPA was computed as the mean of all available grades, not including grades in the nonacademic domains Sports and Music/Art as they did not correlate with the other grades. Grades ranged from 1 to 6, and were recoded so that higher numbers represented better performance.

Statistical Analyses

We conducted relative weight analyses to predict students’ academic achievement separately in math, German, and school in general. The relative weight analysis is a statistical procedure that enables to determine the relative importance of each predictor in a multiple regression analysis (“relative weight”) and to take adequately into account the multicollinearity of the different motivational constructs (for details, see Johnson and LeBreton, 2004 ; Tonidandel and LeBreton, 2011 ). Basically, it uses a variable transformation approach to create a new set of predictors that are orthogonal to one another (i.e., uncorrelated). Then, the criterion is regressed on these new orthogonal predictors, and the resulting standardized regression coefficients can be used because they no longer suffer from the deleterious effects of multicollinearity. These standardized regression weights are then transformed back into the metric of the original predictors. The rescaled relative weight of a predictor can easily be transformed into the percentage of variance that is uniquely explained by this predictor when dividing the relative weight of the specific predictor by the total variance explained by all predictors in the regression model ( R 2 ). We performed the relative weight analyses in three steps. In Model 1, we included the different achievement motivation variables assessed in the respective domain in the analyses. In Model 2, we entered intelligence into the analyses in addition to the achievement motivation variables. In Model 3, we included prior school performance indicated by grades measured before testing in addition to all of the motivation variables and intelligence. For all three steps, we tested for whether all relative weight factors differed significantly from each other (see Johnson, 2004 ) to determine which motivational construct was most important in predicting academic achievement (RQ).

Descriptive Statistics and Intercorrelations

Table 1 shows means, standard deviations, and reliabilities. Tables 2 –4 show the correlations between all scales in school in general, in math, and in German. Of particular relevance here, are the correlations between the motivational constructs and students’ school grades. In all three domains (i.e., school in general/math/German), out of all motivational predictor variables, students’ ability self-concepts showed the strongest associations with subsequent grades ( r = 0.53/0.61/0.46; see Tables 2 –4 ). Except for students’ performance-avoidance goals (−0.04 ≤ r ≤ 0.07, p > 0.05), the other motivational constructs were also significantly related to school grades. Most of the respective correlations were evenly dispersed around a moderate effect size of | r | = 0.30.

Table 2. Intercorrelations between all variables in school in general.

Table 3. Intercorrelations between all variables in math.

Table 4. Intercorrelations between all variables in German.

Relative Weight Analyses

Table 5 presents the results of the relative weight analyses. In Model 1 (only motivational variables) and Model 2 (motivation and intelligence), respectively, the overall explained variance was highest for math grades ( R 2 = 0.42 and R 2 = 0.42, respectively) followed by GPA ( R 2 = 0.30 and R 2 = 0.34, respectively) and grades in German ( R 2 = 0.26 and R 2 = 0.28, respectively). When prior school grades were additionally considered (Model 3) the largest amount of variance was explained in students’ GPA ( R 2 = 0.73), followed by grades in German ( R 2 = 0.59) and math ( R 2 = 0.57). In the following, we will describe the results of Model 3 for each domain in more detail.

Table 5. Relative weights and percentages of explained criterion variance (%) for all motivational constructs (Model 1) plus intelligence (Model 2) plus prior school achievement (Model 3).

Beginning with the prediction of students’ GPA: In Model 3, students’ prior GPA explained more variance in subsequent GPA than all other predictor variables (68%). Students’ ability self-concept explained significantly less variance than prior GPA but still more than all other predictors that we considered (14%). The relative weights of students’ intelligence (5%), task values (2%), hope for success (4%), and fear of failure (3%) did not differ significantly from each other but were still significantly different from zero ( p < 0.05). The relative weights of students’ goal orientations were not significant in Model 3.

Turning to math grades: The findings of the relative weight analyses for the prediction of math grades differed slightly from the prediction of GPA. In Model 3, the relative weights of numerical intelligence (2%) and performance-approach goals (2%) in math were no longer different from zero ( p > 0.05); in Model 2 they were. Prior math grades explained the largest share of the unique variance in subsequent math grades (45%), followed by math self-concept (19%). The relative weights of students’ math task values (9%), learning goals (5%), work avoidance (7%), and hope for success (6%) did not differ significantly from each other. Students’ fear of failure in math explained the smallest amount of unique variance in their math grades (4%) but the relative weight of students’ fear of failure did not differ significantly from that of students’ hope for success, work avoidance, and learning goals. The relative weights of students’ performance-avoidance goals were not significant in Model 3.

Turning to German grades: In Model 3, students’ prior grade in German was the strongest predictor (64%), followed by German self-concept (10%). Students’ fear of failure in German (6%), their verbal intelligence (4%), task values (4%), learning goals (4%), and hope for success (4%) explained less variance in German grades and did not differ significantly from each other but were significantly different from zero ( p < 0.05). The relative weights of students’ performance goals and work avoidance were not significant in Model 3.

In the present studies, we aimed to investigate the relative importance of several achievement motivation constructs in predicting students’ academic achievement. We sought to overcome the limitations of previous research in this field by (1) considering several theoretically and empirically distinct motivational constructs, (2) students’ intelligence, and their prior achievement, and (3) by assessing all predictors at the same level of specificity as the achievement criteria. We applied sophisticated statistical procedures to investigate the relations in three different domains, namely school in general, math, and German.

Relative Importance of Achievement Motivation Constructs for Academic Achievement

Out of the motivational predictor variables, students’ ability self-concepts explained the largest amount of variance in their academic achievement across all sets of analyses and across all investigated domains. Even when intelligence and prior grades were controlled for, students’ ability self-concepts accounted for at least 10% of the variance in the criterion. The relative superiority of ability self-perceptions is in line with the available literature on this topic (e.g., Steinmayr and Spinath, 2009 ; Kriegbaum et al., 2015 ; Steinmayr et al., 2018 ) and with numerous studies that have investigated the relations between students’ self-concept and their achievement (e.g., Möller et al., 2009 ; Huang, 2011 ). Ability self-concepts showed even higher relative weights than the corresponding intelligence scores. Whereas some previous studies have suggested that self-concepts and intelligence are at least equally important when predicting students’ grades (e.g., Steinmayr and Spinath, 2009 ; Weber et al., 2013 ; Schneider et al., 2018 ), our findings indicate that it might be even more important to believe in own school-related abilities than to possess outstanding cognitive capacities to achieve good grades (see also Lotz et al., 2018 ). Such a conclusion was supported by the fact that we examined the relative importance of all predictor variables across three domains and at the same levels of specificity, thus maximizing criterion-related validity (see Baranik et al., 2010 ). This procedure represents a particular strength of our study and sets it apart from previous studies in the field (e.g., Steinmayr and Spinath, 2009 ). Alternatively, our findings could be attributed to the sample we investigated at least to some degree. The students examined in the present study were selected for the academic track in Germany, and this makes them rather homogeneous in their cognitive abilities. It is therefore plausible to assume that the restricted variance in intelligence scores decreased the respective criterion validities.

When all variables were assessed at the same level of specificity, the achievement motives hope for success and fear of failure were the second and third best motivational predictors of academic achievement and more important than in the study by Steinmayr and Spinath (2009) . This result underlines the original conceptualization of achievement motives as broad personal tendencies that energize approach or avoidance behavior across different contexts and situations ( Elliot, 2006 ). However, the explanatory power of achievement motives was higher in the more specific domains of math and German, thereby also supporting the suggestion made by Sparfeldt and Rost (2011) to conceptualize achievement motives more domain-specifically. Conceptually, achievement motives and ability self-concepts are closely related. Individuals who believe in their ability to succeed often show greater hope for success than fear of failure and vice versa ( Brunstein and Heckhausen, 2008 ). It is thus not surprising that the two constructs showed similar stability in their relative effects on academic achievement across the three investigated domains. Concerning the specific mechanisms through which students’ achievement motives and ability self-concepts affect their achievement, it seems that they elicit positive or negative valences in students, and these valences in turn serve as simple but meaningful triggers of (un)successful school-related behavior. The large and consistent effects for students’ ability self-concept and their hope for success in our study support recommendations from positive psychology that individuals think positively about the future and regularly provide affirmation to themselves by reminding themselves of their positive attributes ( Seligman and Csikszentmihalyi, 2000 ). Future studies could investigate mediation processes. Theoretically, it would make sense that achievement motives defined as broad personal tendencies affect academic achievement via expectancy beliefs like ability self-concepts (e.g., expectancy-value theory by Eccles and Wigfield, 2002 ; see also, Atkinson, 1957 ).

Although task values and learning goals did not contribute much toward explaining the variance in GPA, these two constructs became even more important for explaining variance in math and German grades. As Elliot (2006) pointed out in his hierarchical model of approach-avoidance motivation, achievement motives serve as basic motivational principles that energize behavior. However, they do not guide the precise direction of the energized behavior. Instead, goals and task values are commonly recruited to strategically guide this basic motivation toward concrete aims that address the underlying desire or concern. Our results are consistent with Elliot’s (2006) suggestions. Whereas basic achievement motives are equally important at abstract and specific achievement levels, task values and learning goals release their full explanatory power with increasing context-specificity as they affect students’ concrete actions in a given school subject. At this level of abstraction, task values and learning goals compete with more extrinsic forms of motivation, such as performance goals. Contrary to several studies in achievement-goal research, we did not demonstrate the importance of either performance-approach or performance-avoidance goals for academic achievement.

Whereas students’ ability self-concept showed a high relative importance above and beyond intelligence, with few exceptions, each of the remaining motivation constructs explained less than 5% of the variance in students’ academic achievement in the full model including intelligence measures. One might argue that the high relative importance of students’ ability self-concept is not surprising because students’ ability self-concepts more strongly depend on prior grades than the other motivation constructs. Prior grades represent performance feedback and enable achievement comparisons that are seen as the main determinants of students’ ability self-concepts (see Skaalvik and Skaalvik, 2002 ). However, we included students’ prior grades in the analyses and students’ ability self-concepts still were the most powerful predictors of academic achievement out of the achievement motivation constructs that were considered. It is thus reasonable to conclude that the high relative importance of students’ subjective beliefs about their abilities is not only due to the overlap of this believes with prior achievement.

Limitations and Suggestions for Further Research

Our study confirms and extends the extant work on the power of students’ ability self-concept net of other important motivation variables even when important methodological aspects are considered. Strength of the study is the simultaneous investigation of different achievement motivation constructs in different academic domains. Nevertheless, we restricted the range of motivation constructs to ability self-concepts, task values, goal orientations, and achievement motives. It might be interesting to replicate the findings with other motivation constructs such as academic self-efficacy ( Pajares, 2003 ), individual interest ( Renninger and Hidi, 2011 ), or autonomous versus controlled forms of motivation ( Ryan and Deci, 2000 ). However, these constructs are conceptually and/or empirically very closely related to the motivation constructs we considered (e.g., Eccles and Wigfield, 1995 ; Marsh et al., 2018 ). Thus, it might well be the case that we would find very similar results for self-efficacy instead of ability self-concept as one example.

A second limitation is that we only focused on linear relations between motivation and achievement using a variable-centered approach. Studies that considered different motivation constructs and used person-centered approaches revealed that motivation factors interact with each other and that there are different profiles of motivation that are differently related to students’ achievement (e.g., Conley, 2012 ; Schwinger et al., 2016 ). An important avenue for future studies on students’ motivation is to further investigate these interactions in different academic domains.

Another limitation that might suggest a potential avenue for future research is the fact that we used only grades as an indicator of academic achievement. Although, grades are of high practical relevance for the students, they do not necessarily indicate how much students have learned, how much they know and how creative they are in the respective domain (e.g., Walton and Spencer, 2009 ). Moreover, there is empirical evidence that the prediction of academic achievement differs according to the particular criterion that is chosen (e.g., Lotz et al., 2018 ). Using standardized test performance instead of grades might lead to different results.

Our study is also limited to 11th and 12th graders attending the highest academic track in Germany. More balanced samples are needed to generalize the findings. A recent study ( Ben-Eliyahu, 2019 ) that investigated the relations between different motivational constructs (i.e., goal orientations, expectancies, and task values) and self-regulated learning in university students revealed higher relations for gifted students than for typical students. This finding indicates that relations between different aspects of motivation might differ between academically selected samples and unselected samples.

Finally, despite the advantages of relative weight analyses, this procedure also has some shortcomings. Most important, it is based on manifest variables. Thus, differences in criterion validity might be due in part to differences in measurement error. However, we are not aware of a latent procedure that is comparable to relative weight analyses. It might be one goal for methodological research to overcome this shortcoming.

We conducted the present research to identify how different aspects of students’ motivation uniquely contribute to differences in students’ achievement. Our study demonstrated the relative importance of students’ ability self-concepts, their task values, learning goals, and achievement motives for students’ grades in different academic subjects above and beyond intelligence and prior achievement. Findings thus broaden our knowledge on the role of students’ motivation for academic achievement. Students’ ability self-concept turned out to be the most important motivational predictor of students’ grades above and beyond differences in their intelligence and prior grades, even when all predictors were assessed domain-specifically. Out of two students with similar intelligence scores, same prior achievement, and similar task values, goals and achievement motives in a domain, the student with a higher domain-specific ability self-concept will receive better school grades in the respective domain. Therefore, there is strong evidence that believing in own competencies is advantageous with respect to academic achievement. This finding shows once again that it is a promising approach to implement validated interventions aiming at enhancing students’ domain-specific ability-beliefs in school (see also Muenks et al., 2017 ; Steinmayr et al., 2018 ).

Data Availability

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

In Germany, institutional approval was not required by default at the time the study was conducted. That is, why we cannot provide a formal approval by the institutional ethics committee. We verify that the study is in accordance with established ethical guidelines. Participation was voluntarily and no deception took place. Before testing, we received informed consent forms from the parents of the students who were under the age of 18 on the day of the testing. If students did not want to participate, they could spend the testing time in their teacher’s room with an extra assignment. All students agreed to participate. We included this information also in the manuscript.

Author Contributions

RS conceived and supervised the study, curated the data, performed the formal analysis, investigated the results, developed the methodology, administered the project, and wrote, reviewed, and edited the manuscript. AW wrote, reviewed, and edited the manuscript. MS performed the formal analysis, and wrote, reviewed, and edited the manuscript. BS conceived the study, and wrote, reviewed, and edited the manuscript.

We acknowledge financial support by Deutsche Forschungsgemeinschaft and Technische Universität Dortmund/TU Dortmund University within the funding programme Open Access Publishing.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : academic achievement, ability self-concept, task values, goals, achievement motives, intelligence, relative weight analysis

Citation: Steinmayr R, Weidinger AF, Schwinger M and Spinath B (2019) The Importance of Students’ Motivation for Their Academic Achievement – Replicating and Extending Previous Findings. Front. Psychol. 10:1730. doi: 10.3389/fpsyg.2019.01730

Received: 05 April 2019; Accepted: 11 July 2019; Published: 31 July 2019.

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Copyright © 2019 Steinmayr, Weidinger, Schwinger and Spinath. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ricarda Steinmayr, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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A Study of Graduate Students’ Achievement Motivation, Active Learning, and Active Confidence Based on Relevant Research

Jen-chia chang.

1 Graduate Institute of Technological and Vocational Education, National Taipei University of Technology, Taipei City, Taiwan

2 Office of Physical Education, Soochow University, Taipei City, Taiwan

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Graduate students’ failure to graduate is of great concern, with the failure to graduate due to the dissertation being the most influential factor. However, there are many factors that influence the writing of a dissertation, and research on these factors that influence graduate students’ learning through emotion and cognition is still quite rare. A review of past research revealed that the main factor causing graduate students to drop out midway is not completing their thesis, followed by factors including insufficient achievement motivation, lack of learning strategy, and low confidence. The graduation rate of graduate students has been emphasized by the academic community; therefore, this study investigated the correlation between graduate students’ achievement motivation, active learning, and academic confidence in writing research. The study invited graduated students from two universities of science and technology situated in the northern region of Taiwan to complete the questionnaire. In this study, valid data for validation analysis were collected from 173 respondents, and the results showed that achievement motivation positively influenced active learning (higher-order learning, integrative learning, reflective learning) and that active learning (higher-order learning, integrative learning, reflective learning) positively influenced academic confidence. From the above findings, it can be seen that to help graduate students from University of Science and Technology to effectively complete their graduate studies, students should develop good motivation to adopt active learning strategies to enhance their academic self-confidence.

Introduction

In Taiwan, the delayed graduation of graduate students has become an important educational issue of social concern ( Ho et al., 2020 ). Gardner (2009) found that the reasons for the low graduation rate of doctoral students include being unable to complete their degree theses, among others. The completion of the degree thesis is an important milestone and the biggest obstacle for graduate students ( Blum, 2010 ). Muszynski (1990) found that graduate students who fail to graduate in time may be uninterested in the research topic, have low academic confidence, or have too many research papers to complete. Spaulding and Rockinson-Szapkiw (2012) interviewed 76 doctoral graduates and found that motivation, persistence factors, and completion strategies were necessary to complete their dissertations.

However, Pulford et al. (2018) found that people must have motivation before they are willing to put in the effort and persevere. Usta (2017) noted that there are significant mutual influences among an individual’s learning achievement, motivation, and confidence. Belshaw et al. (2020) found that learners who use passive learning not only have lower learning efficiency and less comprehension, but their comprehension level is also low. It has also been found that learners with higher levels of active learning are more willing to engage in learning and gain more knowledge from it, thereby reducing delayed graduation ( Schmidt et al., 2009 ). Wehrens (2008) suggested that learners with low confidence or feelings of inferiority may have limited learning progress, drop out of school, or have other problems. Academic confidence refers to the student’s belief in the learning task and in achieving the learning goal ( Sander and Sander, 2005 ), which reflects the student’s beliefs and expectations for success in the academic field. Students also prefer to perform activities or tasks in which they feel competent ( Bandura, 1977 ; Eccles et al., 1989 ).

In addition, when students are able to succeed academically, it can also be attributed to their motivation and willingness to put in the effort, which in turn brings motivation for learning and academic confidence, and encourages them to take action and persevere ( Pulford et al., 2018 ). Chemers et al. (2001) found that students’ lack of academic confidence is inextricably linked to their expectation of success and has a significant effect on academic performance, as academic confidence controls students’ desire to learn. If students’ academic confidence is low, it will affect their desire to learn. If they do not have sufficient academic confidence, they may not be eager to learn and may not continue their studies ( Ireson and Hallam, 2009 ). Sander and de la Fuente (2020) found that academic confidence helps students to effectively acquire learning strategies and skills. In addition, de la Fuente et al. (2013) found an interdependent effect between academic confidence, learning methods, and achievement in a study of 2,429 psychology students. Based on this, this study validated the control-value theory of achievement emotion (CVTAE) according to Pekrun (2006) .

Regarding CVTAE, Pekrun (2006) mentioned that learners’ assessment of control and value directly predicts their emotional responses, with control referring to the learners’ actions and beliefs about learning tasks (e.g., self-efficacy and attribution) and value referring to the importance learners place on learning tasks and outcomes ( Pekrun et al., 2011 ). When learners attach more importance to learning tasks, good outcomes are achieved ( Pekrun, 2006 ). Garn et al. (2017) found that learners’ perceptions of academic achievement can enhance or reduce the positive or negative outcomes faced in the learning environment. This study used the CVTAE perspective to analyze the correlation between graduate students’ academic achievement motivation, active learning, and academic confidence. It was hoped that the results of this study could be used to help improve the academic confidence of graduate students.

Achievement Motivation

McClelland (1961) rationalize the motivation of achievement and divide it into three demands (1) achievement, (2) connection, and (3) power. Pintrich and Schunk (1996) defined motivation as the process by which an individual is motivated and sustained in order to achieve a goal, thereby laying an important foundation for accomplishing the goal (e.g., planning, learning, and decision-making). Bigge and Hunt (1980) defined achievement motivation as the motivation to perform and actively work toward a goal without interruption, and gain a sense of accomplishment in the process. Individuals who set a goal they want to achieve will take action to achieve it ( Linnenbrink, 2005 ). Therefore, achievement motivation is also considered as a source of motivation to influence or maintain behavior ( Reeve, 2009 ). The motivation for achievement in this study refers to the graduate student’s motivation for wanting to complete a degree thesis.

Achievement motivation is a subjective value, as well as a psychological drive that helps individuals achieve their goals ( Singh, 2011 ). Urdan and Kaplan (2020) found that each scholar’s definition of achievement motivation is somewhat different; however, achievement motivation is inextricably linked to learning outcomes, emotions, and strategies. Research has found that achievement motivation has the ability to predict academic ability and task success ( Liao et al., 2012 ). In addition, Saiti et al. (2017) suggested that graduate student learning goals and motivation are inextricably linked to individual engagement in learning tasks to meet personal needs and expectations.

Active Learning

Bonwell and Eison (1991) noted that active learning occurs when an individual takes the initiative to perform a task and thinks about why they are doing it and that active learning is one of the processes of learning that requires learners to learn independently or in groups ( Singer et al., 2012 ). According to previous scholars, active learning is based on three components (1) higher-order learning: learners focus on the exchange of information and knowledge, (2) integrative learning: learners learn from experience, and (3) reflective learning: learners reflect on whether they have learned after the course ( Fink, 2003 ). Matsushita (2018) suggested that active learning is about driving learners to act and to reflect on learning through action. Graduate students are expected to develop independent research knowledge, skills, and experiences before writing their theses ( Davis et al., 2017 ). Active learning emphasizes learners’ deep learning, understanding, and engagement in the learning process ( Matsushita, 2018 ), and active learning expects learners to think, analyze, discuss, and make decisions with their peers as part of an active learning process ( Freeman et al., 2014 ). Active learning in this study refers to the ability of graduate students to understand the learning content completely, to apply their knowledge, and to learn from their mistakes.

In addition, Budsankom et al. (2015) showed that learners’ psychology is a direct and effective influence on higher-order learning. However, Robertson and Howells (2008) argue that integrative learning allows learners to “learn by doing” during the experiential process, which can deepen learners’ memory and practical skills. In addition, Hwang et al. (2014) showed that integrative learning not only allows learners to think deeply about how to solve problems during the learning process, but also to make a reflective search for what they did not do well when their performance was not as expected. Lucas (2001) pointed out that the learner’s motivation affects the learning strategy to be used in learning. Conversely, if learners have low achievement motivation, they will choose to complete their assignments at a lower standard ( Davidson, 2002 ). Research has found that achievement motivation has a positive effect on active learning ( Everaert et al., 2017 ). Therefore, the hypotheses related to the interaction between achievement motivation and active learning were as follows:

H 1 : Achievement motivation significantly and positively affects higher-order learning. H 2 : Achievement motivation significantly and positively affects integrative learning. H 3 : Achievement motivation significantly and positively affects reflective learning.

Academic Confidence

Rosenberg (1965) noted that confidence is an individual’s evaluation of self and that academic confidence is an important competency in the academic world ( Adnan et al., 2011 ). Academic confidence refers to learners’ beliefs about performing tasks and achieving academic goals ( Sander and Sander, 2005 ), which reflects the learners’ beliefs or expectations about success in the field of study. Wehrens (2008) indicated that when learners’ self-confidence is low, it leads to poor performance in learning outcomes, and Bénabou and Tirole (2002) showed that self-confidence has a strong influence on learning performance. Learners usually choose to perform tasks they believe they can accomplish ( Bandura, 1977 ; Eccles et al., 1989 ), Basturkmen et al. (2014) found that a growing number of graduate students are joining research programs, and found that graduate students need support and encouragement in their writing. For graduate students, academic research, knowledge, skills, competencies, and practices are a serious challenge ( Lee and Aitchison, 2009 ). Aithal and Kumar (2018) suggested that the application of knowledge is skill-dependent and that the application of skills requires confidence. Academic self-confidence in this study refers to the ability of graduate students to find their own research topics and to complete them.

In addition, Retnowati et al. (2018) showed that one of the reasons learners choose to avoid problems with Higher-order Thinking skills is because of low self-confidence and the belief that they cannot achieve the task. Di Francesca (2020) pointed out that engaged learners in active learning environments may build confidence, and some scholars believe that active learning can enhance learners’ responsiveness, confidence, and motivation. ( Robinson, 2017 ; Sibona and Pourrezajourshari, 2018 ). Research suggests that active learning has an important impact on the development of academic confidence ( O'Flaherty and Costabile, 2020 ). Pekrun (2006) proposed that CVTAE defines learners’ emotions as a regulatory process and that emotions can regulate individuals’ cognition, motivation, etc. Whereas good emotions stimulate individual behavior, bad emotions reduce individual behavior ( Pekrun, 2006 ; Pekrun et al., 2009 ). Self-confidence is a good source of emotion for the learner, which means that self-confidence and behavior are inextricably linked. Therefore, the hypotheses related to the interaction between active learning and academic confidence were as follows:

H 4 : Higher-order learning significantly and positively affects academic confidence. H 5 : Integrative learning significantly and positively affects academic confidence. H 6 : Reflective learning significantly and positively affects academic confidence.

Materials and Methods

Research framework.

Pekrun (2006) and Pekrun (2009) proposed that CVTAE theory can be used to observe whether learners’ beliefs affect their mobility and that when learners judge that a learning task is accomplishable, the next step will be to take action to achieve the goal. Therefore, based on the above literature, we proposed an initial model to investigate the relationship between achievement motivation, active learning, and academic confidence, as shown in Figure 1 .

Research model.

This study was conducted using online questionnaires with purposive sampling, and the questionnaires were collected from November 10, 2019 to December 5, 2019 from graduate students enrolled in universities of science and technology in Taipei City and New Taipei City.

Participants

There were 205 participants in this study. After 32 invalid samples were deleted, 173 valid samples were collected, indicating a questionnaire recovery rate of 84.39%, as shown in Table 1 .

Basic information.

Measurement

The questionnaire in this study was adapted from a scale developed by a related researcher and was divided into the three components of achievement motivation, active learning, and academic confidence. The results were assessed using a Likert 5-point scale (with answers ranging from 1: strongly disagree to 5: strongly agree ).

McClelland theorized achievement motivation in 1961 and identified three different needs for it: (1) the need for achievement; (2) the need for connection; and (3) the need for power ( McClelland, 1961 ). Linnenbrink (2005) noted that achievement goals motivate individuals to engage in task behavior. Some scholars believe that motivation can influence individual behavior, generate interest, or serve as a sustaining force ( Reeve, 2009 ). Pintrich (2003) suggested that motivation is inextricably linked to academic behavior. Hong et al.’s (2017) “Measuring intrinsic motivation of Chinese learning” was adopted to measure participants’ perceptions of their achievement motivation.

In Bonwell and Eison (1991) defined active learning strategies as learners actively doing something and thinking about why they are doing it ( Bonwell and Eison, 1991 ). Fink (2003) expanded on the previous foundation to include three additional items: (1) a focus on the learner in the exchange of information and knowledge; (2) allowing learners to actually observe situations; and (3) reflective learning, which involves learners thinking on their own or discussing with others. In addition, Singer et al. (2012) pointed out that active learning is defined as a learning process which requires learners to organize and integrate learning content, either independently or in groups. Therefore, this study revised Entwistle and McCune’s (2004) “Comparison of Scales from Inventories Measuring Study Strategies” to measure the participants’ perceptions of their active learning.

Bénabou and Tirole (2002) suggested that confidence refers to an individual’s belief in his or her own ability, while Stankov et al. (2012) suggested that confidence is a state in which an individual is certain of the success of a task or behavior. However, insufficient confidence or feelings of inferiority can cause learners to perform poorly in their learning ( Wehrens, 2008 ). Some studies have pointed out that confidence has a motivational effect on learning ( Bénabou and Tirole, 2002 ). Therefore, this study revised Sander (2009) “Academic Behavioural Confidence Scale” to measure the participants’ perceptions of their academic confidence.

Data Analysis

Structural Equation Modeling (SEM) is commonly used in the fields of psychology, sociology, and education ( Teo et al., 2013 ), and is often used to analyze the correlations between potential variables ( Hair et al., 2014 ). This study used SPSS for the descriptive statistics, Cronbach’s alpha reliability, and external validity, and used AMOS for the model and fitness validation.

Results and Discussion

Item suitability analysis.

The item analysis in this study was conducted using first-order confirmatory factor analysis. According to scholarly recommendations: the χ 2 / df value should not be greater than 5; the RMSEA should not be greater than 0.1; neither the GFI nor the AGFI should be less than 0.8; and the factor loading (FL) should not be less than 0.5 ( Hair et al., 2010 ; Kenny et al., 2015 ). As a result, the number of items regarding achievement motivation was reduced from nine to five; higher-order learning was reduced from seven to four; integrative learning was reduced from seven to five; reflective learning was reduced from seven to five; and academic confidence was reduced from seven to four items ( Table 2 ).

Item analysis by first-order confirmatory factor analysis.

Construct Reliability and Validity Analysis

The reliability of this study was first verified by Cronbach’s α to verify the internal consistency, and then the composite reliability (CR) was used to check the reliability ( Hair et al., 2010 ). In this study, the Cronbach’s α values ranged from 0.789 to 0.903 and the CR values ranged from 0.783 to 0.903, all of which met the criteria suggested by scholars, as shown in Table 3 .

Reliability and validity analysis.

The average validity of this study was measured by the factor loading (FL) and average variance extracted (AVE). Firstly, it has been recommended by scholars that the FL value should not be lower than 0.5 and that items which are lower than the recommended standard should be deleted ( Hair et al., 2010 ). The FL values for achievement motivation ranged from 0.648 to 0.848; the FL values for higher-order learning ranged from 0.578 to 0.9; the FL values for integrative learning ranged from 0.725 to 0.895; the FL values for reflective learning content ranged from 0.581 to 0.765; and the FL values for academic confidence ranged from 0.629 to 0.763, as shown in Table 3 . In addition, scholars believe that the AVE value should not be less than 0.4 ( Fraering and Minor, 2006 ), as shown in Table 3 .

Scholars have pointed out that the AVE square root value of each construct should not be lower than the Pearson correlation coefficient value of the remaining constructs, in order for the constructs to have construct discriminant validity ( Zainudin, 2015 ). The correlation value of each construct should not exceed 0.85; if the correlation is higher than 0.85, it will give rise to the concern of multivariate co-linearity ( Awang, 2012 ). The results of this study showed that the constructs had sufficient construct discriminant validity, as shown in Table 4 .

Construct discrimination analysis.

The value on the diagonal is the square root of AVE and the other values are the related coefficients.

Index Analysis

According to recommendations, χ 2 / df should not be higher than 5, RMSEA should not be higher than 0.1, PNFI and PGFI should not be lower than 0.5 ( Hair et al., 2010 ), and GFI, AGFI, NFI, NNFI, CFI, IFI, and RFI should not be lower than 0.8 ( Abedi et al., 2015 ). In the present study, the results were as follows: χ 2  = 347.041; df . = 203; χ 2 / df . = 1.710; RMSEA = 0.064; GFI = 0.842; AGFI = 0.803; NFI = 0.853; NNFI = 0.923; CFI = 0.932; IFI = 0.933; RFI = 0.833; PNFI = 0.676; and PGFI = 0.750. The results all met the criteria suggested by scholars, and therefore this study had a good model index.

Path Analysis

The model validation results showed that achievement motivation had a positive effect on higher-order learning ( β  = 0.721, p  < 0.001, t  = 9.141), achievement motivation had a positive effect on integrative learning ( β  = 0.749, p  < 0.001, t  = 8.173), achievement motivation had a positive effect on reflective learning ( β  = 0.746, p  < 0.001, t  = 8.016), higher-order learning had a positive effect on academic confidence ( β  = 0.259, p  < 0.01, t  = 2.424), integrative learning had a positive effect on academic confidence ( β  = 0.295, p  < 0.01, t  = 2.424), and reflective learning had a positive effect on academic confidence ( β  = 0.391, p  < 0.01, t  = 2.797), as shown in Figure 2 .

Verification model. ** p  < 0.01 and *** p  < 0.001.

The explanatory power of achievement motivation for higher-order learning was 51.9%; the explanatory power of achievement motivation for integrative learning was 55.7%; the explanatory power of achievement motivation for reflective learning was 56.1%; and the explanatory power of active learning (higher-order learning, integrative learning, and reflective learning) for learning confidence was 62.9%, as shown in Figure 2 .

Achievement Motivation Positively Influences Academic Active Learning

Entwistle (1998) and Vermunt and Vermetten (2004) stated that teachers encourage learners to adopt active learning because it leads to better learning outcomes, while Skaalvik and Skaalvik (2005) stated that learners’ understanding of learning objectives depends on the level of self-understanding. Ferla et al. (2010) confirmed that learners’ cognitive abilities predict their overall learning strategies, and Everaert et al. (2017) proved that achievement motivation has a positive relationship with active learning. The results of this study validated H 1 , H 2 , and H 3 , and showed that learners’ achievement motivation significantly and positively influences active learning (higher-order learning, integrative learning, and reflective learning), echoing the above study. It could be seen that the higher the learner’s motivation to achieve, the more enthusiastic the learner will be about the learning task and so take the initiative to learn.

Active Learning Positively Influences Academic Confidence

Adnan et al. (2011) identified academic confidence as an important competency for scholars in academia, while Sander and Sander (2005) stated that academic confidence gives learners the motivation to perform learning tasks and achieve goals. Libby (1991) confirmed that active learning can increase learning motivation, learning interest, confidence, problem-solving abilities, communication skills, and judgment, while Sibona and Pourrezajourshari (2018) confirmed that active learning can increase confidence. O'Flaherty and Costabile (2020) confirmed that active learning has a positive relationship with academic confidence. This study verified the results of H 4 , H 5 , and H 6 and confirmed that the active learning (higher-order learning, integrative learning, and reflective learning) of learners has a positive effect on their academic confidence, echoing the above study. This result indicated that the more active learners are in learning, the more their academic confidence will increase.

Conclusion and Recommendations

Conclusion and limitations.

Ehrenberg et al. (2009) found that the most important factor for learners to drop out of graduate school is the need to write a dissertation. Graduate students’ motivation, persistence, and strategies used to complete their degree dissertations are essential factors ( Spaulding and Rockinson-Szapkiw, 2012 ). Of course, the learner’s confidence that he or she can complete the research is also an essential factor ( Sander and Sander, 2005 ). In this study, six research hypotheses were formulated using CVTAE theory and were used to determine learners’ mobility, beliefs, and importance of learning ( Pekrun, 2006 ). Through validating the CVTAE framework, a theoretical model of the relationship among achievement motivation, active learning (higher-order learning, integrative learning, and reflective learning) and academic confidence was developed. The results showed that achievement motivation positively influences active learning (higher-order learning, integrative learning, and reflective learning) and that active learning (higher-order learning, integrative learning, and reflective learning) positively influences academic confidence. Therefore, thesis is the biggest factor influencing graduation, and in order to increase the graduation rate, we should improve the motivation of graduate students’ research, and encourage them to submit more journals to train their ability to write and further enhance their self-confidence.

Some studies have found that the establishment of students’ academic confidence is influenced by the importance of mentoring ( Gearity and Mertz, 2012 ). The achievement motivation and active learning used in this study were based on using the students’ self-assessments as the main influence. This study was not concerned with whether external factors would affect students’ confidence building, which was a limitation of this study. In addition, this study only investigated the relationship among achievement motivation, active learning, and academic confidence but did not address the students’ background variables, such as intelligence, gender, and family economics. It is suggested that future researchers expand the study population to include different background variables so as to create different models or use background variables as control variables.

Recommendations

The dissertation/ thesis is the last hurdle before a graduate student graduates, and it is also the hurdle that causes the most attrition of graduate students. A dissertation is a paper that a graduate student must produce within a few months, and it includes a number of processes, such as problem identification and validation, literature collection, data collection and analysis, and writing. If a graduate student fails to complete the dissertation on time, he or she will be required to delay graduation for one or more semesters ( Dupont et al., 2013 ). Komarraju et al. (2009) suggested that motivation affects learners’ interest, emotion, and confidence in learning tasks, while CVTAE theory holds that controlling the learner’s beliefs is an essential factor, and that whether the learner will take action on a learning task depends on whether he or she thinks the task can be completed ( Pekrun, 2006 , 2009 ). Similar to the concept of self-efficacy proposed by Bandura (1997) , in this study, the feasibility of a task was found to be judged by the learner before action, after which the learner’s judgment of the feasibility becomes a critical factor. Therefore, this study confirmed that the higher the motivation of graduate students to achieve, the higher the willingness to take the initiative to learn and the higher the academic confidence will be. Graduate students can gain a sense of accomplishment by setting goals, such as completing scale development or searching classical literature, and gradually increase their proportion of active learning through the guidance of their supervising professors, and eventually conduct independent research and gain academic confidence in research.

Sharma (2018) analyzed research related to stress and found that most of the studies believe that stress has negative effects on learners; however, the studies confirmed that an appropriate level of stress is beneficial for enhancing the achievement motivation of learners. It is suggested that subsequent studies include stress and investigate whether stress interacts differently with achievement motivation, active learning, and academic confidence.

In addition, Arsenis and Flores (2021) suggested that learner confidence has a critical influence on learning and achievement performance. As learners’ confidence in their learning ability affects their perceived performance on learning tasks, it is suggested that future research include learning achievement to investigate whether academic confidence predicts learning performance.

Gearity and Mertz (2012) noted that if graduate students want to find a direction for their research, they must first find supervising professors who can help them. Under the professor’s guidance, they must find the direction, questions, and structure of their research, and the professors must supervise the students to complete their research. Welton et al. (2015) confirmed that the supervising professor’s supervisory style or interaction with the graduate student also affects the completion of the graduate student’s thesis. The failure of graduate students to graduate is not only influenced by personal factors, but also by external factors. The results of this study showed that academic motivation, active learning, and academic self-confidence are positively influenced, but it is worthwhile to investigate whether the influence of external factors (e.g., Advisors) will change their influence.

Data Availability Statement

Ethics statement.

Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

J-CC and J-NY: concept and design and drafting of the manuscript. Y-TW and J-NY: acquisition of data and statistical analysis. J-CC and Y-TW: critical revision of the manuscript. All authors contributed to the article and approved the submitted version.

This study was partially funded by the Ministry of Science and Technology of Taiwan, with grant number MOST 110-2511-H-027-001.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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A STUDY OF SELF-ESTEEM AND ACADEMIC ACHIEVEMENT OF ADOLESCENTS IN URBAN AND RURAL AREA

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International Journal of Health Sciences and Pharmacy (IJHSP)

Srinivas Publication

The purpose of self-esteem is to feel and imagine that people nurtured in their mind over time about their self. In simple words, self-esteem is self-assessment; this perception and evaluation can be positive or negative and pleasant or unpleasant. Children with high self-esteem, usually feel good about themselves and better able to resolve their conflicts with other children and are resistant to deal with problems. One of the most important human traits to achieve objectives is self-esteem. The term self-esteem means " reverence for self ". The " self " pertains to the values, beliefs, and attitudes that we hold about ourselves. Having a strong will and self-confidence, decision-making power and originality, creativity, sanity and mental health is directly related to self-esteem and sense of self-worth. It also refers to an individual's sense of his or her value or worth, or the extent to which a person values, approves of, appreciates, prizes, or likes him or herself. During childhood, if individual's feelings are respected, thoughts valued and abilities recognized then self-esteem strengthens. When feelings are trampled upon, thoughts belittled and ability criticized then the individual's self-esteem remains at a low point of development and is therefore weak. During the course of time, an individual faces many life situations. Depending upon the success or failure and one's reaction to every significant situation in life, self–esteem grows stronger or gets considerably weakened Self-esteem is described as the evaluation that one makes about oneself, based on one's self-worth. Increases and decreases in self-esteem generally bring strong emotional reactions. Self-esteem and academic performance are interrelated factors. This study tries to bring the connectivity between academic performance and the self-esteem. The main aim of the study is to know the level of self-esteem of the students with low academic performance. The objectives of this study are to investigate the relationship between self-esteem and academic achievement, to understand the SocioEconomic background, to assess the level of self-esteem, and to know what could be the reason for low academic performance in spite of having high self-esteem. The research design used for the study would be descriptive in nature.

Scholarly Research Journal for Humanity Science & English Language

Ponmozhi, D.

The current investigation was planning to assess self-esteem of higher secondary school students in Cuddalore district, Tamil Nadu. Self-esteem scale constructed and standardized by researcher and guide is used to collect data from 210 higher secondary school students randomly. The scale contains 26 items in 6 dimensions. The collected data were analyzed with help of IBMSPSS19. Statistical techniques like Descriptive analysis, inferential analysis, correlation analysis and regression analysis have been used in this study. The higher secondary school student self-esteem is very high (109).Standard and Gender shows significant relationship with self-esteem of higher secondary school students. A stepwise regression was carried out to find the predication model for self-esteem. The predication model contained three of the ten predictors and was reached in 3 steps with 7 variables removed. The model was statistically significant, F(3,206)=17.08, p<0.01, and accounted for approximately 19% of variance in self-esteem(R2=0.199, Adjusted R2= 0.187).The structure coefficient suggests that standard and Gender were strong Indicator of Self-esteem and age was moderate Indicator of Self-Esteem.

Sehat Ullah

Self-esteem is an important academic construct in the process of education. It is recognized as one of the major factors in learning outcomes of students. Research has established that there is close relationship between selfesteem and academic achievements of students. This study also investigates government secondary school teachers’ perceptions of the relationship between self-esteem and students’ academic achievements. To collect data, a questionnaire, based on five point likert scale was designed and administered to 200 teachers. The selected teachers were randomly sampled from 30 government secondary schools in Swabi District. Data were collected, tabulated, analyzed and interpreted in simple percentage. The results demonstrated that students with positive self-esteem have high academic performance. Hence, it is inferred from the result of this study that there is a significantly high relationship between self-esteem and academic achievements of students. On the basis of this ...

Vijayakumari K

Journal of Educational and Social Research

Fatbardha Osmanaga

IOSR Journals

Abstract: This Study Investigated The Difference Between The Academic Performance Of Students With High Self-Esteem And Students With Low Self-Esteem. A Descriptive Research Design Of Survey Type Was Adopted For The Study. The Population For This Study Comprised All Public Secondary School Students In Ondo State. The Sample Consisted Of 240 Students From Six Randomly Selected Schools. A Questionnaire Tagged ‘Academic Performance Questionnaire’ Was Used To Collect Data. Expert Judgements Were Used To Ensure Face And Content Validity. Test-Retest Method Was Used To Determine The Reliability And A Reliability Coefficient Of 0.72 Was Obtained. Data Collected Were Analysed By Using T-Test. The Result Revealed That There Is A Significant Difference In The Academic Performance Of Students With High Self-Esteem And Students With Low Self-Esteem. It Can Be Concluded From The Result That Students With High Self-Esteem Perform Better In School Work Than Students With Low Self-Esteem. It Was Recommended That Parents Should Help Their Children To See Themselves In Positive Light. They Should Also Desist From Belittling Their Children And Doing Things That Can Deflate Their Self-Esteem.

IJAR Indexing

Self-esteem plays a crucial role during the adolescence stage when adolescents start to observe changes. Adolescents? self-esteem is often affected by the physical and hormonal changes they experience during puberty. They are extremely concerned about how they look, they are perceived and accepted by their peers. High self-esteem is directly related to having a very supportive family and body image is a major component in adolescents? self-esteem. Adolescent?s overall evaluation of his or her own worth as a self, such as how people feel about their physical appearance, skills, academic performance, and so on is reflected in their self-esteem. The aim of the present study was to assess the level of self-esteem of adolescents studying in Higher Secondary School. The researcher adopted descriptive research and using Dr. S. Karunanidhi?s Self Esteem Questionnaire assessed the level of self-esteem of adolescents in school. Census method was adopted and data was collected from 300 adolescents. The findings of this study reveal that more than half of the respondents have low overall level of self- esteem.

International Journal of Scientific & Technology Research

Yulia Nabella

Self-esteem is an evaluation made by an individual to maintain everything related to what is expressed in an agree or disagree attitude and the belief to be able, important, successful and useful. This study discusses the self-esteem of children in terms of the entrance path to the school, namely zoning or non-zoning of seventh-grade at SMPN 1 Cimahi in the 2019/2020 school year. This research uses quantitative. The design used in this study is correlational with descriptive methods. The population of participants in this study involved 50 students consisting of 25 zoning students and 25 non-zoning students. The data collection uses one instrument, the Rosenberg Self-Esteem Scale instrument.

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