thesis about motivation of students

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.

Reviewed by:

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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How Students’ Motivation and Learning Experience Affect Their Service-Learning Outcomes: A Structural Equation Modeling Analysis

Kenneth w. k. lo.

1 Service-Learning and Leadership Office, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China

2 Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China

Stephen C. F. Chan

Kam-por kwan, associated data.

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

Guided by the expectancy-value theory of motivation in learning, we explored the causal relationship between students’ learning experiences, motivation, and cognitive learning outcome in academic service-learning. Based on a sample of 2,056 college students from a university in Hong Kong, the findings affirm that learning experiences and motivation are key factors determining cognitive learning outcome, affording a better understanding of student learning behavior and the impact in service-learning. This research provides an insight into the impact of motivation and learning experiences on students’ cognitive learning outcome while engaging in academic service-learning. This not only can discover the intermediate factors of the learning process but also provides insights to educators on how to enhance their teaching pedagogy.

Introduction

The application of motivation theories in learning has been much discussed in the past decades ( Credé and Phillips, 2011 ; Gopalan et al., 2017 ) and applied in different types of context areas and target populations, such as vocational training students ( Expósito-López et al., 2021 ), middle school students ( Hayenga and Corpus, 2010 ) and pedagogies, including experiential learning and service learning ( Li et al., 2016 ). Motivation is defined in learning as an internal condition to arouse, direct and maintain people’s learning behaviors ( Woolfolk, 2019 ). Based on the self-determination theory, motivation is categorized as intrinsic motivation and extrinsic motivation ( Ryan and Deci, 2017 ). Intrinsically motivated learners are those who can always “reach within themselves” to find a motive and intensity to accomplish even highly challenging tasks without the need for incentives or pressure. In contrast, extrinsically motivated behaviors are motivated by external expectation other than their inherent satisfactions ( Ryan and Deci, 2020 ). To conceptualize student motivation, Eccles et al. (1983) proposed the expectancy-value model of motivation with two components: (a) expectancy, which captures students’ beliefs about their ability to complete the task and their perception that they are responsible for their own performance, and (b) value, which captures students’ beliefs about their interest in and perceived importance of the task. In general, research suggests that students who believe they are capable of completing the task (expectancy) and find the associated activities meaningful or interesting (value) are more likely to persist at a task and have better academic performance ( Fincham and Cain, 1986 ; Paris and Okab, 1986 ; Kaplan and Maehr, 1999 ).

Since then, expectancy-value theory has focused on understanding and enhancing student motivation, especially in core academic subjects ( Wigfield and Eccles, 2000 ; Liem and Chua, 2013 ). Many empirical studies demonstrate that the expectancy-value theory helps understand achievement-related behaviors and performance in key academic subjects in the school curriculum. Studies report that the expectancy and value components are positively related to students’ academic performance. For example Joo et al. (2015) conducted a study on 963 college students enrolled in a computer application course and found that the expectancy component and value component had statistically significant direct effects on academic achievement. Puzziferro (2008) found significant positive correlations between students’ self-efficacy for online technologies and self-regulated learning with the final grade and level of satisfaction in online undergraduate-level courses. Trautwein et al. (2012) conducted a study for 2,508 German high-school students and found that self-efficacy, intrinsic value, utility value and cost can predict academic performance in Mathematics and English. Schnettler et al. (2020) applied expectancy-value theory to study the relationship between motivation and dropout intention. A total of 326 undergraduate students of law and mathematics were studied, and findings showed that low intrinsic and attainment value was substantially related to high dropout intention. These studies argue that the expectancy component, value component and other student experiential variables such as self-regulated learning may positively relate to academic achievement. Recently, this theory has been applied to experiential learning, such as civic education ( Liem and Chua, 2013 ; Li et al., 2016 ). Results showed that higher expectancy and value beliefs could enhance students’ appreciation and engagement in civic activities, and finally promote the development of targeted civic qualities. This suggests that if expectancy-value theory is applied to service-learning, it would be expected that if students perceive that they are capable of completing the service project (expectancy component) or find the project meaningful (value component), they have higher motivation to engage in the project, and therefore, attain higher learning outcomes.

Students’ motivation in learning can be affected by different factors. These include their emotional, expressive and affective experiences ( Pintrich and De Groot, 1990 ; Deci, 2014 ), previous learning experiences and culturally rooted socialization, such as gender and ethnic identity ( Wigfield and Eccles, 2000 ). For example, Yair (2000) conducted a study to investigate the effects of instructions on students’ learning experiences. The result showed that structured instructions are better able to improve the learning experiences, which leads to higher motivation of the students. In short, research suggests that students’ motivation affect the academic performance, and motivation itself is impacted by other factors.

Despite all these studies, there has been limited work that applies the expectancy-value theory to study the learning process and understand how the different variables affect students’ motivation and learning outcomes, especially in service-learning. Service-learning is a type of experiential learning that provides a rich set of learning outcomes through applying academic knowledge to engage in community activities that address human and community needs and structured reflection ( Jacoby, 1996 ). Bringle and Hatcher defined academic service-learning as:

a credit bearing educational experience in which students participate in an organized service activity that meets identified community needs and reflect on the service activity to gain further understanding of course content, a broader appreciation of the discipline, and an enhanced sense of civic responsibility ( Bringle and Hatcher, 1996 , p. 5).

This pedagogy helps students translate theory into practice, understand issues facing their communities, and enhance personal development ( Eyler and Giles, 1999 ; Hardy and Schaen, 2000 ). Previous studies on the benefit of service-learning showed that service-learning could be an effective pedagogy to achieve a wide range of cognitive and affective outcomes, especially on their academic ( Giles and Eyler, 1994 ; Lundy, 2007 ), social ( Weber and Glyptis, 2000 ), personal ( Yates and Youniss, 1996 ; Billig and Furco, 2002 ), and civic outcomes ( Bringle et al., 2011 ; Mann et al., 2015 ). Service-learning is recognized as a high-impact educational practice ( Anderson et al., 2019 ) and it promote positive educational results for students from widely varying backgrounds ( Kuh and Schneider, 2008 ). It is increasingly adopted in universities across the world ( Furco et al., 2016 ; Wang et al., 2020 ; Sotelino-Losada et al., 2021 ) and has received significant attention from both academics and researchers in different academic disciplines ( Yorio and Ye, 2012 ; Geller et al., 2016 ; Rutti et al., 2016 ), and an increasing number of institutions have formally designated service-learning courses as part of the curriculum ( Nejmeh, 2012 ; Campus Compact, 2016 ).

Academic service-learning requires students to learn an academic content that is related to a social issue, and then apply their classroom-learned knowledge and skills in a service project that serves the community. In other words, students’ cognitive and intellectual learning is augmented via a mechanism that allows them practice of said knowledge and skills ( Novak et al., 2007 ). An example would be learning about energy poverty and solar electricity, and then conduct a service project installing green energy solutions for rural communities in developing countries. Another example is learning about the impact of eye health on academic study, and conducting eye screenings for primary school students. To prepare the students, lectures and training workshops teach students about the academic concepts to equip them with the necessary skills to deal with complex issues in the service setting, and prepare them to reflect on their experience to develop their empathy and build up a strong sense of civic responsibility. The objective is to develop socially responsible and civic-minded professionals and citizens. Therefore, the linkage between academic content, students’ learning and meaningful service activity is critical, as the classroom theory, in a sense, is experienced, practiced and tested in a real-world setting.

Yorio and Ye (2012) suggest that tackling real-life community problems in service-learning leads to increased motivation that can also result in increased cognitive development. However, similar to other educational areas, not much effort has been paid to the “process” by these learning gains are imparted to students. To reveal the mechanism of the learning behavior and provide suggestions for improving the effectiveness of students’ learning, researchers need to investigate the dynamic processes and the influencing factors on how students learn during service-learning. Students do not automatically learn from just engaging in service-learning activities. Instead, how and what students learn depends on different factors. Fitch et al. (2012) suggested using structural equation modeling to develop a predictive model to investigate how students’ initial levels of cognitive processes and intellectual development may interact with the quality of service-learning experiences, and therefore predict cognitive outcomes and self-regulated learning.

Since then, a few studies have been conducted to discover the factors that affect the learning outcomes in service-learning, such as the quality of students’ learning experiences ( Ngai et al., 2018 ), students’ motivation ( Li et al., 2016 ) and students’ disciplinary backgrounds ( Lo et al., 2019 ). Also, Moely and Ilustre (2014) found that the academic learning outcomes were strongly predicted by the perceived value of the service. If students have a clear understanding of the value of the service and acknowledge the benefits to the community, their motivation will increase, which ultimately improves their cognitive learning.

Despite the accumulating evidence suggesting that students’ motivation is an important factor affecting study outcomes, and other research showing that service-learning has positive impacts on students, several research gaps are present. First, there has not been much research using the expectancy-value theory of motivation in service-learning to examine how motivation affects students’ learning from service-learning. Li et al. (2016) explored the effect of subjective task value on student engagement during service-learning and found that the subjective task value of the service played an essential role in their engagement and, therefore, affected their learning. However, this study only focused on the value component of motivation and how this dimension affected students’ engagement, which is correlated to student learning outcomes, but it did not directly study the impact on the learning outcomes. Service-learning, being an experiential learning pedagogy, requires students to actively engage in and reflect on the learning experiences and community needs, then plan and conduct a service project by applying their knowledge ( Kolb, 1984 ). During the project, students interact with the service recipients and instructors to reflect on the assumptions, identifying connections or inconsistencies between their experiences and prior knowledge. This clarification of values and assumptions generate new understandings of the issue, which may lead to changes in the design and execution of the service project. This learning cycle involves a very different set of learning experiences compared to conventional classroom teaching, and thus may impact students differently. This leads us to the second point. As researchers and educators, we must ask how learning occurs and what conditions foster the development. In other words, it is important to examine not only if , but also how , service-learning affects students’ academic outcomes. Although studies have been conducted to understand the factors influencing students’ learning outcomes, results are far from conclusive.

This study aims to fill in these gaps. Grounded on the expectancy-value theory of motivation in learning, the research question would be, “How do students’ learning experiences and motivation affect their cognitive learning outcomes from service-learning?” The hypothesized model is presented in Figure 1 , which includes four elements (i) initial level of cognitive knowledge, (ii) the learning experiences, (iii) students’ motivation on the service-learning course, and (iv) the cognitive learning outcome. It posits that students’ cognitive learning outcomes from service-learning are affected by their initial ability, the learning experience, and also mediated by their motivational beliefs about the expectancy component and value component in completing the service-learning tasks. In the service-learning context, if a student perceives that the service project has a high chance of success (expectancy component) and they do find the associated activities meaningful or interesting (value component), then they have higher motivation to engage in the project and thus achieve a higher cognitive learning outcome. In addition, the model hypothesizes that students’ motivation is affected by their learning experiences and their initial level of cognitive knowledge.

Hypothesized model.

To answer the research question, three hypotheses are defined:

• 1. Based on the preceding literature review, we hypothesize that students’ motivation, both the expectancy and value components, can be impacted by their learning experiences. Also, the initial level of cognitive knowledge of students may have an impact on the motivation (Hypothesis 1).
• 2. Based on the theoretical framework of the expectancy-value theory of motivation in learning, we expect that students’ motivation, both the expectancy and value components, can positively predict the learning outcomes (Hypothesis 2).
• 3. Based on the existing literature, we hypothesize that both the students’ learning experiences and their motivation, both the expectancy and value components, directly affect students’ cognitive learning outcome, and motivation can further act as a mediating factor between learning experiences and cognitive outcomes (Hypothesis 3).

Methodology

The study was conducted at a university in Hong Kong in which service-learning is a mandatory graduation requirement for all full-time undergraduate students. Students have choices over when and which subject to take to meet the requirement. Most of the courses are open-to-all general education type courses, while others are discipline-related subjects restricted to students from particular disciplinary backgrounds or major students. Our study covers 132 of these service-learning subjects offered by 30 academic departments during the 2019/2020 and 2020/2021 academic years. All of the academic service-learning subjects involved in this study carried three credits and followed an overall framework with common learning outcomes standardized by the university, which includes (a) applying classroom-learned knowledge and skills to deal with complex issues in the service setting; (b) reflecting on the role and responsibilities both as a professional and as a responsible citizen; (c) demonstrating empathy for people in need and a strong sense of civic responsibility; and (d) demonstrating an understanding of the linkage between service-learning and the academic content of the subject. All subjects required roughly 130 h of student study effort and were standardized to three main components: (a) 60 h of classroom teaching and project preparation; (b) a supervised and assessed service project comprising of at least 40 h of direct services to the community and which is closely linked to the academic focus of the subject, and (c) 30 h of structured reflective activities. Students’ performance and learning were assessed according to a letter-grade system. The nature of the service projects varied, including language and STEM instruction, public health promotion, vision screening, speech therapy and engineering infrastructure construction. Those projects also covered a diverse range of service beneficiaries, including primary and secondary school children, elderly, households in urban deprived areas, ethnic minorities, and rural communities. Approval for this study was granted by the university’s “Human Subjects Ethics Sub-Committee.”

The study employed several quantitative self-report measures to assess students’ learning experiences, learning outcomes, and motivation as described below and shown in Supplementary Appendix 1 . Also, hypothesized model with measures was present in Figure 2 .

Hypothesized model with measures.

(1) Students’ learning experiences was measured by their self-reported experiences regarding the (a) pedagogical features of the course, and (b) design features of the service-learning project. A 13-item instrument was developed in the same university under a rigorous scale development procedure, and students were asked to indicate their experiences after completing the service-learning subject, on a seven-point Likert scale (1 = strongly disagree; 4 = neutral; 7 = strongly agree). All items were written and reviewed by a panel of experts, then a large-scale validation through EFA and CFA was undertaken.

The Pedagogical Features dimension included seven items to measure the extent to which students perceived how well they are facilitated and supported in their learning process. This relates to the teachers’ skills in preparing the students for the services, nurturing the team dynamic and assisting the students in reflecting upon the service activities.

The Project Design Features dimension included six items to measure to the extent to which students perceived positive experiences during the service project, which is a unique and necessary component of academic service-learning. These features are designed and positioned by the teaching team. Examples include the level of collaboration with the NGO/service recipients and the opportunities for the students to try new things. These experiences are all part of the project design, which, as it is linked to the academic concept covered in the classroom, is controlled by the teacher.

In terms of the construct validity, an exploratory factor analysis (EFA) was conducted with a sample of 11,185 students who completed the service-learning subjects between 2014/2015 and 2018/2019, which yielded a two-factor structure with an 0.81 average factor loading for both aspects without cross-loading at the threshold of 0.30. The reported Cronbach’s α value was 0.90 and 0.89 for pedagogical features and project design features, respectively. Confirmatory factor analysis (CFA) was conducted in this study, and the results showed a good model fit for the two-factor model of learning experiences (χ 2 = 232.33, df = 52, CFI = 0.96, NFI = 0.95, RMSEA = 0.08).

(2) Students’ motivation was measured by items taken from the Motivated Strategies for Learning Questionnaire ( Pintrich and De Groot, 1990 ), which included 44 items measuring two main dimensions, (a) Motivational Beliefs (22 items) and (b) Self-Regulated Learning Strategies (22 items). Under motivational beliefs, three sub-dimensions were defined, including intrinsic value, self-efficacy, and text anxiety. Intrinsic value and self-efficacy were corresponding to the value component and expectancy component, respectively, under the expectancy-value model of motivation proposed by Eccles et al. (1983) . To align with the institutional service-learning context, an expert review was conducted to select and modify the items. Test anxiety was removed since tests or examinations were not part of the assessment criteria in the service-learning context. One item, “I often choose paper topics I will learn something from even if they require more work,” under the intrinsic value sub-dimension was removed, as the service-learning courses that we are studying require direct services which are connected to tangible community needs and “paper topics” would not be encountered in our context. Wordings from five items were modified to specifically refer to the context for better understanding of students. For example, “class” was changed to “service-learning class” and “class work” was changed to “service project.” The self-regulated learning strategies construct was not included as this study focuses on the causal relationship between learning experiences, students’ motivation, and cognitive learning outcome for engaging in academic service-learning.

After modification, 17 items were selected with eight items from the intrinsic value sub-dimension (value component) to measure the subjective task value of the service-learning subject to the students and nine items from the self-efficacy sub-dimension (expectancy component) to measure the competence belief or expectancy for success in completing the project. Pintrich and De Groot (1990) reported a reliability coefficient of 0.87 and 0.89 for the intrinsic value and self-efficacy, respectively. In this study, a CFA was conducted to ensure the construct validity and a good model fit for the two-factor structure of motivation was found (χ 2 = 329.05, df = 88, CFI = 0.97, NFI = 0.95, RMSEA = 0.08). The average factor loading of intrinsic value was 0.77 and self-efficacy was 0.73.

(3) Cognitive Learning outcomes from service-learning was measured by a four-item scale adopted by the Service-Learning Outcomes Measurement Scale instrument (S-LOMS) developed by Snell and Lau (2019) . This scale was developed and validated under a cross-institutional research project in Hong Kong. With the localization of the items, the scale contains four dimensions with 11 sub-domains. Students are required to respond to the items on a 10-point Likert-type scale ranging from 1 (strongly disagree) to 10 (strongly agree).

Knowledge application is one of the dimensions that comprise a single cognominal domain to measures the extent to which students are able to understand the knowledge learnt in the service-learning course and apply it to real-life situations. Following the standard approach employed in academic research, the instrument was first developed through review by a panel of experts and focus groups of students. Then, the psychometric properties, including underlying dimensionality and internal consistency, were tested via EFA and CFA with a sample of 400 university students from four Hong Kong institutions ( Snell and Lau, 2020 ), reporting a strong internal consistency with a Cronbach’s α value of 0.96. Then, the scale was validated again with another group of students, this time from Singapore ( Lau and Snell, 2021 ). To ensure the construct validity could be maintained, an EFA was conducted for both pre-experience and post-experience data, and the results confirmed a single-factor model with factor loadings over 0.82.

Participants and Administration

Our survey was administered to all students enrolled in any credit-bearing service-learning subject offered by the institution of study during the 2019/2020 and 2020/2021 academic years. Students were asked to complete a survey both at the beginning and end of the subject. This generally corresponds to the beginning and end of the semester; some subjects ran over multiple semesters. The pre-experience survey was comprised of the cognitive learning outcome (knowledge application) scale while the post-experience survey consisted of items related to their leaning experiences (pedagogical features and project design features), motivation (intrinsic value and self-efficacy) and cognitive learning outcome (knowledge application). Only the pre-experience survey in the fall semester of 2019/2020 was administered via paper-based questionnaires. For the rest of the offerings, both pre-experience and post-experience surveys were administered via the university online survey platform. To conduct the survey in pen-and-paper format, the course instructors or teaching assistants visited the class to distribute the questionnaires within the first 4 weeks of the semester. For the electronic format, the pre-experience survey was sent to the students by the lecturers within the first 4 weeks of the semester and the post-experience survey was conducted at the end of the subject. For both surveys, email invitations were sent at least twice to follow up with non-respondents to urge them to complete the questionnaire. The collated data was analyzed with the statistical analysis software programs IBM SPSS Statistics (Version 26) and IBM AMOS (Version 26).

Data Analysis Method

Our data analysis went through the following steps to examine the relationship between students’ learning experiences, motivation and learning outcomes in service-learning, and established the causal effect of the exogenous and endogenous variables.

Means and standard deviations were computed for the data obtained. The reliability of the measures was estimated by the Cronbach’s α values ( Cronbach, 1951 ). Pearson correlation coefficients were calculated to describe the linear association between students’ learning experiences, motivation and learning outcome.

Path analysis in structural equation modeling (SEM) was then employed to examine the effect of initial level of cognitive knowledge, learning experiences and students’ motivation toward learning outcomes using SPSS AMOS 26. SEM is a collection of tools for analyzing connections between various factors and developing a model by empirical data to describe a phenomenon ( Afthanorhan and Ahmad, 2014 ). Path analysis is a special problem in SEM where its model describes causal relations among measured variables in the form of multiple linear regressions. The hypothesized model studied the direct or indirect effects of students’ learning experiences and motivation on their learning outcome. Therefore, the dependent variable was the cognitive learning outcome from service-learning, and the independent variables were their motivation (intrinsic value and self-efficacy) and learning experiences (pedagogical features and projects design features).

The path analysis was conducted through the following steps:

• 1. Multivariate kurtosis value was computed to confirm the multivariate normality ( Kline, 2015 );
• 2. Mahalanobis distances were calculated to determine the outliners ( Westfall and Henning, 2013 );
• 3. Goodness of fit of the hypothesized model was tested ( Shek and Yu, 2014 );
• 4. R-square ( R 2 ) were computed to illustrate the explained variation; and
• 5. Standard estimate coefficients (β) of the significant paths were calculated to quantify the “magnitude” of the effect of one variable on another.

The survey was administered to 8,271 students in the 132 credit-bear service-learning subjects offered during 2019/2020 and 2020/2021. A total of 5,216 and 3,102 responses were received in the pre and post-experience surveys, respectively, making up a response rate of 63.06 and 37.50%. For the paper-based responses, casewise deletion was applied for handling the missing value. For the electronic-based responses, the survey platform would ensures there would not be any missing values. 2,116 (25.58%) valid matched-pair responses were finally obtained and included in the study. A detailed analysis of the respondents’ demographic information reveals that 883 (41.73%) were female and 1,233 (58.28%) were male. Almost half of the students, 988 (46.69%), were from junior years, while 1,128 (53.31%) were from senior years. In terms of the disciplinary background, 608 (28.73%) were from engineering, 530 (25.05%) students from business and hotel management, 475 (22.45%) were studying health sciences, 254 (12.00%) were in hard sciences, and the remaining 249 (11.77%) were in humanities, social sciences, or design. Of the 132 subjects, 46 (34.85%) were from the discipline of health sciences, 31 (23.48%) were from engineering, 27 (20.45%) from humanities and social sciences, 14 (10.61%) from hard sciences, and the remaining 14 subjects (10.61%) were from the business, hotel or design disciplines.

Descriptive Statistics and Reliability of the Measures

The scale scores were computed by taking the arithmetic mean of the items purported to be measuring the respective constructs. Table 1 presented the minimum, maximum, mean and standard deviation for each measure.

Descriptive statistics and reliabilities.

Generally, students gave medium to high scores on their learning experiences and motivation. The mean scores on their learning experiences with respect to the project design and pedagogical features were 5.49 and 5.53, respectively. For their motivation measures, the means and standard deviations were 5.42 and 0.85 for intrinsic value and 5.34 and 0.86 for self-efficacy. For the knowledge application learning outcome, students reported mean scores of 6.95 and 7.48, respectively, in the pre- and post-experience survey.

Cronbach’s α estimates were computed for the six measures included in the study to check for internal consistency. The results were also shown in Table 1 . The alpha values for the scales on learning outcomes and motivation were over 0.93, which would be classified as having excellent reliability ( Kline, 2000 ). On the other hand, the alpha values of the learning experience measures were 0.88 and 0.91, suggesting good to excellent reliability of these two scales.

Correlations

The Pearson’s product-moment correlations between the measures were presented in Table 2 . All correlations were positive at the 0.01 level, which indicated that the measures change in the same direction: when one increased, the others also tended to increase. In other words, students’ motivation and cognitive learning outcome increased when they had a better learning experience. In general, all scales had a medium to strong association except for the initial cognitive learning scale, which had weak to medium associations with other scales.

Correlation between motivation, learning experiences, and learning outcomes.

N = 2,116. **Correlation is significant at the 0.01 level (2-tailed).

Students’ ratings on the project design features were significantly related to the two motivational belief measures, with r = 0.74 and 0.64 for intrinsic value and self-efficacy, respectively. Their ratings on the project design features were also significantly related to their initial level of cognitive knowledge ( r = 0.30) and post-cognitive learning outcome ( r = 0.64) measures. Similar results were observed for the pedagogical features, where the correlation coefficient with the post-experience cognitive outcome score was 0.65, suggesting a highly correlated relationship. However, the correlation coefficient with the pre-experience score was 0.32, suggesting a rather medium level of association between the two. Significant correlations were found between pedagogical features and intrinsic value ( r = 0.76) and self-efficacy ( r = 0.61).

Regarding the correlations between motivation and learning outcomes, a medium association was found between motivation and the initial level of cognitive knowledge with reported correlation coefficients of 0.35 (intrinsic value) and 0.36 (self-efficacy). Significant and high correlations were found between motivation and post-experience cognitive learning outcome, with coefficients of 0.68 (intrinsic value) and 0.61 (self-efficacy).

Path Analysis in Structural Equation Modeling

A path analysis was conducted to determine the causal effects among learning experiences, students’ motivation and learning outcomes. The models were tested using the maximum likelihood method, which required multivariate normality.

Multivariate kurtosis value of the observed variables was examined with results ranging from 0.15 to 1.22, suggesting that the variables had a multivariate normal distribution ( Kline, 2015 ). Then, Mahalanobis distances were calculated in AMOS to determine the outliers ( Westfall and Henning, 2013 ), and 60 responses were identified as outliers with a significance level at p < 0.001. These responses were therefore excluded from the data. As a result, only 2,056 data points were included in the path analysis. The resulting model was shown in Figure 3 , which was consistent with our original conceptual model from Figure 1 . The paths shown in the figure were statistically significant at the 0.001 level, and the standardized regression coefficients (β) and explained variation ( R 2 ) were also presented. A chi-square test showed that the estimated model has an acceptable level of goodness of fit [χ 2 (2, N = 2,056) = 225.05, p < 0.001]. Table 3 showed the values of goodness-of-fit indices. The CFI, NFI, and GFI values all met the respective criterion for goodness of fit.

Path diagram between the initial level of cognitive knowledge, learning experiences, students’ motivation, and the cognitive learning outcome.

Outliers and goodness-of-fit statistics.

*CFI, comparative fit index; NFI, normed fit index; GFI, Goodness of Fit. Evaluation criteria are determined according to Bentler and Bonett (1980) , Bollen and Long (1993) , Schumacker and Lomax (2004) , and Kline (2015) .

The results of the path analysis were consistent with our hypotheses:

• Hypothesis 1: Students’ learning experience and previous cognitive knowledge had a direct effect on motivation. Intrinsic value was positively predicted by the initial level of cognitive knowledge (β = 0.12, p < 0.001), the project design (β = 0.36, p < 0.001), and pedagogical (β = 0.45, p < 0.001) features of the service-learning subjects as experienced by the students, with a 60% of variation explained. Similarly, self-efficacy was positively affected by the initial level of cognitive knowledge (β = 0.15, p < 0.001), the project design (β = 0.39, p < 0.001), and pedagogical (β = 0.27, p < 0.001) features. These factors explained 42% of the variation of self-efficacy. However, the direct effect of the initial level of cognitive knowledge was much less than the direct effect of the two dimensions of learning experiences.
• Hypothesis 2: Students’ motivation had a positive direct effect on their learning outcome. Students’ post-experience knowledge application ability is positively predicted by their ratings on intrinsic value (β = 0.25, p < 0.001) and self-efficacy (β = 0.18, p < 0.001) in completing the service-learning subject.
• Hypothesis 3: Students’ learning experience had a direct effect and an indirect effect mediated by motivation on their learning outcomes. The total effect (E Total = E Direct + E Indirect ) of the project design features on cognitive learning outcome was 0.32, with a direct effect (β) of 0.16 and an indirect effect of 0.16 through intrinsic value (E Indirect = 0.09) and self-efficacy (E Indirect = 0.07). For pedagogical features, the total effect was 0.33 with a direct effect (β) of 0.17 and an indirect effect of 0.16 through intrinsic value (E Indirect = 0.11) and self-efficacy (E Indirect = 0.05). In total, 50% of the variation in students’ cognitive learning outcomes could be explained by their previous level of knowledge, learning experiences and motivation.

Previous research in academic service-learning in higher education tend to focus on its benefits and impact to students. A number of studies have shown that service-learning is an effective pedagogy for improving cognitive learning outcomes; however, most of these studies were outcome-based rather than process-based ( Li et al., 2016 ). Since the outcome of service-learning has been established, we argue that it is now necessary to examine the dynamic processes and understand the underlying factors that produce these positive learning outcomes. These insights not only provide suggestions for improving the effectiveness of service-learning, but also complete the theoretical framework for understanding the learning behavior in service-learning. Levering on the theoretical support of the expectancy-value theory in motivation, we hypothesized that the expectancy component and value component of students’ motivation play an important role in affecting the cognitive outcome and act as a mediator between the learning experiences and academic outcome.

In line with the research focus, this study aimed to explore the causal relationship between learning experiences, learning motivation, and learning outcomes in the context of academic service-learning. Using a validated, quantitative instrument and analyzing the responses with structural equation modeling showed that in the context of academic service-learning, significant direct and indirect effects were found between initial level of cognitive knowledge, students’ learning experience, motivation and cognitive learning outcomes.

According to the expectancy-value theory introduced by Eccles et al. (1983) , motivation is affected by multi-layered factors, including individuals’ perceptions of their own previous experiences, culturally rooted socialization (i.e., gender roles or ethnic identity), and self-schemata (i.e., self-concept of one’s ability or perceptions of task demands). Recent research also found that students’ motivation increases when they gain insight into their values and goals ( Brody and Wright, 2004 ; Duffy and Raque-Bogdan, 2010 ). This has also been found to be the case in academic service-learning ( Darby et al., 2013 ). Our results demonstrated similar findings in which students’ learning experiences in academic service-learning were a significant determinant of their learning motivation.

From the path analysis, significant direct effects to students’ motivation were identified from students’ initial level of cognitive knowledge and both aspects of learning experiences. These factors positively associated to intrinsic value and self-efficacy, explaining 60 and 42% of the variation, respectively. The effect of the learning experiences were much higher than the effect of the initial level of cognitive knowledge. This indicated that students who had positive learning experiences, regardless of whether the experiences were project- or pedagogically related, were more motivated to learn and were more likely to believe they had the ability to complete the subject. Also, pedagogically related experiences had a slightly larger effect than project design-related experiences on both motivation measures, which implied that preparation and feedback from teachers were more critical with respect to improving students’ motivation than the design of the service project.

Our results suggest that “student motivation” is not static, but could be learned and improved, and the learning experiences played an important role. If educators want better-motivated students, they need to have good interaction with the students, offer necessary support and provide insightful feedback in reflective activities. In the context of academic service-learning, the subject teachers or teaching assistants would achieve best results by working side-by-side with the students throughout the course, including the service project, instead of delegating this component to outside agencies. During the lectures, instructors have to prepare the students appropriately, such as guiding students to understand the linkage between the academic concept and service objective and equipping the students with necessary professional or technical skills. Educators should also regularly conduct reflective activities to cover different aspects of the service-learning course, such as team dynamics, service preparation, community impacts, or personal learning.

On the other hand, even if slightly less critical, the project design features also played an important part. The service project should be designed to be challenging and allow students to have ample direct interaction with the community. Well-prepared students would be more likely to feel competent and confident of success in their project, and challenging but valuable projects that benefit the community and gain the appreciation of the service targets convince students that what they were doing was important and had value. Taking the example of an engineering service project, teachers should allow a certain level of autonomy to the students and challenge them to interact with the collaborating agency or service recipients, understand the needs, and design a tailored solution, rather than asking students to simply replicate a previously designed solution, which may discourage students from engaging in the services, which then leads to a decrease in motivation.

In terms of cognitive outcome, the results of the path analysis indicated that the outcome was affected in three ways, (i) directly through the learning experiences; (ii) directly through the students’ motivation, and (iii) indirectly through the learning experiences with motivation as a mediating factor.

Academic service-learning programs are intentionally designed to have a strong linkage between academic content and service activities. It is known that students do not automatically learn from engaging in service-learning activities. Instead, how and what students learn depends on the quality of their learning experiences ( Ngai et al., 2018 ). Other research has highlighted the importance of the learning experience ( Billig, 2007 ; Taylor and Mark Pancer, 2007 ; Chan et al., 2019 ), and showed that they are positively correlated with the learning outcomes ( Eyler and Giles, 1999 ; Joo et al., 2015 ).

Results of the path analyses showed that the both the pedagogical and project design aspects of the learning experience have similar direct effects and total effects on the cognitive learning outcome, as well as an indirect effect on the outcome through motivation. These findings were consistent with prior studies ( Liem and Chua, 2013 ; Li et al., 2016 ; Lo et al., 2019 ) and illustrate the causal relationship between learning experiences, motivations and learning outcomes, which demonstrated the cognitive processes of learning. The standardized beta coefficients further show that the magnitude of the indirect effect was slightly larger than the direct effect, suggesting that the larger impact from the learning experiences is via motivation as a mediating factor.

These findings have implications on service-learning practice. One of the differences between academic service-learning and traditional classroom learning is that in service-learning, students need to step outside the classroom and conduct a project to meet identified community needs in real life. Some service projects are delinked from the course material. Sometimes, students are sent out to do piece-meal service or charity work without preparation. Some service projects are over-conceptualized or over-abstracted, for example, having students work primarily on data analysis or reporting. Service-learning teachers should note that both pedagogical and project experiences are equally important. Students needed to understand and relate to the community and individuals they serve, including their needs and their challenges, and to build relationship and empathy with them. Students need also to be equipped with the necessary knowledge and skills for designing and implementing the service, which needs to meet genuine identified needs of the community. Only then do they learn. For example, if students are tackling a challenging project, but they perceive the values and benefits of the services and are well prepared and supported by teachers, and feel connected to and appreciated by the community, they are more likely to recognize the importance of their efforts (value component) and believe that they have the ability to complete the project (expectancy component). This strengthens their engagement and thus they are better able to reflect on their experience and performance. This process positively affects their understanding of the academic content, and therefore, increases their ability to apply knowledge and skills to tackle social issues in real-life service settings.

We study the causal relationship between learning experiences, students’ motivation, and the cognitive learning outcome in academic service-learning. Decades of research have demonstrated the positive impacts of service-learning on students’ learning, but there has been limited efforts on studying the process and understanding the intermediate factors. Our findings highlight the fact that learning experiences and motivation are key determining factors toward the learning outcome. Motivation in particular is dependent upon the learning experiences, which have not only a direct effect on the outcomes but also indirect influence through motivation as a mediating factor. By applying the expectancy-value theory, this study makes a unique contribution to understanding students’ learning behaviors in academic service-learning. Results show that positive learning experiences can increase the level of expectancy for success and increase the personal value of the project. These can enhance the students’ motivation and engagement in the learning activities, and finally, promote the development of academic learning outcomes.

There are some implications for teachers and practitioners of service-learning. First, students’ motivation can and does change. Second, the learning experience has a strong impact on students’ motivation. Hence, effort should be paid to designing the service project and pedagogical elements. In terms of project design, students need to be intentionally educated, via interactions with service recipients and other means of observing or evaluating the impact brought about by their project, the contribution and value of their project to the community. It is also important to expand students’ boundaries with challenging service activities that allow a certain level of autonomy. For example, students conducting public health tests can be tasked with studying the income level and dietary availabilities within the community, and to design some healthy eating menus to share with their community recipients in addition to going through the standardized health test protocol. This challenges students to consolidate and apply their knowledge and allows them some degree of self-directing the design of the projects. In terms of the pedagogical features, teachers and practitioners need to schedule regular – and structured – reflection activities, and make space for good quality interactions with students and ensure that they receive help and support when needed.

It should be stressed that the subjective task-value and expectancy of success are important factors and should be treated with respect. Educators should intentionally design classroom or project activities to highlight these aspects, such as guiding students to reflect on what service-learning and positive citizenship means to them, and how their efforts can contribute to the lives of the underserved in community. These can increase students’ efforts, attention, and persistence in service-learning tasks, which eventually improves their motivation, which can bring positive effects to the learning outcome.

Limitations and Future Studies

This study has applied expectancy-value theory in understanding the effect of students’ motivation and learning experiences in academic service-learning and shed light on the role of expectancy and value beliefs in the learning outcomes. However, several potential limitations need to be considered when interpreting findings. They also provide directions for future research.

First, the data analyzed in this study were mainly derived from self-report surveys. Although the use of the self-report method may affect the strength of inter-factor relationships examined in this study, we minimize this potential method bias by applying the structural equation analytic technique with a large sample size that purges the measurement of its errors. In future research, additional data sources should be utilized, such as observation from teachers and structured reflective essays, and using different methodological paradigms such as structured interviews or observation. Second, all the data came from one single university in Hong Kong, and the students were enrolled in credit-bearing service-learning subjects within the same curricular framework. The cross-sectional nature of the study is also a limitation. Hence, generalizability of the findings should be viewed with caution. Additionally, after showing that learning experiences are significant predictors to motivation, it would be helpful to understand what particular learning experiences have a larger effect on motivation, and whether there are other factors that influence it. Therefore, a future research direction might expand the dimensions of learning experiences to look for causal relationships with students’ motivation. We will also consider other potential variables, such as student demographic data, learning style, personality, or service nature, to enrich our model.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by the Human Subjects Ethics Sub-Committee, Research and Innovation Office (RIO) The Hong Kong Polytechnic University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

KL performed the data collection, statistical analysis, and wrote the first draft of the manuscript. All authors contributed to the conception and design of the study and manuscript revision, read, and approved the submitted version.

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.

This project was partially financially supported by Grant 15600219 from the Hong Kong Research Grants Council.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2022.825902/full#supplementary-material

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• Research article
• Open access
• Published: 18 April 2019

Student motivation to learn: is self-belief the key to transition and first year performance in an undergraduate health professions program?

• Susan Edgar   ORCID: orcid.org/0000-0001-5728-2369 1 ,
• Sandra E. Carr 2 ,
• Joanne Connaughton 1 &
• Antonio Celenza 2  

BMC Medical Education volume  19 , Article number:  111 ( 2019 ) Cite this article

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Student motivation to learn has been undervalued to date though has been identified as an area influencing student success and retention at university. The transition into university has been highlighted as a key period affecting student outcomes as well as well-being. Early identification of those students at risk may assist the transition for many students moving into higher education. Previous research has identified the Motivation and Engagement Scale – University/College (MES-UC) as a valid instrument for measuring motivation to learn in physiotherapy students. The aim of this study was to determine the relationship between a student’s motivation to learn on entry into an undergraduate physiotherapy program and their performance through first year. The relationship of admissions scores, to motivation to learn on entry, were also considered, to determine any link between these measures.

An observational longitudinal study was conducted on one cohort of undergraduate physiotherapy students commencing their studies in 2015 with a response rate of 67%. Correlations were performed between admission variables and Year 1 MES-UC scoring; and between Year 1 MES-UC scoring and subsequent academic performance across first year, taking into consideration gender and age.

Self-belief was identified as the key dimension of motivation influencing student success in the transition into university. Results identified the link between self-belief scores on entry and academic performance in first year, including grade point average and performance in six of nine courses. Courses where there was no significant relationship were identified as curriculum areas where students may be less motivated. There was a relationship between the admissions interview and MES-UC scoring, demonstrating a link between non-cognitive selection measures and student motivation to learn on entry into the program.

Motivation to learn and specifically self-belief with learning, may be influential in the transition into higher education. Undertaking measures of academic motivation may be useful to determine student engagement with curriculum, through identifying any link between student self-belief and performance in specific courses. Changes to curriculum based on student motivation as well as targeting early those students with reduced self-belief may improve student success, psychosocial wellbeing and retention.

Peer Review reports

There is an increasing focus on identifying factors that improve student retention at university. Completion rates and direct measures of student satisfaction and engagement have been identified as possible indicators for future performance funding in Australian higher education [ 1 ]. A review of student dropout and completion in higher education in Europe identified study success as an important issue for future policy development [ 2 ]. It was noted that research into study success impacting on completion rates and retention was limited. In a study reviewing the reasons for students leaving higher education [ 3 ], three broad factors were identified as affecting student retention: learner characteristics including motivation and cognitive abilities; external factors including the current job market and family commitments; and institutional factors including teaching quality and interactions with peers and staff. In a review of high achieving medical students’ thoughts on key factors influencing their success, four key areas were identified: motivation; learning strategies; resource management and dealing with non-academic external problems [ 4 ]. Motivation has been identified as an important contributor to student success as well as influential in determining student retention in higher education.

From a psycho-educational perspective, ‘motivation to learn’ has been described as a student’s ‘energy and drive to learn, work effectively and achieve to their potential’, in addition to the behaviours associated with this energy and drive [ 5 ]. Kusurkar et al. [ 6 ] highlighted that higher education curriculum to date has been guided predominantly by cognitive approaches rather than by motivation theory, concluding that motivation to learn has been undervalued thus far. In the Association for Medical Education in Europe (AMEE) guide ‘Motivation in medical education’ [ 7 ], it is noted that motivation is under-researched in the health sciences due to the assumption that students who enter professional courses such as medicine are highly motivated. Placing importance on ‘why’ students learn as well as ‘what’ and ‘how’, may guide educators in their teaching approaches and ultimately influence student outcomes including retention.

Multiple theories as well as dimensions or factors of motivation have been presented in the literature [ 6 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Cook and Artino’s review [ 8 ] recommended additional research on academic motivation specific to health professions education and enhanced transparency with researchers identifying the ‘lens’ of motivation they are investigating, to improve clarity, application and replication. The lens or conceptual approach that has been adopted for this research is the model of academic motivation developed by Martin and represented in the Motivation and Engagement Wheel [ 5 , 16 , 17 , 18 ]. The Motivation and Engagement Wheel [ 19 ] is a framework representative of positive and negative motivation and engagement dimensions. Positive motivation or cognitive dimensions include self-belief, valuing and learning focus. Pajares [ 20 ] noted that a person’s efficacy beliefs are linked to their effort, perseverance and resilience when completing tasks. These behavioural outcomes are also present in the adaptive behavioural dimensions of the Motivation and Engagement Wheel represented by Task Management, Planning and Persistence. Negative motivation dimensions include anxiety, failure avoidance and uncertain control. Negative engagement dimensions include self-sabotage and disengagement. The Motivation and Engagement Wheel and associated scales are supported by contemporary motivation theories [ 19 ], resulting in a broad, comprehensive instrument.

Martin designed a suite of Motivation and Engagement Scales (MES) based on the Motivation and Engagement Wheel, for respondents to contextualise to their current academic or work situation. The scales demonstrated equal validity across different domains from school to university and into the workplace [ 21 ]. The Motivation and Engagement Scale – University/College (MES-UC) has been validated for the university student population and has been found to be reliable (Cronbach’s alpha 0.78) in research conducted on undergraduate students from two Australian universities [ 9 ]. Research undertaken with the MES-UC has predominantly been conducted in the last five years with an increasing focus by researchers on utilising the instrument to both measure learner motivation and predict subsequent achievement.

Research to date has shown links between aspects of motivation as measured by the MES-UC and student typologies, adaptability and performance in their first year of university [ 22 , 23 , 24 ]. In a recent study focusing on the behavioural or engagement factors from the MES-UC, a relationship was seen between negative engagement in first year university students and lower semester one Grade-Point Average (GPA) for 186 undergraduate psychology students [ 23 ]. Similarly, Wurf and Croft-Piggin [ 24 ] studied the influence of MES-UC scoring early in course on first year achievement, alongside academic score on entry (via the Australian Tertiary Admission Rank or ATAR) and emotional intelligence. The MES-UC, applied at week four following commencement, was the most powerful predictor of academic achievement, greater than ATAR on entry, and accounting for 21% of the variance in the regression model. Transition into higher education, particularly post-secondary education transition, has been identified as a period of significant psychosocial adjustment with research to date acknowledging the psychological, cognitive and affective changes that student’s experience [ 25 , 26 ]. Exploring how motivation to learn impacts on the first year of higher education is key to understanding the contribution of motivation to student transition, achievement and retention.

A preliminary proxy longitudinal study was conducted to review physiotherapy students’ motivation to learn, as measured by the MES-UC [ 27 ]. This study provided the first data identifying mean values for motivation dimensions for health professional students using the MES-UC. Results demonstrated the validity of the MES-UC instrument in measuring motivation and determining differences between demographic and year groups. The results, taken from 233 students, representing 82% of Physiotherapy students enrolled in a Western Australian program, identified some concerning issues including higher levels of anxiety in female students compared to males across all year groups. Disengagement from studies was also noted as a concern for male first year students, highlighting the need to investigate motivation to learn as a standalone factor influencing transition and subsequent first year performance.

The aim of this study was to determine the relationship between a student’s motivation to learn on entry into an undergraduate physiotherapy program and their progress and performance through first year, taking into consideration gender and age. The relationship of co-variables, including admissions scores and educational background, to motivation to learn on entry, were also considered.

Specifically, the following research questions were addressed:

What is the relationship between educational score on entry, background (school leaver versus mature age), admissions interview score and a student’s motivation to learn on entry into an undergraduate physiotherapy program, as determined by the MES-UC?

What is the relationship between a student’s motivation to learn on entry, as determined by the MES-UC, and subsequent first year performance? Which dimensions of motivation, as measured by the MES-UC, may enhance or negatively impact academic performance in the first year of a physiotherapy program?

Understanding the individual motivation dimensions that may influence learning and implementing appropriate interventions may improve both student motivation to learn and retention rates. Further, lower motivation levels have been associated with increased distress in medical students [ 28 ]. Facilitating improved student motivation to learn may have the added role of enhancing student wellbeing. Early identification of those students at risk may assist the transition for many students moving from secondary to higher education. Lessons learned from this study will benefit localised translation into practice, informing other institutions looking to utilise outcomes measures to identify factors influencing student success and retention.

Population and recruitment

This research is part of an observational longitudinal study with one cohort of undergraduate physiotherapy students from a Western Australian university, surveyed on entry into the four-year program in 2015. The cohort were subsequently surveyed every year until program completion in 2018. Participants in this study were recruited at the end of a teaching activity in week three of semester one in 2015, allowing maximal separation from assessment items to minimise any influence of assessment stress.

The researcher distributed hard copy participant information sheets, consent forms and surveys to students and invited them to drop their completed or non-completed surveys in a collection box at the rear of the lecture theatre following teaching activities. Students who consented to participate recorded their student number as an identifier as well as year level, age and sex. There was no incentive to participate and the researcher did not play any role in the assessment of the student cohort.

Consent to add admissions data, including educational and interview scores, to the study was sought retrospectively, as admissions scores were later deemed to be pertinent co-variables to consider. The educational score on entry is calculated from either an applicant’s ATAR, for school leavers, or their GPA of previous undergraduate studies, for mature age applicants. The interview score is calculated from performance at a semi-structured admissions interview, with questioning including aspects of an applicant’s motivation to study physiotherapy. It is an integral component within the selection process, accounting for 40% of overall admissions scoring, once applicants pass initial academic screening. Individual consent was sought for the addition of admissions data from students still enrolled in the university with a waiver of consent approved for students no longer enrolled ( n  = 7). Six students did not provide consent for the addition of admissions data. Data were sourced from existing admissions spreadsheets at the School of Physiotherapy.

The MES-UC is a 44-item instrument incorporating 11 dimensions of motivation, each represented by four items in the instrument, rated on a scale of 1 (strongly disagree) to 7 (strongly agree). The 11 motivation scores are grouped into four global domains. ‘Global booster thoughts’ includes scoring for self-belief, valuing and learning focus items. ‘Global booster behaviours’ includes planning, task management and persistence item scoring. For each global booster score and its individual dimensions, higher scores are more ideal. ‘Global mufflers’ represents scoring for the anxiety, failure avoidance and uncertain control items. ‘Global guzzlers’ includes item scoring from self-sabotage and disengagement dimensions. For ‘global mufflers’, ‘global guzzlers’ and their individual dimensions, lower scores are more ideal. Each of the 11 dimensions within the scale convert to a raw score out of 100.

Data analysis

MES-UC survey results were collated in Microsoft® Excel before being transferred to IBM SPSS® Statistics Version 24.0 for analysis, with recording of all 44 items, 11 first order motivation dimensions, as well as scoring for each of the four higher order domains, per student. Admissions data including educational score on entry, interview score and background (school leaver/mature age) were also collated. The educational score was calculated by the School of Physiotherapy based on either a student’s predicted ATAR for school leavers, or a student’s previous undergraduate performance in a partially completed or completed undergraduate program for mature age students. A predicted ATAR score was calculated for each school leaver applicant by the university admissions office based on subjects studied and academic performance in the previous three semesters of school work. School leavers with a predicted ATAR of 94 or greater scored 40 out of 40, decreasing to 10 out of 40 for students scoring 85 or below. Mature age applicants who completed an undergraduate degree in a related field with a distinction/high distinction average scored 40 out of 40, decreasing to 10 out of 40 for those students having completed less than one year with a credit average. Academic results were also collated including the overall mark for every course of study undertaken and semester and year level Grade Point Average (GPA). Scatterplots were created to explore the linearity of data. The educational score was the only variable determined to not be linear in nature, due to clusters of data at extremes.

Descriptive statistics were undertaken to determine the mean, standard deviation (SD) and range of age on entry, as well as proportions of sex and background for the 2015 cohort. The mean, SD and range of admissions variables were also determined with a comparison of means conducted with a one-way ANOVA per gender and background.

Bivariate correlations were performed between the admission variables of educational score and interview score and Year 1 MES-UC scoring; and between Year 1 MES-UC scoring and subsequent performance in the program, as determined by course marks in academic units and semester and year level GPA. Spearman’s rank correlation coefficients were calculated for the educational score versus outcome variables; Pearson correlation coefficients were performed for the interview score versus outcome variables; and Point biserial correlation coefficients were calculated for the dichotomous variable background and outcome variables. A univariate analysis of variance was performed adopting a general linear model, to determine the effect of variables including each motivation factor, on subsequent performance, controlling for the variables of age and gender. Significant findings were determined by a p -value of less than 0.05.

Descriptive analysis

The sample population entering first year in 2015 included 83 students, 51 (61.45%) female with an age range of 17 to 52 years (mean 19.87; SD 4.71) on entry. The cohort comprised 49 (59.04%) school leavers, with 34 (40.96%) mature age students having completed part or all a previous undergraduate degree program. Fifty-five first year physiotherapy students completed the survey representing 67% of the starting cohort with 33 (60%) females and 32 (58.18%) school leavers. The mean age was 19.91 (SD 5.32) with an age range of 17–52 years. The sample of participants were representative of the broader population of students in first year of the program.

Admissions scores and motivation to learn

The mean educational score on entry was 32.31 (SD: 5.40) with scores ranging from 10 to 40. Females (mean: 33.48; SD: 4.17) scored higher than males (mean: 30.53; SD: 6.58) though this was not significant ( p  = 0.062). Interview scores for the cohort ranged from 20.5 to 40 (mean: 32.36; SD: 4.28). Males (mean: 33.92; SD: 4.12) scored significantly higher than females (mean: 31.30; SD: 4.11), ( p  = 0.038). Table  1 shows the relationships between admissions scores on entry and dimensions and global scores of the MES-UC as completed by students in week three of first semester. There was a significant correlation between educational score and student disengagement ( ρ  = 0.309; p  = 0.033). The interview score correlated with scoring in three of the four global scores as well as three individual dimensions, with a further four dimensions trending towards significance. There was a negative relationship between interview score and student disengagement ( r  = − 0.406; p  = 0.005). School leavers scored significantly higher, comparative to mature age students, in the dimensions of uncertain control ( r pb  = 0.352; p  = 0.008) and self-sabotage ( r pb  = 0.275; p  = 0.042).

Motivation to learn and student performance in first year

Mean motivation scores and standard deviations for the cohort and per sex are presented in Table  2 . Anxiety and task management were the only dimensions on the MES-UC to show a difference between genders, with females scoring significantly higher in both. When determining differences between backgrounds, two motivation dimensions had significant differences. School leavers scored higher for uncertain control (mean: 48.56; SD: 16.10) compared to mature age students (mean: 37.61; SD: 12.31; p  = 0.008). Self-sabotage was also higher in school leavers (mean: 31.16; SD: 13.56) compared to mature age students (mean: 24.39; SD: 8.98; p  = 0.042).

The mean first year GPA for the cohort was 2.29 (SD: 0.72) with no significant difference between male students (2.39; SD: 0.71) and female students (2.21; SD: 0.73; p  = 0.368). Mature age students (mean: 2.56; SD: 0.76) had a higher first year GPA compared to school leavers (mean: 2.10; SD: 0.64) and this was significant ( p  = 0.021).

The results of the univariate analysis of variance are presented in Table  3 showing the effect of variables including each motivation factor and global score, on first year GPA, controlling for the other variables. Self-belief was the only motivation dimension to have a significant effect on first year GPA ( p  = 0.014). The effect of self-belief, controlling for gender and age, on all aspects of academic performance in first year, are presented in Table  4 . There was a significant relationship between self-belief scoring on entry and academic performance in three out of four first semester courses and three out of five second semester courses.

Six students exited the course by the end of first year. A comparison of means between those students who stayed and exited the program revealed no significant differences in their motivation dimensions on entry into the program.

The first aim of this research was to determine the relationship between students’ background and educational and interview scores on entry, and their motivation to learn as measured by the MES-UC in week three of the physiotherapy program. Of note, for this sample, there was no relationship between academic entry scores and scoring on the MES-UC, with the exception of scoring for the disengagement dimension where there was a positive relationship between academic entry scores and disengagement from learning. Given the small sample size, this may be a chance finding although disengagement from learning in first year students was also previously noted in study of a larger sample of physiotherapy students from the same university [ 27 ]. The authors postulate that the transition into the higher education learning environment as well as possible alternate aspirations for some high achieving students, including progression into the medical program, may be possible explanations for this finding.

The admissions interview correlated with three of four global scores including a positive relationship with booster thoughts and behaviours and a negative relationship with disengagement and the global behavioural score representing ‘guzzlers’. Applicants selected for interview have undertaken academic screening and have reached a threshold of academic performance deemed appropriate to complete academic tasks within the physiotherapy program. Thus, students enter with similar academic capabilities. Differentiating students that may be more motivated to learn and progress through the program, is much more difficult to determine on entry but the link between admissions interview and MES-UC does confirm that for this sample, the interview may be targeting alternate factors outside of cognitive ability. Previous research has shown a relationship between the admissions interview for this program and performance in clinical placements in Years 2–4, stronger than academic scores on entry [ 29 ]. Determining any link between academic motivation and performance though the program, including clinical performance, may be useful to determine the value of monitoring student motivation in future cohorts. Monitoring of students was highlighted as a key institutional activity to improve study success, in a report into student dropout and completion in higher education in Europe [ 2 ].

The second aim of the study was to determine any relationships between the dimensions of academic motivation and student performance, taking into consideration gender and age. Although gender differences in achievement at university have previously been identified in the literature [ 29 , 30 , 31 , 32 ], anxiety and task management were the only motivation dimensions to show any significant gender differences, with females scoring higher in both areas. Of note there was no link between either of these motivation dimensions and student performance so although they may have affected motivation to learn, they did not influence subsequent outcomes in first year. Anxiety towards learning may bring about enhanced task management to avoid failure [ 33 ], thus the two dimensions may have worked together to ensure satisfactory academic outcomes.

Significant relationships were found between self-belief and results in three out of four semester one courses and three out of five semester two courses. There was no relationship between the other 10 motivation factors and student performance. Self-belief, as represented on the MES-UC, denotes a ‘students’ belief and confidence in their ability to understand or to do well in their university/college studies, to meet challenges they face, and to perform to the best of their ability’ [ 31 ]. This definition of self-belief is congruous with ‘self-efficacy’, where students make cognitive judgements of their capabilities [ 34 ]. Zajacova et al. [ 35 ] further termed self-efficacy in the academic context as ‘academic self-efficacy’, referring to a student’s confidence in their ability to complete a particular learning activity or task. Motivation to learn and self-efficacy have an integrated or co-dependent relationship as determined by contemporary motivation theories. In the expectancy value theory of motivation, individuals are more likely to engage in tasks where they have higher self-efficacy or belief about their actions and the likely outcomes that will follow [ 13 ]. In Bandura’s social cognitive theory [ 11 , 36 , 37 ], the perceived importance of the task is central to motivation with self-efficacy underpinning a person’s beliefs about their personal competence. Pajares [ 20 ] noted that a person’s efficacy beliefs are linked to their effort, perseverance and resilience when completing tasks and further highlighted the link between self-efficacy and emotional reactions with decreased self-efficacy leading to stress, depression and/or reduced problem-solving abilities. Likewise, Zajacova et al. [ 35 ] found academic self-efficacy and stress to be negatively correlated. Thus, tapping into a student’s self-efficacy or self-belief may be the key to both improving performance and decreasing stress. Targeting students with lowered self-belief on entry into higher education and providing appropriate intervention, may result in improved student outcomes including retention.

It is important for universities to understand how student self-efficacy interacts with institutional characteristics as this may ultimately influence retention rates. Self-belief scoring was linked to performance in certain first year courses comparative to other courses. Two out of three of the courses where there was no link between self-belief scoring and student performance, were not delivered by the physiotherapy program, with the third course since undergoing substantial changes due to student feedback on curriculum provided through traditional course review processes. This may indicate that measuring academic motivation may be useful to assist curriculum review and feedback. Curriculum development based on motivation theory needs further investigation though Turner [ 38 ] identified the role of developing experiences through the higher education journey based around control, success and improvement, to foster self-belief in students.

The initial transition into university has been highlighted as a key period to provide intervention, with a review of Australian higher education students from eight institutions, undertaken mid-year, revealing that just over a third of first year students reported having difficulty getting motivated to study [ 39 ]. Similarly, a review of psychosocial adjustment of first year college students in the U.S. noted a significant decline in psychological, cognitive and affective well-being in first semester [ 25 ]. The decline plateaued in second semester with the researchers noting that identification and intervention in first semester was paramount. It appears that the key time to measure and implement any intervention to enhance motivation to learn is within the first six months of first year.

The authors acknowledge the limitations of this study, including the small sample size of study participants from one cohort of physiotherapy students from a Western Australian university. Further, this study involved the use of a self-reporting instrument applied at one time point, to determine motivation to learn, based on a framework developed by A.J Martin, supported by contemporary models of motivation theory [ 19 ]. A preliminary proxy longitudinal study determined the validity of this instrument for the population tested [ 27 ].

The literature points towards context and institution-specific research as being the key to understanding the complex construct of student motivation [ 8 , 40 ]. The value of lessons learned from a local study to produce benefits through localised translation into practice, cannot be underestimated. Thus, this study reported on findings from investigating motivation to learn, specific to a physiotherapy program, considering the social context and interplay between a student’s motivation including their academic self-efficacy and the role of localised curriculum, specific to the learner. It is important to note that although the sample size for this study was not large, moderate effect sizes were shown in the correlation findings. Further research will review students’ change in motivation over time, as measured by the MES-UC, as well as relationships between academic motivation and performance throughout Years 2–4 of the program, including clinical performance. This may assist with planning the timing of any proposed intervention to enhance academic motivation during the degree program.

In a sample from one physiotherapy undergraduate program, there is a relationship between the admissions interview score on entry and motivation to learn, as measured by the MES-UC, applied at week three of the program. Self-belief, though not related to other admissions elements, was linked to academic performance in the transition into university, as measured by first year results. Motivation to learn and specifically self-belief with learning, may be influential in the transition into higher education. Consideration of individualised follow-up for students with lowered motivation levels on entry, may be appropriate. Motivation measures, such as the MES-UC, may be pertinent to determine student engagement with curriculum, ensuring that experiences in first year programs foster student self-efficacy with learning.

Abbreviations

Australian Tertiary Admission Rank

Grade-Point Average

Motivation and Engagement Scale – University/College

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Edgar, S., Carr, S.E., Connaughton, J. et al. Student motivation to learn: is self-belief the key to transition and first year performance in an undergraduate health professions program?. BMC Med Educ 19 , 111 (2019). https://doi.org/10.1186/s12909-019-1539-5

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