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Chan Choong Foong , Peng Yen Liew; The relationships between academic motivation and academic performance of first-year chemical engineering students. AIP Conf. Proc. 26 October 2022; 2433 (1): 020012. https://doi.org/10.1063/5.0072668

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Academic motivation is linked to benefits in terms of learning effectiveness. This study investigated motivation of pursuing an engineering degree among first year chemical process engineering students. Forty-six students (n=46) who were in their first week of study completed a self-administered online questionnaire, that is the Academic Motivation Scale (AMS). The results showed that students had higher intrinsic motivation, higher extrinsic motivation and lower amotivation upon enrolling into the degree. Next, students’ academic performance in the first semester was collected. Correlations between motivation and academic performance were studied. The results indicate that extrinsic motivation is correlated significantly with academic performance. Recommendations were made to improve teaching and learning effectiveness, using the Self-Determination Theory perspective.

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Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda

  • Review Article
  • Open access
  • Published: 05 May 2021
  • Volume 34 , pages 39–71, ( 2022 )

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  • TuongVan Vu   ORCID: orcid.org/0000-0001-6700-2439 1 , 2 ,
  • Lucía Magis-Weinberg 3 ,
  • Brenda R. J. Jansen 4 ,
  • Nienke van Atteveldt 1 , 2 ,
  • Tieme W. P. Janssen 1 , 2 ,
  • Nikki C. Lee 1 , 2 ,
  • Han L. J. van der Maas 4 ,
  • Maartje E. J. Raijmakers 1 , 2 ,
  • Maien S. M. Sachisthal 4 &
  • Martijn Meeter 1 , 2  

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The question of how learners’ motivation influences their academic achievement and vice versa has been the subject of intensive research due to its theoretical relevance and important implications for the field of education. Here, we present our understanding of how influential theories of academic motivation have conceptualized reciprocal interactions between motivation and achievement and the kinds of evidence that support this reciprocity. While the reciprocal nature of the relationship between motivation and academic achievement has been established in the literature, further insights into several features of this relationship are still lacking. We therefore present a research agenda where we identify theoretical and methodological challenges that could inspire further understanding of the reciprocal relationship between motivation and achievement as well as inform future interventions. Specifically, the research agenda includes the recommendation that future research considers (1) multiple motivation constructs, (2) behavioral mediators, (3) a network approach, (4) alignment of intervals of measurement and the short vs. long time scales of motivation constructs, (5) designs that meet the criteria for making causal, reciprocal inferences, (6) appropriate statistical models, (7) alternatives to self-reports, (8) different ways of measuring achievement, and (9) generalizability of the reciprocal relations to various developmental, ethnic, and sociocultural groups.

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Introduction

In most countries, motivation for school clearly declines throughout school time (Martin, 2009 ; OECD, 2016 ; Scherrer & Preckel, 2019 ) with individual heterogeneity in changes depending on specific motivation constructs across academic domains (Gaspard et al., 2020 ; Scherrer & Preckel, 2019 ). Given this undesirable decline and the fact that motivation can be targeted by interventions, motivation has long been a central focus of educational psychology. The influence of motivation on achievement is well-documented (Burnette et al., 2013 ; Gottfried et al., 2013 ; Greene & Azevedo, 2007 ; Valentine et al., 2004 ). Yet the reverse relation is also often found, as achievement can affect motivation through experiences of success or failure (Garon-Carrier et al., 2016 ; Guay et al., 2003 ; Jansen et al., 2013 ). A common view is that both the “motivation → achievement” and “achievement → motivation” links exist and that motivation and achievement influence each other in a reciprocal manner over time (Huang, 2011 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Möller et al., 2009 ).

Researchers have been studying this reciprocal relationship between motivation and achievement for at least 20 years (Marsh et al., 1999 ). However, further insights into the nature of the relationship are currently lacking; features such as the direction of causality, behavioral mediating pathways, possible effect of the time scale, and generalizations to different motivation constructs and population groups are currently not well understood. These issues are important not just from a scientific viewpoint, but also from a practical viewpoint. To be able to design the most effective interventions aimed at improving achievement and motivation, we need to improve our understanding of the reciprocity to identify the best timing, duration, content, and appropriate target variables of such interventions, as well as other contextual factors contributing to their success.

Our objective is to summarize the current understanding of motivation-achievement interactions (drawing mainly from the academic motivation literature) and to identify the theoretical and methodological challenges that could inspire further advances to understand such specific features of this reciprocal relationship. While an exhaustive review of the literature is beyond the scope of the current paper (see the Special Issue on Prominent Motivation Theories: The Past, Present, and Future on Contemporary Educational Psychology, edited by Wigfield and Koenka, 2020 ), we start with a summary of how influential theories of academic motivation have conceptualized reciprocity between motivation and achievement, and the types of empirical evidence that have been found in support of the reciprocal relationships. In our current understanding, we have found areas of consensus, but have also identified sizable gaps. This leads to a recommended research agenda for future empirical studies on the reciprocal relations between motivation and academic achievement and suggestions on how these insights could inform future interventions.

Reciprocal Relations in Theories of Academic Motivation

Commonalities between theories.

Individual differences in academic achievement are partly the result of differences in motivation for learning (Arens et al., 2017 ; Burnette et al., 2013 ; Eccles & Wigfield, 2002 ; Guay et al., 2003 ; Huang, 2011 ; Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Robbins et al., 2004 ; Seaton et al., 2014 ). This robust finding has spawned a wealth of theories on academic motivation and how to stimulate it. These theories differ in both substance and focus, but also have many common elements. Figure 1 represents an attempt to synthesize, for the purposes of this paper, some of the commonalities of well-established theories that have had an impact in the field of academic motivation (leaning strongly on the seminal review of Eccles & Wigfield, 2002 and adding theories that have gained traction since). Our goal is not to comprehensively review and synthesize the existing theories (although this is an urgent task, Koenka, 2020 ), but rather to illustrate how the commonalities between the theories suggest a framework in which the reciprocal relationships between motivation and achievement can be studied and understood.

figure 1

The motivation-achievement cycle, a summary model of motivation-achievement interactions, capturing some of the commonalities within prominent theories of academic motivation. Blue boxes denote motivation constructs, green (dotted) arrows behavioral intermediaries (quality of learning and quantity of learning), and yellow boxes and arrows denote achievement-related constructs (flow and perceived performance). Gray arrows denote outside influences that are themselves not part of motivation-achievement interactions (e.g., cultural and social influences that affect both expectancies and values)

Motivation has up to 102 definitions (Kleinginna & Kleinginna, 1981 ), but is often seen as a condition that energizes (or de-energizes) behaviors. In many theories, motivation results from what can be called an appraisal of the behavior that one is motivated to perform (the word appraisal is rarely used with regard to motivation, but the processes described are akin to those captured in the emotion literature). In that appraisal, two elements are combined (Eccles & Wigfield, 2002 ): the value attached to the behavior and its outcomes, and the expectancy of the likelihood of certain outcomes of the behavior. These two sides, expectancy and value, are explicit in expectancy-value theory (Eccles & Wigfield, 2002 , 2020 ), attribution theory (Graham, 2020 ; Weiner, 2010 ), control-value theory (Pekrun, 2006 ; Pekrun et al., 2017 ), and Dweck’s integrative theory (Dweck, 2017 ).

Many other theories focus either on the value attached to behavior or on expectancies. Theories on the values side of the ledger (goal theories, flow theory, self-determination theory, individual differences theories, and interest theories) focus on interest, goals, needs for relatedness, competence, and autonomy. Theories on the expectancy side, notably self-efficacy theory, control theories, social-cognitive self-regulation theories, and the process-oriented metacognitive model, focus on how students’ beliefs (or perception ) about their competence and efficacy (i.e., academic self-concept, see below), expectancies for success or failure, and sense of control over achievement affect motivation. Different constructs have been studied that tap into these beliefs underlying one’s expectancies, such as academic self-concept, self-efficacy, locus of control, and perceived control.

A motivation construct frequently used to study the reciprocal motivation-achievement relationship is academic self-concept (hitherto, ASC, discussed in further details in section “Different motivation constructs” below) which is how individuals evaluate their ability specifically in an academic domain (Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Shavelson et al., 1976 ). ASC is a component distinct from physical, social, and emotional self-concepts within the multidimensional, hierarchical model of self-concept (Marsh & Craven, 2006 ; Marsh & Martin, 2011 ). ASC is itself also multidimensional and usually measured by the Self Description Questionnaire (Marsh et al., 1999 ; Marsh & O’Neill, 1984 ); its academic subscales tap into general academic self-concept, math self-concept, and verbal self-concept. Much empirical research on motivation-achievement interactions operationalizes motivation as ASC in a certain academic domain, most often in mathematics and verbal subjects such as language and reading (Guay et al., 2003 ; Seaton et al., 2014 ); for meta-analyses and reviews, see Burnette et al. ( 2013 ), Eccles and Wigfield ( 2002 ), Marsh and Craven ( 2006 ), Marsh and Martin ( 2011 ), and Robbins et al. ( 2004 ).

It is worth noting that many theories posit that beliefs about the self (including self-concept and self-esteem and mindset/implicit theory of self attributes) are important causes of human behavior and learning (Bandura, 1997 ; Carver & White, 1994 ; Deci & Ryan, 2000 ; Molden & Dweck, 2006 ). Although the idea that ASC or other beliefs about the self affect achievement has been challenged (see the discussion in Marsh & Craven, 2006 ), there has also been much empirical research in support of it (Burnette et al., 2013 ; Gottfried et al., 2013 ; Greene & Azevedo, 2007 ; and the meta-analyses of Huang, 2011 ; Valentine et al., 2004 ). One suggested pathway is that positive self-beliefs can lead to self-affirmative, self-regulatory, academic behaviors (or achievement behaviors , see below) such as exerting effort, demonstrating persistence, and selecting goals that are conducive to the achievement of academic goals.

Another pathway for beliefs about the self to act as a causal agent on academic achievement, according to self-worth theory (Covington, 2000 ), is that students with positive beliefs about themselves assign high and positive values to academic activities. Academic activities are then viewed as important, intrinsically interesting, of high expected utility and of low cost, which leads to high achievement (Valentine et al., 2004 ). Also, in self-determination theory, feelings of competence are a precursor of intrinsic motivation, again leading to a higher value being assigned to academic activities if one feels competent. This would then lead to behaviors that support later achievement. A recent study of more than 30,000 college students found that need for competence (relative to need for autonomy and relatedness) is the strongest predictor of perceived learning gains (Yu & Levesque-Bristol, 2020 ).

An appraisal of values and expectancies leads to the decision to engage (Cleary & Zimmerman, 2012 ; Kuhl, 1984 ; Schunk & DiBenedetto, 2020 ). According to the self-regulatory account of motivation (Cleary & Zimmerman, 2012 ; Schunk & DiBenedetto, 2020 ), students first identify values and expectancy of learning activities, then engage in self-regulatory processes (self-instruction, attention focusing, task strategies, etc.). Following their performance, students conduct self-evaluations, infer causal attributions, and make adaptive or maladaptive attributions of their successes and failures. This account stresses the importance of metacognition, where students who can monitor their learning processes can then maintain their engagement in the learning cycle.

The appraisal of values and expectancies can also trigger academic emotions, such as pride in achievement, hope, boredom, and enjoyment. Control-value theory (Pekrun, 2006 ; Pekrun et al., 2017 ) describes how such emotions codetermine what are termed achievement behaviors —behaviors that are conducive to the achievement of academic goals. In line with dominant theories of emotion (e.g. Frijda, 1988 ; Lazarus, 1999 ), Pekrun ( 2006 ) assumed that an appraisal of control of the learner and the value of learning activities lie at the basis of academic emotions. For example, if a learner values an academic outcome and believes it is somewhat under his or her control, he or she may feel the emotion of hope. While it is not certain that the same kinds of appraisal lie at the basis of both motivation and academic emotions, it would seem plausible and parsimonious. Indeed, Pekrun ( 2006 ) suggested that this is the case, though he cautioned that more research is needed.

Figure 1 may raise the question of what actually distinguishes motivation from emotions, since both seem to result from an appraisal of the situation, and both energize or de-energize certain behaviors. This is a valid question, and Kleinginna and Kleinginna ( 1981 ) already noted that a sharp line between motivation and emotion is difficult to draw (also see Berridge, 2018 ). Emotions will typically be more temporary than motivation, but this is a fuzzy distinction. Emotions and motivation may also interact. Emotions may for example make a learner assign more or less value to academic activities, or may change the learner’s expectations around their chances of success or failure, which then changes the appraisal that underlies motivation. Literature showing that emotions and academic achievement also form reciprocal relationships over time has recently emerged (Putwain et al., 2018 ).

Pathways from Motivation to Achievement and Vice Versa

While it is generally accepted that motivation affects achievement, it is not completely clear how . Theoretically, two routes can be discerned (see Fig. 1 ). The first is the quantity (frequency and intensity) of academic behaviors aimed at achievement (such as effort, persistence, etc.) (Cury et al., 2008 ; Dettmers et al., 2009 ; Doumen et al., 2014 ; Marsh et al., 2016 ; Pinxten et al., 2014 ; Plant et al., 2005 ; Trautwein et al., 2009 ). As a second route, higher levels of motivation could also be associated with higher quality of academic behaviors; for example, by adopting effective learning strategies, adaptive meta-cognitive strategies, spaced practice, elaboration, retrieval practice, interleaving, dual coding, and so on. Several theories of academic motivation support the idea that higher motivation leads to higher quality behaviors. Both intrinsic motivation (self-determination theory, Deci & Ryan, 2000 ) and interest (interest theories, Alexander et al., 1994 ) have been linked to deeper learning (Alexander et al., 1994 ; Schiefele, 1999 ; Scott Rigby et al., 1992 ). Positive academic motivations have also been suggested to facilitate creative learning strategies (control-value theory, Pekrun, 2006 ), and incremental implicit beliefs (growth mindset) to facilitate mastery-oriented strategies (Burnette et al., 2013 ).

Effects of achievement on motivation may also take two routes. The first is through perceived achievement. Many theories, such as self-efficacy theory (Bandura, 1997 ), expectancy-value theory (Eccles & Wigfield, 2002 ), control theories (Skinner, 1995 ), and attribution theory (Weiner, 2010 ) explicitly suggest that past achievement leads a learner to experience feelings of self-efficacy and perception of control. What matters most in this regard is the learner’s own evaluation of this outcome, for which we use the term perceived performance in Fig. 1 . High perceived performance will thus change the expectancies of learners (i.e., make them trust that good outcomes are attainable), but it may also alter the value attached to learning activities. For example, in self-determination theory, the feeling of competence (strengthened by positive perceived achievement) is a basic need that increases the intrinsic value of learning.

The second route from achievement to motivation is central to flow theory (Csikzentmihalyi, 1990 ). An activity in which the learner is holistically immersed can generate a feeling of flow, which is rewarding in its own right and alters the value attached to the academic behaviors.

External Factors Affecting Motivation, Effort, and Achievement

Figure 1 suggests a positive feedback loop, with motivation feeding achievement, and achievement feeding motivation—an idea that is alluded to in some theories (Cleary & Zimmerman, 2012 ; Eccles & Wigfield, 2002 ; Schunk & DiBenedetto, 2020 ). Most explicit in this regard is the self-regulatory account of motivation (Cleary & Zimmerman, 2012 ) where the pathway between self-regulation and achievement is a cyclical feedback loop. Schunk and DiBenedetto ( 2020 ) suggest an iterative process between perceived progress, self-efficacy, and goal pursuit. Bandura’s social cognitive theory also stresses the reciprocity of the interactions between behavioral, environmental, and personal factors (Bandura, 1997 ). Crucially, this raises the question of how such a positive feedback loop could get started, and how, once started, it could lead to any other outcome than either perfect motivation and achievement, or negative motivation and failure. The answer to those questions may rest in the external influences on motivation and achievement. These are indicated in Fig. 1 by the gray arrows:

Extrinsic rewards and requirements tied to achievement, e.g., schools or parents, may change the value attributed to academic behavior, and so change motivation. Although this has been described in self-determination theory as potentially detracting from intrinsic motivation (Deci & Ryan, 2000 ), it may also jolt a motivation-achievement cycle that would otherwise not start (Hidi & Harackiewicz, 2001 ). Supporting autonomy and creating relatedness are other ways in which external actors can increase the value attached to learning, increasing motivation and achievement (Deci & Ryan, 2000 ).

Cultural norms (described in control theories and control-value theory, Pekrun, 2006 ; Skinner, 1995 ), social learning, and verbal persuasion by others (social cognitive theory, Bandura, 1997 ) can alter the expectations, values, and attributional processes of learners (expectancy-value theory, attribution theories, Eccles & Wigfield, 2020 ; Graham, 2020 ), and therefore keep a motivation-achievement cycle going that would otherwise falter or not start up.

Effort is not only a result of the learner’s motivation but also of outside requirements (e.g., deadlines and exams set by the educational institution, Kerdijk et al., 2015 ). Such outside requirements can lead to achievement in the absence of strong motivation.

Quality of learning is not only affected by motivation but also by the abilities of the learner and the quality of teaching, instructions, and study materials. Thus, achievement can increase in the absence of stronger motivation, because of better support for learning.

Perceived achievement is not only determined by true achievement but also by elements of educational design, such as the form in which feedback is given (e.g., a grade that either accentuates the ranking of the student or the degree to which the study material was mastered, or feedback on effort instead of performance, De Kraker-Pauw et al., 2017 ). Perceived achievement is also subject to interpretative, comparison, and attributional processes (described in attribution theories, Graham, 2020 ; Weiner, 2010 ). This means that true high achievement can still fail to support motivation (e.g., when a sibling performs even better), or low achievement can be viewed in such a way so as to not be detrimental for motivation.

Such external factors are not only important for a complete causal understanding of motivation-achievement interactions (i.e., highly relevant for educational researchers) but also because they offer entry points for interventions that enhance motivation, achievement, or both (i.e., highly relevant for educators).

What Avenues for Empirical Research Have Been Explored?

Figure 1 shows that theories of academic achievement imply a reciprocal relationship between motivation and achievement. A comprehensive review of studies is beyond the scope of this manuscript (see narrative reviews and meta-analyses) (Huang, 2011 ; Marsh & Craven, 2006 ; Scharmer, 2020 ; Valentine et al., 2004 ; Valentine & Dubois, 2005 ), but we will review the kinds of evidence that have been brought to bear in support of such reciprocal relationships. Analyzing this evidence allows future directions on the field to be charted.

The earliest support for the relationship between motivation (focusing specifically on self-concepts and other self beliefs) and academic achievement comes from cross-sectional and correlational studies, reviewed by Hansford and Hattie ( 1982 ). These studies established a relationship between self-concepts and academic achievement, but no causal paths. Subsequent work set out to investigate the causal and temporal ordering of the effects using structural equation models (SEMs) and longitudinal data (e.g., Marsh et al., 1999 ). To date, the majority of evidence for the reciprocal relationship between self-concept and achievement has come from such time-series or cross-sectional data collected at schools, to which various SEMs have been fitted (see Marsh & Craven, 2006 for a narrative review and Huang, 2011 for a meta-analysis of such studies).

More recent studies showcase impressive efforts of researchers to use large sample sizes and longitudinal data of up to six waves, allowing changes in motivation and achievement of students to be tracked across their school career (e.g., Marsh et al., 2018 ; Murayama et al., 2013 ). A recent meta-analysis (Scharmer, 2020 ), which includes such studies that were published between 2011 and August 2020, showed that overall, the pooled effect of achievement on motivation was twice (β = .12) the pooled effect of motivation on achievement (β = .06), though both are what is conventionally considered a small effect. These findings are in line with Valentine and DuBois ( 2005 ) who found that academic achievement had a stronger effect on self-belief than vice versa. In contrast, Huang ( 2011 )’s meta-analysis found a slightly larger effect of self-concept on achievement than the other way around. Valentine and DuBois ( 2005 )’s findings were also more similar to Scharmer’s ( 2020 ) in terms of the size of the effects (achievement on self-belief: β = 0.08; self-belief on achievement: β = 0.15). Huang ( 2011 ), however, found considerably larger ranges of effects overall (achievement on self-concept: β = 0.19–0.25; self-concept on achievement: β = 0.20–0.27).

There have also been interventions and randomized controlled field studies in which either self-concept or other motivation constructs were manipulated (e.g., Savi et al., 2018 ; Vansteenkiste et al., 2004 ), thereby allowing for causal inferences. The meta-analysis of these studies by Lazowski and Hulleman ( 2016 ) showed that, while interventions targeting motivation usually led to positive outcomes on achievement (medium effect size; average Cohen’s d of 0.49), it did not matter which theory was at the basis of the intervention— all theories of motivation performed about equally well. However, experimental studies that look at the reverse causal path, manipulating achievement (or the perception of achievement) to affect motivation, are scarce. One example is an intervention study by Betz and Schifano ( 2000 ) where students were ensured of successful completion of a task followed by affirmation of their accomplishments with applause and verbal praise. This resulted in an increase in self-efficacy (a motivation construct highly related to ASC, Bong & Skaalvik, 2003 ). Nevertheless, to the best of our knowledge, few studies have done both: combining experimental manipulation and longitudinal design to investigate reciprocal motivation-achievement relations (an exception that we are aware of is Bejjani et al., 2019 which will be discussed later).

Research Agenda

The overview given above suggests that empirical evidence for reciprocal relations between motivation and achievement exists. However, several features of such relationships are still poorly understood. Also, some doubts about the robustness of the effects have recently surfaced (which we discuss in detail in section “Choice of appropriate statistical models” below). In other words, there are still unanswered theoretical and empirical questions about the reciprocal relationship between motivation and academic achievement. Below, we outline these issues and a research agenda for future research that can answer these remaining questions. These are organized into questions pertaining to theoretical lacunae, methodological challenges, and questions about the scope of theories and the generalizability of empirical results.

Theoretical Lacunae

Multiple motivation constructs.

First, as we presented above, many motivation theories have implicitly or explicitly conceptualized the relationship between a plethora of motivation constructs and achievement as reciprocal. However, to date, a large amount of empirical research on reciprocal motivation-achievement interactions has mainly studied ASC (Arens et al., 2019 ; Brunner et al., 2010 ; Chen et al., 2013 ; Dicke et al., 2018 ; Gottfried et al., 2013 ; Grygiel et al., 2017 ; Guay et al., 2003 ; Guo et al., 2015 ; Möller et al., 2011 ; Niepel et al., 2014a , 2014b ; Retelsdorf et al., 2014 ; Viljaranta et al., 2014 ; Walgermo et al., 2018 ; for meta-analyses and reviews, see Marsh & Craven, 2006 ; Marsh & Martin, 2011 ; Valentine et al., 2004 ; Valentine & Dubois, 2005 ) . This raises the question of whether findings generalize to other motivation constructs that are related yet could also have a distinctive reciprocal relationship with academic achievement.

Moreover, although the studies involving ASC were groundbreaking attempts to show reciprocal relations, there are several reasons why future studies should contemplate using different motivation constructs other than ASC. First and foremost, ASC and achievement are highly intertwined, as items in ASC questionnaires usually ask students to report on their achievement (e.g., “I get good marks in most academic subjects,” “I learn quickly in most academic subjects” (Marsh & O’Neill, 1984 ). Fulmer and Frijters ( 2009 , p. 228) in their critique of how motivation is measured in educational psychology also made the point that “self-report measures confound the measurement of motivation with other variables, such as ability and attention.”

Second, a meta-analysis investigating mean-level changes of a number of important motivation constructs concluded that the decline in motivation shows non-trivial differences across these constructs (Scherrer & Preckel, 2019 ). An important implication of this finding is that more attention should be paid to differentiation among multiple motivation constructs in future empirical studies.

Third, ASC might also be less malleable than other motivation constructs since general self-concept is relatively stable—especially for those at lower levels (Scherbaum et al., 2006 ). Research into the Big-Fish-Little-Pond phenomenon (i.e., students in high-achieving classes having lower ASC than those with comparable aptitude in regular classes) suggests that domain-specific ASC (more so than general ASC) is influenced by social comparison (Fang et al., 2018 ; Marsh et al., 2018 ). Nevertheless, it may be hard to manipulate ASC in a randomized controlled trial (although it has been indirectly done by affirming general self-esteem and personal values, Cohen et al., 2009 ). Other motivation constructs that can be modified through external influences (e.g., situational interest, perceived control, etc.) might yield useful guidance for designing interventions.

Furthermore, the heavy focus on ASC may reflect an emphasis on a cognitive, intrapsychological theoretical view of motivation while losing sight of social, contextual, historical, and environmental factors that arguably also play important roles (see the Special Issue on Prominent Motivation Theories: The Past, Present, and Future on Contemporary Educational Psychology, edited by Wigfield and Koenka, 2020 ). Last but not least, ASC is mainly self-reported and, despite the availability of well-constructed measures, it suffers from all the caveats inherent to self-report measures (see section “Alternatives to self-reports” below).

Given that there are other well-studied motivation constructs such as achievement goals, self-efficacy, interest, and intrinsic motivation (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ), further research with multiple non-ASC motivation constructs included as concomitant predictors of academic achievement is therefore much needed. In recent investigations of the reciprocal relationship between motivation and achievement, motivation constructs other than ASC have started to be included (e.g., self-efficacy in Grigg et al., 2018 ; Schöber et al., 2018 ; achievement goals in Scherrer et al., 2020 ; intrinsic motivation in Hebbecker et al., 2019 ; and interest in Höft & Bernholt, 2019 ). Yet, these studies are still small in number. Twenty-four out of 41 studies included in the meta-analysis of Scharmer ( 2020 ) still used ASC as the main motivation construct of interest.

Behaviors as Mediating Factors in the Motivation → Achievement Link

As mentioned above, theories of academic motivation imply several pathways through which motivation influences achievement and vice versa (see Fig. 1 ). For the motivation → achievement link, the rationale is that motivation leads to active and effortful commitment to learning (e.g., E. Skinner et al., 1990 ), implying that motivation constructs that are beliefs about competence and efficacy influence achievement by inducing self-regulatory, academic behaviors. In a similar vein, the volition theory of motivation (Eccles & Wigfield, 2002 ; Kuhl, 1984 ) posits that motivational beliefs only lead to the decision to act. Once the individual engages in action, volitional processes are required and determine whether the intention is fulfilled. Thus, self-regulatory processes theoretically mediate the link between beliefs and accomplishment of the task.

However, there is a relative paucity of empirical research and especially longitudinal studies that include measures of such regulatory processes. Usually, when studies found reciprocal relations between ASC and other motivation constructs and achievement, they left unanswered which pathways mediate the link between such beliefs and achievement (Marsh & Martin, 2011 ). To our knowledge, initial attempts to study mediating processes in longitudinal designs (Marsh et al., 2016 ; Pinxten et al., 2014 ; Trautwein et al., 2009 ) yielded mixed findings with regards to the role of effort in the relationship between ASC and academic achievement. This may be due to the fact that there are multiple operationalizations and evaluations of the construct effort (Massin, 2017 ), which may have varying relations with academic achievement. Specifically, Marsh et al. ( 2016 ) and Pinxten et al. ( 2014 ) measured subjective effort—i.e., students were asked to rate their own effort expenditure. Students might perceive that having to try hard (i.e., expending a great deal of effort) is indicative of a lack of academic ability (Baars et al., 2020 ). Subjective effort, as opposed to objective effort, might therefore have a very different relation to motivation and achievement.

In non-longitudinal studies looking at the relations between academic motivation and achievement, the evidence on behavioral mediators also shows differentiation related to how effort is measured. When effort is measured as quality of learning (e.g., selecting adaptive goals, adopting higher-quality learning strategies, etc.), there is some evidence for a positive link between academic achievement and effort (Trigwell et al., 2013 ). However, when effort is measured as a quantity of learning (such as study time, practice time, time-on-task, persistence, etc.), this relationship seems either weak or only significant after controlling for quality of learning (Cury et al., 2008 ; Dettmers et al., 2009 ; Doumen et al., 2014 ; Plant et al., 2005 ) or even negative (the labour-in-vain effect, Koriat et al., 2006 ; Nelson & Leonesio, 1988 ; Undorf & Ackerman, 2017 ). This provides suggestions for future attempts to parse the mediating factors in the motivation → achievement link in reciprocal relations between these two constructs. It is most fruitful to measure subjective and objective measures of quantity and quality of learning (and use triangulation of methods, as strongly suggested by Scheiter et al., 2020 ) and compare their effects on academic achievement.

Irrespective of what operationalization is chosen, it is important to note that it is not trivial to evaluate and conceptualize effort (see extensive discussions in Baars et al., 2020 ; Scheiter et al., 2020 ). Is effort the allocation of cognitive control, i.e., mental effort (Kool & Botvinick, 2018 ), or the intention to think deeply, regardless of the amount of time spent (Haynes et al., 2016 ), or a preference for thinking hard (Beck, 1990 ), a decision process rather than a capacity or resource that is physically limited (Gendolla & Richter, 2010 )? Yet, only by measuring regulatory processes that mediate the motivation → achievement pathway, we can make progress in understanding the underlying mechanism of mutual influences between motivation and achievement.

Mutualistic Perspective and the Network Approach

Next, studies have typically investigated relations between one or a small number of motivation constructs (e.g., ASC and interest, Walgermo et al., 2018 ). The discussion above and Fig. 1 show that multiple motivation constructs are linked to academic achievement, which may also all be mutually related. Like many topics in psychology, there is a huge overlap in terms and variables in the literature on motivation and achievement; the same construct may have different names, or different constructs go under the same name (this is known as Jingle-Jangle fallacies; e.g., Marsh et al., 2003 ). One possible solution to the Jingle-Jangle fallacies with regard to motivation was proposed by Marsh et al. ( 2003 ), who presented a factor model with two higher-order factors (dubbed learning and performance ) that explained relations between motivation constructs. In this approach, assumptions on the number of factors and factor structure are necessary.

The network approach is different; it does not assume an a priori structure of motivation factors. Instead, it uses the (bidirectional) partial correlations between variables in empirical data and in doing so clusters of variables which can be interpreted as constructs may emerge. The idea of a network of mutual relations to model psychological constructs was introduced by van der Maas and colleagues (van der Maas et al., 2006 , 2017 ) as an explanation for the positive correlations (the positive manifold) between intelligence sub-test scores. This led to a productive area of research with applications in many areas of psychology (Dalege et al., 2016 ; Robinaugh et al., 2020 ; Sachisthal et al., 2019 , 2020 ; Zwicker et al., 2020 ). The general hypothesis in psychological network models is that correlations between observed behaviors, such as cognitive functions, psychopathological symptoms, and attitudes (or, motivation constructs ), are not due to unobserved common causes, but to a network of interacting psychological, social, and/or biological factors. These observed behaviors are the nodes in the network and the partial correlations are the edges.

An example of how such a network approach can be applied to the area of motivation can be found in a study of interest in science (Sachisthal et al., 2019 ). This study included measures of students’ value of science, their science engagement, and achievement. The correlations between these measures were modeled as a network, within which clusters of variables emerged. These can be seen as empirically derived constructs, replacing the at times arbitrary theoretical separation between (motivation) constructs. Given that in motivation research many constructs with considerable overlap exist (Anderman, 2020 ; Hattie et al., 2020 ), such empirically derived concepts may prove especially relevant.

Within this network, variables with the strongest direct relationships can be identified. A positive change in a central variable should lead to a positive change throughout the network and these central variables may differ between contexts. For example, enjoyment emerged as the central node in the network of Dutch students, whereas engagement behaviors emerged as central in the network of Colombian students and therefore different approaches for increasing science interest are advised for the two countries (Sachisthal et al., 2019 ). Central variables may be efficient intervention targets as interventions informed by network analyses have been shown to be highly effective as these central variables were later shown to be predictive of subsequent behaviors (e.g., Sachisthal et al., 2020 ). Moreover, further support for this assumption comes from a recent study by Zwicker et al. ( 2020 ) who identified guilt as the central node in the network of attitude and environmental behaviors. They then successfully manipulated guilt which increased willingness to engage in such behaviors.

In sum, these works exemplify how network approaches can be used (1) to model distinctive but highly related motivation and achievement constructs simultaneously and map their relations and (2) to derive hypotheses about which included constructs may be efficient targets for interventions (see Borsboom, 2017 , for an overview). Moreover, the fact that network analyses found different central variables in different populations also showcases how such an approach can flexibly capture interactions between motivation factors in real life. Last but not least, at a more abstract level, a mutualistic network approach can potentially solve the question of the mechanisms of the impact of motivation on achievement (also raised in Hattie et al., 2020 as an important avenue for future research). Specifically, how clusters of motivation constructs, behavior, and achievement interact with one another can be modeled, and how reciprocal relations between them arise over time. This can only be achieved when multiple motivation constructs are measured in one single study (as argued above in section “Multiple motivation constructs”).

Time Scale of the Interactions (Short vs. Long Cycle)

Another gap in the literature that we identified is that much research on the reciprocity between motivation and achievement has been done with data collected at large time intervals, which reflect changes that happen over months or years (e.g., Harackiewicz et al., 2008 ; Marsh et al., 2005 , 2016 ; Nuutila et al., 2018 ). For example, it is common for studies to include data collected per academic semester or year (e.g., Gottfried et al., 2013 ); sometimes, other time intervals have been used, such as weeks (e.g., Yeager et al., 2014 ). However, theories of motivation such as self-determination theory or expectancy-value theory are not formulated with an explicit time scale, and the interactions they describe seem framed in terms that suggest that the effects of motivation constructs happen without delays (i.e., on a time scale of seconds). Recent accounts of motivation are situated ones, which also call attention to fine-grained, moment-to-moment fluctuations that occur during learning engagement (Schunk & DiBenedetto, 2020 ). This raises the question how such fast dynamics can be captured if constructs are measured with large time lags in between.

It is possible that there are interactions between motivation and achievement at both short and long timescales, and that these are qualitatively different. We will refer to these hypothetical interactions at different time scales as short (or fast) and long (or slow) cycles between motivation and achievement. Some constructs may change in slower cycles (e.g., achievement goal orientation, mindset, academic self-concept) than others (e.g., autonomy, or even faster: emotions). In research focusing on interest and achievement emotions, for instance, a stable, so-called trait level (e.g., individual interest) is often distinguished from a shorter, task-dependent state level (e.g., situational interest) (see Hidi & Renninger, 2006 ; Renninger & Hidi, 2011 for interest; Pekrun, 2006 for achievement emotions). Nesselroade’s ( 1991 ) model of within-person psychological change also distinguishes between state and trait. The former is rapid and potentially more easily reversed than the latter. Developmental processes are thought to underlie trait constructs, for instance suggesting that the repeated experience of a positive state (i.e., enjoyment) will lead to a positive trait value. While it has been suggested that reciprocal relations play a more central role on the trait level—e.g., explaining the stronger relations between emotion antecedents and emotions (Bieg et al., 2013 ), studies investigating reciprocal relations are still missing at a state (or task) level . Furthermore, the difference between slow and fast change is also more salient for certain constructs than for others. For example, in one rare study where the within-task changes in multiple motivation constructs was studied, researchers found that while students’ self-efficacy generally grew throughout the progress of a task, interest did not (Niemivirta & Tapola, 2007 ). This suggests that when studies do not consider fast vs. long cycles of constructs, the effects of a faster changing variable on a slowly changing variable can be missed.

The remedy to these problems is to consider using data collected at either diverse time intervals or with theoretically informed time intervals to capture the ebbs and flows of the relations between constructs over time and their corresponding short and long cycles (Duff et al., 2015 ; McNeish & Hamaker, 2019 ). In addition, special attention should be paid to “short cycles”—especially since fast-changing constructs may be more effective targets for interventions. Intensive longitudinal designs can help uncover potential “real-time” causal variance attributable to a construct that would be missed when it is measured at relatively lengthy intervals such as one academic semester or year (McNeish & Hamaker, 2019 ). This may also help when developmental trajectories are characterized by non-linear trends that cannot be captured by low-frequent measurements (McNeish & Hamaker, 2019 ). A deliberate choice of time intervals and the use of non-questionnaire measures will also be helpful in this respect (see section “Alternatives to self-reports” below).

A related but distinguishable issue is the stability of the reciprocal relation between motivation and achievement. Whether or not reciprocal effects of motivation and achievement are stable across school careers is a question with significant theoretical and practical consequences (Marsh et al., 2018 ). Two recent studies found motivation declines to be associated with particular academic stages, for example some constructs such as achievement goal orientation specifically dropped in the transition to secondary school (Scherrer et al., 2020 ). The Scherrer et al. ( 2020 ) data are however among the first longitudinal attempts that can reveal how such declines could potentially impact the reciprocity between motivation and achievement. Theoretically, one could assume that the impact of motivation on achievement is low early in a new environment (e.g., a school transition) where learners experience considerable uncertainty regarding their competence and academic standing (Eccles et al., 1993 ; Valentine et al., 2004 ). When the learning environment is stable, the impact of achievement on subsequent motivation might be more substantial. Some support for such a pattern is provided in Scherrer et al. ( 2020 ) who found the reciprocal effects only in later time points and not in earlier time points after transition into secondary school. However, these studies were not designed specifically to test the transition vs. non-transition contrast, prompting the need for subsequent longitudinal studies that focus on the effect of school transition (to our knowledge, Rudolph et al., 2001 is among the first but only has two waves of data).

Methodological Challenges

When extant research finds the relationships between motivation and achievement, the interpretation with regards to causal relations remains difficult due to the lack of experimental manipulation (Granger, 1980 ; Holland, 1986 ; Marsh et al., 2018 ; Mega et al., 2014 ). In almost every study investigating reciprocal motivation and achievement relations, the need for experimental designs in which either motivation or achievement is manipulated is raised as a suggestion for future research (Marsh et al., 2016 , 2018 ; Mega et al., 2014 ; Pinxten et al., 2014 ). The term “effect” in many existing studies is used only in “conventional statistical sense and standard path analytic terminology, as representing a relation that is not necessarily causal” (Marsh et al., 2018 , p. 268).

Research that aims to establish causality in the reciprocal relationship between motivation and achievement would need to meet three preconditions. The first precondition of causality is order , that is “x must precede y temporally” (Antonakis et al., 2010 , p. 1087). Causality of reciprocal effects requires both orders (x precedes y, y precedes x), as well as alternations of x and y (x precedes y, which is again followed by x). The pale blue (with solid outline) squares in Fig. 2 show this alteration of measurements of motivation and achievement. The top pale blue rectangle starts with motivation, whereas the bottom starts with achievement. The second precondition is correlation : “x must be reliably correlated with y (beyond chance)” (Antonakis et al., 2010 , p. 1087).

figure 2

Representation of three types of study designs that can investigate the relationships between motivation and academic achievement. (1) The gray box shows that to establish that motivation causes academic achievement (top) or vice versa (bottom), experimental manipulation is needed, intervening on the predictor at time point 1, which influences the outcome at time point 2 and so on. The straight thin arrows are the cross-lagged relations and the curved arrows the autoregressive relations. (2) The light blue boxes (top and bottom) illustrate the types of design where reciprocity but not necessary causal effects between motivation and achievement can be established. (3) The green boxes (top and bottom) show the type of design that can investigate both reciprocity and causality between motivation and achievement (i.e., a study where experimental manipulation is included and reciprocal relationships are measured). t time-point, M motivation, A achievement

Several studies with high quality and quantity of longitudinal data meet these two pre-conditions (e.g., Arens et al., 2017 ; Bossaert et al., 2011 ; Chamorro-Premuzic et al., 2010 ; Chen et al., 2013 ; Collie et al., 2015 ; Dicke et al., 2018 ; Grygiel et al., 2017 ; Hebbecker et al., 2019 ; Höft & Bernholt, 2019 ; Marsh et al., 2016 , 2018 ; Miyamoto et al., 2018 ). In these studies, autoregressive paths (the curved arrows in Fig. 2 , which go from measurement of a variable at one time point to the measurement of the same variable at the next time point) and cross-lagged paths (the straight arrows in Fig. 2 , which go from measurement of a variable at one time point to the measurement of a different variable at a later time point) are found. In other words, autoregressive paths represent the direct effects of variables on themselves over time and cross-lagged paths the direct effects of two variables on each other over time. Such cross-lagged paths show the reciprocity between the variables but not necessarily causality in these relations (Usami et al., 2019 ). Correlation between different variables, measured at different time points, is a necessary but not sufficient requirement of causality in mutual relations. Establishing causality of reciprocal effects requires the experimental manipulation of at least one of the two variables.

Importantly, to our knowledge, no studies of the mutual relations between motivation and achievement also satisfy the third precondition of causality, that is the manipulation of x has an effect on y at a later time point, followed by (a) repeated measure(s) of x (and y) (Antonakis et al., 2010 ). In Fig. 2 , manipulation is indicated by the thick arrow. In the upper panel of Fig. 2 , the manipulation of motivation affects achievement in the gray (with dash outline) part of the figure. If the manipulation is followed by an alteration of the variables with cross-relations, the findings would support causality of motivation in reciprocal relations between motivation and achievement. We searched for such studies in meta-analyses of interventions (Harackiewicz et al., 2014 ; Lazowski & Hulleman, 2016 ; Sisk et al., 2018 ), in the latest meta-analysis of longitudinal studies (Huang, 2011 ) and Scharmer ( 2020 ). We encountered two studies that contained both an experimental manipulation of a motivation construct and subsequent multiple, alternate measurements of motivation and performance. Cohen et al. ( 2009 ) found that structured writing assignments to prompt African American students to reflect on their personal values (i.e., self-affirmation interventions) resulted in improved academic achievement (GPA), as well as self-perception and an increased rate of remediation, in the following school year for low-achieving African Americans. Yeager et al. ( 2019 ), in a large-scale mindset intervention, also had more than one wave of manipulated motivation and measurement of achievement. Although the authors discuss the role of a recursive process Yeager & Walton, 2011 ) , neither of these interventions modeled reciprocal effects between motivation and performance (Cohen et al., 2009 ; Yeager et al., 2019 ).

In the lower panel of Fig. 2 , the arrow indicates manipulation of achievement. A manipulation of achievement that affects motivation, which is again cross-related to achievement, would support a causal effect of achievement in reciprocal relations between achievement and motivation. However, it is hard to manipulate achievement independently from motivation. For example, manipulations of instruction, modeling, practice, and self-correction may all affect achievement, but they may do so partly by making the material more appealing, raising motivation at the same time or before achievement is raised. New manipulations are needed that raise, for example, perceived performance without raising performance per se, as a way to circumvent such issues. For causal inferences, experiments would ideally include (double-blinded) random assignment, which is possible in the lab but poses important practical problems in the classroom (cf. Savi et al., 2018 ). In sum, future research with the types of studies that can investigate both reciprocity and causality between motivation and achievement would be highly valuable.

Choice of Appropriate Statistical Models

Although the existence of the reciprocal relationship between motivation and performance is generally agreed upon, there are also empirical works that fail to establish such a relationship (Fraine et al., 2007 ) or cast doubts on the robustness of the reciprocal effects (Burns et al., 2020 ; Ehm et al., 2019 ). Such studies most importantly also point out that the choice of sophisticated statistical models to investigate such relationships can have implications for the conclusion drawn (e.g., Burns et al., 2020 ; Ehm et al., 2019 ). Ehm et al. ( 2019 ) specifically found that although a cross-lagged panel model (CLPM) supported reciprocal motivation-achievement relations, other models did not—such as the random-intercept CLPM, which Hamaker et al. ( 2015 ) showed to be more effective than CLPM in explicitly modeling within- and between-individual changes across time. In addition, as Usami et al. ( 2019 )—in their comprehensive unified framework of longitudinal models—demonstrated, it is important to identify the existence of third time-varying or time invariant variables (such as stable traits) that can have a causal effect on the longitudinal relationship but are yet accounted for in a model. Substantial knowledge about such confounders will help researchers select the correct statistical model. Again, this issue is closely related to the short vs. long cycle of the constructs discussed above.

Alternatives to Self-Reports

Most studies investigating reciprocal relationship between motivation and achievement have measured motivation through questionnaires probing ASC (e.g., the Academic Self-Description Questionnaire by Marsh & O’Neill, 1984 ). Despite their evident psychometric benefits, self-reports (including questionnaires) of motivation suffer from many inherent caveats. Fulmer and Frijters ( 2009 ) list several that are relevant. First of all, questionnaires are subjective and rely on the assumption that motives are consciously accessible, declarative, and communicable to other people, while as discussed above, motivation arises from partially inaccessible and non-declarative cognition and emotions. Students may also differ in their capacity to reliably answer the questions (e.g., consider alexithymia—a psychological trait that is characterized by difficulties with verbalization of one’s own emotions and psychological introspection, Lumley et al., 2005 ). Second, the lack of rigor in the conceptualization of motivation constructs often becomes apparent when using questionnaires (we discuss concrete issues related to ASC in the Different Motivation Constructs section). This is closely related to the Jingle-Jangle Fallacies discussed in Marsh et al. ( 2003 , p. 192). Third, questionnaires might not measure reliably motivation constructs that are not trait-like and subject to temporal and situational fluctuations (e.g., situational interest) (also see our discussion of this point in Time scale of the relations section above). In practice, self-reports cannot be sampled with high frequency during learning (see process-oriented measures below). Fourth, questionnaires are problematic from a developmental perspective because, across age groups, there might be varying factor structures in empirical data. Furthermore, some children may be too young to process some motivation constructs. Finally, self-reports are sensitive to demand characteristics and a tendency to give socially desirable answers (e.g. students who are familiar with the implicit theory of intelligence might tend to report that they endorse a growth mindset, Lüftenegger & Chen, 2017 ).

Most recent discussions of motivation-achievement interactions emphasize the need for alternative methods to self-report questionnaires. These alternatives include experience sampling, daily diaries, think-aloud protocols, observations, and structured interviews (Eccles & Wigfield, 2020 ). These alternatives have their strengths, but some limitations remain, such as the subjective nature of these measures and a possible high demand on research participants’ cognitive resources when a large number of measures are administered during a session. In addition, some demand frequent small breaks during a task to report internal states, which may interfere with the flow of the task.

Several alternative methods are available to observe and measure motivation or engagement “online” during learning, for example by using frequent choices of learners or video observations (Järvenoja et al., 2018 ). With the development of new technologies, it is now also possible to collect such data longitudinally on a large scale. For example, MathGarden, an online math learning tool, provides access to math learning data of thousands of students. Motivation is indexed by the frequency and length of voluntary, self-initiated practice, and can be linked to learning and performance (Hofman et al., 2018 ). Other promising process-oriented measures are eye-tracking and facial emotional expressions (D’Mello et al., 2008 ; Grafsgaard et al., 2014 , 2011 ; Nye et al., 2018 ; van Amelsvoort & Krahmer, 2009 ).

Another process-oriented approach uses physiology for high-frequency and non-interfering measures of motivational states. We will briefly discuss the use of autonomic nervous system (ANS) and central nervous system (CNS) measures. ANS techniques can be used to measure arousal , which is defined as higher activation of the sympathetic relative to the parasympathetic system. Motivated and effortful behavior is accompanied by increased arousal, and thus ANS techniques can provide an index of motivation. Popular techniques are electrodermal activity (EDA), electrocardiograms (ECG), and impedance cardiography (ICG). Sympathetic arousal measured with EDA has been associated with emotion, cognition, and attention (Critchley, 2002 ). Sympathetic arousal can also be measured with pre-ejection period (Tavakolian, 2016 )—which is the time in between “the electrical depolarization of the left ventricle and the beginning of the ventricular ejection” (Lanfranchi et al., 2017 , p. 145). One shared challenge with EDA and ECG is that arousal is a “fuzzy” construct, meaning many things, yet nothing specific (Mendes, 2016 ). A common factor that elicits EDA is subjective salience or motivational importance . Pre-ejection period is often used as an index for effort mobilization in studies investigating motivational intensity theory (Brehm & Self, 1989 ). Suppression of parasympathetic activity, which can be measured as reduction in high frequency heart rate variability, has been associated with effortful control (Spangler & Friedman, 2015 ) and emotion regulation (Beauchaine, 2015 ), but a recent meta-analysis supports a more general role in top-down self-regulation (Holzman & Bridgett, 2017 ).

A CNS measure of motivational states can be provided by electroencephalography (EEG). Higher mental effort/workload has been associated with attenuated parietal alpha activity (Brouwer et al., 2012 , 2014 ; Fink et al., 2005 ), higher frontal theta activity (Cavanagh & Frank, 2014 ; Klimesch, 2012 ), and a higher theta/alpha ratio. Another useful EEG index of motivation is asymmetrical frontal activity, which has been proposed to index motivational direction . Approach and avoidance motivation are respectively related to greater left and right frontal activity (Kelley et al., 2017 ).

It should be noted that none of these process-oriented measures has currently been established as reliable enough to replace verbal reports. A standard conclusion is that “more research is needed” (Holzman & Bridgett, 2017 ). A constructive way forward, which Fulmer and Frijters ( 2009 ) and Scheiter et al. ( 2020 ) strongly advocate, is to triangulate multiple methods, including self-reported and process-oriented measures. Given that physiological measures are relatively new, triangulation can help establish their reliability and validity. For example, EEG could be measured along with behavioral process-oriented task measures of effort. This allows testing whether fluctuations in theta and alpha activities are due to subjective effort mobilization and not due to other processes such as emotional arousal. Such triangulation studies can point the way to reliable online measures of motivation that do not rely exclusively on self-reports.

Measuring Achievement

While achievement is a less-fraught construct than motivation, it still presents its own challenges. First, achievement is nearly always bound to a specific domain, for example mathematics (Arens et al., 2017 ) or reading skill (Ehm et al., 2019 ; Sewasew & Koester, 2019 ). It is unclear whether findings generalize from one domain to others. It is possible that there are quantitative or even qualitative differences between domains in how motivation and achievement interact, for example as a function of the feeling of flow that is or is not associated with performance within the domain.

A second aspect of achievement that may affect results is the type of measurement used. Achievement can be measured using standardized tests and grades in schools (Arens et al., 2017 ; Marsh et al., 2016 ), but for example also through teacher or self-assessment (Chamorro-Premuzic et al., 2010 ). These tend to vary substantially in reliability and validity and yield different results (e.g., stronger reciprocity for school grades than for test scores; Marsh et al., 2016 ). Moreover, in longitudinal studies, it is often difficult to assess whether performance at different moments in time truly reflects the same skill. For example, studies of reading skill may assess basic letter decoding skills in a first wave, and complex reading comprehension in the last (Sewasew & Schroeders, 2019 ). Such changes in tested skills are likely to lead to a lower stability of scores, and skew estimates of change over time. This consideration would speak for designs (discussed above) with shorter periods between measurement waves, where the same measures can be used in different waves.

A third aspect of achievement which may be important is that achievement can be construed as mastery of skills, which usually grows over time, or as performance relative to peers, which by definition cannot grow for all students. Studies typically use raw test scores as a dependent measure to assess this (Huang, 2011 ; Scharmer, 2020 ), which reflect mastery of skills. What is communicated to students, on the other hand, tends to be performance relative to peers (e.g., rankings or grades, which tend to be age-normed either explicitly or implicitly). This implies that perceived performance (see Fig. 1 ) will be based on relative performance, and not on the absolute achievement that researchers tend to study.

Scope of the Theories and Generalizability of Findings

Studies investigating motivation-achievement interactions have often studied the development of these processes separately during childhood, adolescence, and early adulthood. It is therefore unclear whether results can be generalized across developmental stages. Furthermore, as in many subfields of psychology, the majority of research in this area has been conducted in Western, educated, industrialized, rich, and democratic (WEIRD) societies (Henrich et al., 2010 ), where, for example, rates of schooling are much higher than other places (e.g. the Global South). Here, we outline considerations of generalizability across developmental stages and ethnic and sociocultural settings.

Generalization Across Developmental Stages

Childhood and adolescent development is characterized by rather different trajectories for academic achievement (with a general pattern of improvement with age) than for academic motivation (with a general pattern of decrease during adolescence, as well as diversification in sources of motivation) (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ). As a result, we can speculate that the reciprocal relationships between motivation and achievement will change with age. Below, we first highlight findings on changes in motivation across development, and next describe the consequences of developmental differences on reciprocal relations between motivation and achievement, as a function of age, developmental, and academic stages (such as puberty or school grade).

The way in which value guides goal pursuit transforms profoundly from childhood to adolescence to adulthood (Davidow et al., 2018 ), and is reflected in changes in reward sensitivity and cognitive control. At the individual level, motivational beliefs related to competence, control and agency, intrinsic and extrinsic motivation, and subjective task value undergo significant changes throughout the lifespan (Wigfield et al., 1998 , 2019 ). Social cognitive accounts often postulate that the development of more sophisticated cognitive capacities with age allows adolescents to improve performance but also to be more aware of their own abilities and those of their peers (Dweck, 2000 , Scherrer and Preckel, 2019 ). As children go through school, previously held optimistic beliefs on competency become more realistic or even pessimistic (Fredricks & Eccles, 2002 ; Jacobs et al., 2002 ; Scherrer & Preckel, 2019 ; Watt, 2004 ). A meta-analysis by Scherrer and Preckel ( 2019 ) found a small but significant overall decrease in several motivation constructs including academic self-concept, intrinsic motivation, mastery, and performance-approach achievement goals over the course of elementary and secondary school. However, for several other constructs, including self-esteem, academic self-efficacy, and performance-avoidance achievement goals, there was no consistent developmental trend across empirical studies. Overall, this heterogeneity in developmental patterns of various motivation constructs suggests that the reciprocal interactions with achievement may also follow different trajectories across development and still need to be investigated.

Beyond the individual level, social influences on learning and motivation within the family, peer, and school contexts (see Fig. 1 ) also play a significant role in the changes in motivation and achievement (Nolen & Ward, 2008 ; Wigfield et al., 1998 ). Sensitivity to social context continues to develop through childhood and adolescence, transforming through the different school stages (Ladd et al., 2009 ). Broadly speaking, motivation for academic activities decreases between childhood and adolescence, and motivation reorients toward social and novel situations (Crone & Dahl, 2012 ). According to the stage-environment fit account, the decline in academic motivation in adolescents is driven by a mismatch between their newly developed needs and their social settings (Scherrer et al., 2020 ; Scherrer & Preckel, 2019 ). Specifically, the transition to middle and high schools is usually accompanied by changes in peer relationships, friendship, and teacher-student relationships, an increase in normative and performance-focused evaluation and a decrease in perceived autonomy. Adolescence is especially characterized by heightened social influences on motivation (Casey, 2015 ): social interactions become increasingly important and peer affiliation motivation peaks (Brown & Larson, 2009 ).

Indeed, peer relationships show a stronger influence on academic self-concept for seventh graders, compared to fifth graders (Molloy et al., 2011 ). As children transition into middle school, there is increased competition for grades and typically a larger pool of peers that serve as a reference group (Molloy et al., 2011 ). During adolescence, same-aged peers in school can motivate academic achievement to a larger extent, and a stronger focus on performance rather than mastery goals is sometimes empirically observed (Maehr & Zusho, 2009 , but see Scherrer et al., 2020 ; Scherrer & Preckel, 2019 where meta-analytic findings point to declines in both mastery and performance goals).

In sum, individual developmental changes in self-concept, self-regulation, social influence, and the values attributed to certain academic goals suggest that reciprocal motivation-achievement relations from one age group cannot be readily generalized to other ages (Marsh & Martin, 2011 ). Qualitative and quantitative differences in the reciprocal relationship between motivation and achievement thus seem plausible, but the lack of developmentally appropriate measures complicates comparisons across different stages (Fulmer & Frijters, 2009 ). Populations of different ages have distinct motivation factor structures (Rao & Sachs, 1999 ) and young children do not yet have the cognitive and memory capacity to process some motivation constructs and contextual references (Fulmer & Frijters, 2009 ).

Taken together, it is critical to understand how changes in motivation interact with changes in abilities, and affect behavior across different age groups and school career. The literature would greatly benefit from an integration of research across a broader age range, and identifying continuities and discontinuities in the reciprocal relationship between motivation and performance across development. One way to do this is to leverage accelerated longitudinal designs, with multiple measurements of cohorts with different starting ages and differentiation between multiple motivation constructs (Guay et al., 2003 ; Marsh & Martin, 2011 ; Scherrer & Preckel, 2019 ).

Generalization Across Sociocultural Settings

The reciprocal relationship between motivation and achievement may also take different shapes across contexts, as students belong to different ethnic, gender, socioeconomic (SES), and cultural groups. However, the majority of current research on the reciprocal relations between motivation and academic achievement has suffered from what can be considered a sampling bias problem (Pollet & Saxton, 2019 ), i.e., conducted using homogenous samples in terms of ethnicity (Marsh & Martin, 2011 ) and cultural background (Henrich et al., 2010 ). In the meta-analysis by Valentine et al. ( 2004 ), which showed that samples from non-Western countries tended to have larger effect sizes than those from Western countries, there were only four non-Western samples out of a total of 60 samples. In her meta-analysis of Scharmer ( 2020 ), 90% of samples were collected in WEIRD countries (Australia, USA, and Western Europe, with fully half using German samples). This is problematic, given that even within WEIRD samples, motivation of students from different groups (e.g., African Americans vs. European Americans) is influenced by different factors, and may contribute differently to their academic achievement (Cohen et al., 2009 ). Ten years later, the remark of Marsh and Martin ( 2011 ) thus still stands that it is premature to conclude that the reciprocal relationship between motivation and achievement is universal.

Demonstrating this across diverse populations is important for three reasons. Firstly, even the same motivation construct might contribute differently to achievement across groups. For example, Chiu and Klassen ( 2010 ), using PISA data and a very large and diverse sample ( N participant = 88,590, N country = 34), found a positive link between mathematics self-concept and mathematics achievement, but this relationship was moderated by cross-country differences in cultural orientations (specifically, degree of egalitarianism, rigidity in gender roles, aversion to uncertainty). As mentioned above, Sachisthal et al. ( 2019 ) also showed that across populations different motivation constructs are central in the network of constructs.

Second, it is not unlikely that different groups have diverging motivation constructs. For instance, general self-concept is conceptualized differently across cultures (Becker et al., 2012 ; Taras et al., 2010 ; Vignoles et al., 2016 ). Thus, the extent to which academic self-concept contributes to a general sense of self likely differs across groups (Hansford & Hattie, 1982 ). Chen and Wong ( 2015 ) also found that Chinese students assigned different meanings to performance-approach and performance-avoidance goals than what is usually found in Western populations. As a result, interventions may need to target different factors in different sociocultural settings.

Finally, there might be culture-dependent or population-specific pathways connecting the relationship between motivation and achievement. For example, culture is likely to have a strong influence on attributional processes (see extensive theoretical discussion in Graham, 2020 ; empirical data in Chiu & Klassen, 2010 ) and implicit theory of intelligence (W. W. Chen & Wong, 2015 ). Chiu and Klassen ( 2010 ) found that calibration of mathematics self-concept (i.e., the degree to which judgments of one’s competence in a domain accurately reflect actual performance) was positively related to mathematics achievement. However, this link was significantly stronger in places where the prevailing culture was more egalitarian or more tolerant of uncertainty.

Such findings suggest differences between sociocultural contexts are not just gradual but also likely to be qualitative. This would threaten the generalizability of findings (Henrich et al., 2010 ). Note that many of the empirical studies cited in this section are non-longitudinal. Reciprocal relationships between motivation and achievement may look different from what we currently know when representative samples are included. It is thus highly relevant for future motivation research to increase ethnic, and other group diversity in their studies. This can be done by better sampling within geographical boundaries (Pollet & Saxton, 2019 ) and by reaching out to under-researched territories such as in Africa, Middle East, Southeast Asia, Central Asia, and South America.

Diversifying study populations can be tough, but is essential for new understanding of human universals and specifics in motivation. For example, collecting experimental data across countries offers alternative perspectives to experimental set-ups and findings, which subsequently prompt researchers to rethink the constructs of interest and their operationalizations (Vu et al., 2017 ). Nevertheless, there are innovative solutions to overcome practical difficulties, including collaborating with researchers who reside in places where certain specificity and universality in motivation constructs can be expected (as outlined in some of the examples above) and making use of networks of researchers such as Psychological Science Accelerator to get access to multiple laboratories and populations across the world ( https://psysciacc.wordpress.com/ ).

Discussion and Conclusions

We have summarized theories of motivation and analyzed these specifically with regards to how they conceptualize reciprocal interactions between motivation and achievement. This led to a summary of pathways between motivation and achievement, depicted in Fig. 1 . The common denominator between theories suggested reciprocal positive influences of motivation on achievement and vice versa, which has been supported by much previous research. We reviewed the strengths of the underlying data, but also limitations and gaps in the evidence. This led to a research agenda consisting of the following recommendations for future studies on the relationship between motivation and performance: (1) include multiple motivation constructs (on top of ASC), (2) investigate behavioral mediators, (3) consider a network approach, (4) align frequency of measurement to expected change rate in intended constructs and include multiple time scales to better understand influences across time-scales, (5) check whether designs meet the criteria for measuring causal, reciprocal inferences, (6) choose an appropriate statistical model, (7) apply alternatives to self-reports, (8) consider various ways of measuring achievement, and (9) strive for generalization of the findings to various age, ethnic, and sociocultural groups.

One of the hardest problems to solve is the lack of studies that allow for firm causal inferences. Most studies contain sophisticated statistical analyses of longitudinal data. While impressive, the underlying data remains correlational in nature and susceptible to explanations in terms of the presence of a (time-varying or time-invariant) third variable (or variables) that is not accounted for in a model, yet does have a causal effect on the outcomes. Usami et al. ( 2019 ) outline three assumptions that need to be checked when making causality inferences in the context of longitudinal designs. These are the assumptions of consistency, of positivity after controlling for confounders, and of no unobserved confounders (see full the discussion in Usami et al., 2019 ). In our view, the trickiest is the third assumption: “the relation between x and y must not be explained by other causes”(Antonakis et al., 2010 , p. 1087; Usami et al., 2019 ). There seems to be no way to conclusively rule out the presence of such confounders. Substantial knowledge about potential confounders and their characteristics, and using that knowledge to select the most appropriate cross-lagged model, is necessary.

We argued that the strongest support for causal claims on motivation-achievement relations would be studies manipulating either motivation or achievement at one time point and studying the effects on motivation-achievement interactions across subsequent time points. Such studies do not yet exist to our knowledge. Many studies do show effects of manipulations affecting motivation thereby having an effect on achievement, but these studies have not looked at longitudinal interactions. The other pathway (i.e., achievement → motivation) has not been studied extensively, because of difficulties identifying manipulations that would directly affect achievement but not motivation.

A way to work around this problem is to manipulate perceived achievement, instead of true achievement (our lab study, manuscript in preparation). In this experiment, participants perform a learning task that lasts an hour. Their motivation and achievement are measured at multiple consecutive time points. Halfway through the experiment, a manipulation of perceived feedback is introduced: participants receive rigged feedback that their achievement has dropped to below peer average. The causal relations between motivation and achievement can be examined because manipulated perceived achievement leads to corresponding changes in motivational beliefs, to changes in motivational behaviors and eventually, to changes in actual achievement across multiple consecutive time points. Another example of manipulation of achievement can be found in Bejjani et al. ( 2019 ) where they used a feedback manipulation (a competence-threatening IQ score) to study its effect on subsequent motivation and learning.

Furthermore, we have argued that motivation can best be seen as a constellation of highly related, multidimensional constructs, and manipulations of motivation may directly or indirectly influence achievement and vice versa. An innovative method to study the motivation-achievement relationship can be a network approach, where observational and interventional data are used to estimate a causal graph. The idea is that to estimate causal relations, one variable can be manipulated at a time, and its effects on other variables can be observed. The network approach is also beneficial in the classroom context where there are many variables to take into account which cannot be independently manipulated (Yeager & Walton, 2011 ).

Our discussion of various theories of motivation in education showed how densely motivation and performance are interlinked. They can best be seen as a cycle of mutually reinforcing relations. While a cycle suggests a closed loop, we list several options for outside intervention, which are represented by the gray arrows in Fig. 1 . Some of these are well-researched practical interventions, such as autonomy support and training in helpful attributions (Hulleman et al., 2010 ). Others are excellent avenues for future research. For example, designing how feedback reaches the learner offers opportunities for motivation support. Research has shown how to provide negative feedback in a way that does not lower a learner’s motivation (Fong et al., 2019 ), how peer comparison can be harnessed for motivation (Mumm & Mutlu, 2011 ), or how feedback can be given without giving away that errors have been made (Narciss & Huth, 2006 ). It is our impression that this research has so far not reached all classrooms.

In conclusion, this view of a cycle between motivation and achievement, as shown in Fig. 1 , has intuitive appeal and fits well with theories of academic motivation. However, empirical evidence for a cycle is far from complete. The research agenda we have presented contains important challenges for future research aimed at elucidating how motivation and achievement exactly interact, and whether a cycle and a network of constructs are good ways of conceptualizing these interactions. As academic motivation typically drops considerably in adolescence and may be lower for some groups (e.g., through the effects of SES, stereotype threat, and the likes), such evidence is necessary for gaining knowledge on how to best intervene in the cycle, and bring out the best in each student.

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Acknowledgements

We would like to thank Sibel Altikulaç, Abe Hofman, and Simone Plak who participated in our Expert Workshop in Motivation-Performance Cycle in Math Learning in Amsterdam in June 2019 where we discussed ideas for this paper. We would like to thank Milene Bonte, Wouter van den Bos, Camila Bosano, and Bruce McCandliss who are members of the advisory board for the Jacobs Foundation project of which the subproject to write this manuscript is a part. We also want to thank Asmar Isilak who helped with the first database search for empirical studies on the motivation-achievement cycles in learning .

This work was supported by the Jacobs Foundation Science of Learning pilot grant to Nienke van Atteveldt and Brenda R. J. Jansen [project number 2019 1329 00]. Nienke van Atteveldt was also supported by a Starting Grant from the European Research Council (ERC, grant #716736). The funders had no role in study design, decision to publish, or preparation of the manuscript.

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Vu, T., Magis-Weinberg, L., Jansen, B.R.J. et al. Motivation-Achievement Cycles in Learning: a Literature Review and Research Agenda. Educ Psychol Rev 34 , 39–71 (2022). https://doi.org/10.1007/s10648-021-09616-7

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Please note you do not have access to teaching notes, the relationship between student motivation and academic performance: the mediating role of online learning behavior.

Quality Assurance in Education

ISSN : 0968-4883

Article publication date: 13 May 2022

Issue publication date: 10 January 2023

This paper aims to use a quantitative approach to explore the role of online learning behavior in students’ academic performance during the COVID-19 pandemic. Specifically, the authors probe its mediating effect in the relationship between student motivation (extrinsic and intrinsic) and academic performance in a blended learning context.

Design/methodology/approach

Survey data were collected from 148 students taking an organizational behavior course at one Chinese university. The data were paired and analyzed through regression analysis.

The results show that students should actively engage in online learning behavior to maximize the effects of blended learning. Extrinsic motivation was found to positively influence academic performance both directly and indirectly through online learning behavior, while intrinsic motivation affected academic performance only indirectly.

Originality/value

Through paired data on extrinsic and intrinsic motivation, online learning behavior and academic performance, this study provides a more nuanced understanding of how online learning behavior affects the focal relationship, and it advances research on the mechanisms underlying the focal relationship. Practitioners should enhance students’ online learning behavior to boost blended learning effects during the COVID-19 pandemic.

  • Blended learning
  • Extrinsic motivation
  • Intrinsic motivation
  • Academic performance
  • Online learning behavior

Acknowledgements

This research was funded by Research Project on Comprehensive Education and Teaching Reform of Sino Foreign Cooperation in Running Schools in Henan University of Technology (GJXY202117), Research Project on Education and Teaching Reform of School of Management of Henan University of Technology (GLXY2021YB02) and Education Research Fund of North China University of Water Resources and Electric Power.

Meng, X. and Hu, Z. (2023), "The relationship between student motivation and academic performance: the mediating role of online learning behavior", Quality Assurance in Education , Vol. 31 No. 1, pp. 167-180. https://doi.org/10.1108/QAE-02-2022-0046

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Metacognitive awareness and academic motivation and their impact on academic achievement of Ajman University students

Rasha m. abdelrahman.

a Psychology Department Ajman University, United Arab Emirates

b Researcher at the National Center for Examination and Educational Evaluation (NCEEE), Egypt

Metacognition is the ability of learners to take necessary steps to plan suitable strategies for solving the problems they face, to evaluate consequences and outcomes and to modify the approach as needed, based on the use of their prior knowledge. Metacognition helps learners to successfully achieve a personal goal by choosing the right cognitive tool for this purpose. The study, therefore, aims to explain the relationship and impact of metacognitive awareness and academic motivation on student's academic achievement. This descriptive and correlational study design has included 200 students (60 males) studying sociology in the College of Mass Communication and Humanities at Ajman University, UAE. Academic intrinsic motivations scale and the metacognitive awareness inventory were used as instruments. PLS-SEM was used to examine the relationship between metacognitive awareness and academic motivation, and their impact on academic achievement. Females obtained significantly higher levels than males on the two scales of metacognitive awareness, as shown in metacognitive knowledge. Females reported a higher-level academic extrinsic motivation than males. There is a highly significant correlation between the students' academic achievement and academic motivation; academic achievement and academic intrinsic motivation; academic achievement and academic extrinsic motivation. Metacognitive awareness is a major contributor to success in learning and represents an excellent tool for the measurement of academic performance.

Psychology; Academic achievement; Academic motivation; Gender differences; Metacognitive awareness.

1. Introduction

The quality of education has been positively changed by the rapid development of science ( Darling-Hammond et al., 2019 ). This condition (quality of education) further paved the way to transition from teacher-centered education to student-centered education, completing changing the conventional understanding of education ( Kasim and Aini, 2012 ). Furthermore, the crucial components of student-centered education are among the study procedures, where students use their metacognitive awareness, regulating their own study procedures, and possessing motivation. Metacognitive awareness, metacognitive experiences, metacognitive knowledge, metacognitive beliefs, metacognitive skills, high-level skills, and upper memory are some terms associated with metacognition ( Veenman et al., 2006 ; Yeşilyurt, 2013 ). The objective of education in the 21st century is not only to provide students with a huge amount of knowledge and information but also to prepare students to become effective and independent learners, who have self-regulatory skills and can achieve academic success as long with life success. Wolters (2003) identified the self-regulated learners as “the persons who have the cognitive, metacognitive abilities as well as motivational beliefs required to understand, monitor, and direct their own learning”.

Boekaerts and Corno (2005) have argued that students must be actively engaged in the learning process. Students should be able to plan, monitor, regulate, and control their cognitive procedures with respect to their attitudes and behaviors. Therefore, students need to possess high metacognition skills to engage actively in learning and achieve success. Achieving excellence in academic performance is founded on the student's academic intrinsic motivation, which plays a vital role in the learning process and human's life activities. Learners are not only information recipients from psychologists' viewpoint, but they must be active participants in the process of learning, which requires full engagement and deep involvement of students. Modern statistical investigations proved that optimum learning outcomes are achieved when learners possess the intrinsic motivation and true interest in the subject they learn ( Cerasoli et al., 2014 ; DePasque and Tricomi, 2015 ; Ryan and Deci, 2000 ). Learners equipped with intrinsic motivation can face academic challenges and difficulties with the appropriate flexibility and adaptability.

College-aged students can take advantage of using strategies under metacognition strategies. Moreover, metacognitive skills can be understood by students for enhancing their learning ( Fisher et al., 2015 ; Barenberg and Dutke, 2019 ). Pintrich claims that students will more likely to use different types of strategies for learning, problem-solving, and thinking. Furthermore, Pintrich (2002) argues that there is a need to teach metacognitive knowledge comprehensively. Two recent studies have presented particular strategies for enhancing metacognition ( McGuire, 2015 ; Medina et al., 2017 ). The relationship between the metacognitive level of students with their demographic attributes including academic achievement and grade point average (GPA) is also examined ( Özsoy and Ataman, 2017 ; Mokhtari et al., 2018 ). Higher cognition knowledge was observed among undergraduate students ( Erenler and Cetin, 2019 ), whereas Medina et al. (2017) have found higher knowledge of cognition among graduate students as compared to undergraduates.

The commitment of the teachers is considered as the principal indicator to endorse failure or success, in the education system. Due to minimal commitment of the teachers, students tend to lose the level of self-efficacy. In this way, students switch the deeper strategic approach to learning and move in the direction of surface learning approach, in the first year of education ( Güvendir, 2016 ). Most of the teachers do not assist or develop the motivation of the students appropriately, which reduces the motivation of the students. Therefore, behavior of the teachers is important to increase the motivation of the students. Specifically, behavior of autonomy tends to increase the motivation within the students while the control behavior decreases it ( Hallinger et al., 2018 ). Moreover, the learning atmosphere and environment are important for the motivation of the education rather than teachers' behavior and individual students. Similarly, the practices of the institutes and perception of the class mates are likewise important ( Hanus and Fox, 2015 ). Furthermore, it is observed that the major downside of the extrinsic motivation is its tentative nature. The extrinsic motivation disappears when the reward or prize is achieved ( Hofferber et al., 2016 ).

The study, therefore, aims to explain the relationship and impact of metacognitive awareness and academic motivation on student's academic achievement. Following research questions are constructed to achieve the aim comprehensively;

  • 1. Is there any significant difference in (academic achievement, metacognitive awareness, and academic motivation) related to Gender differences?
  • 2. Is there a relationship between metacognitive awareness (metacognitive knowledge and metacognitive regulation) and academic achievement?
  • 3. Is there a relationship between academic motivation (intrinsic motivation-extrinsic motivation) and academic achievement?
  • 4. Is there a relationship between metacognitive awareness (metacognitive knowledge and metacognitive regulation) and academic motivation (intrinsic motivation and extrinsic motivation)?

The importance of this study is to provide the insights about the factors which impacts upon the academic achievement of the students in Ajman University. Firstly, the exploration of the concepts related to the metacognition will help the literature in the settings of educational institutes. Secondly, this study adds value to the literature on motivation as the concept of intrinsic and extrinsic motivation among the students is also the focus of this study. Thirdly, this study develops the concept about the academic achievement of the students, in the context of Ajman University. Hence, this research work should add value to the lives of university students to increase the level of academic achievement among the students of Ajman. Moreover, the outcomes, implication, and suggestions of the study should provide an advantage to the administrators of the university as well, to develop the strategy to improve the teacher's affective support among the teachers of Ajman.

2.1. Metacognition awareness and academic motivation

Several studies have indicated a strong relationship between metacognition skills and intrinsic motivation. These studies linked the success of academic involvement of students to their intrinsic motivation and application of sound and fruitful metacognition strategies, in comparison to their fellow students who have no intrinsic motives ( DePasque and Tricomi, 2015 ; Efklides, 2011 ). Pintrich and DeGroot (1990) believed that metacognition strategies are essential for success in the learning process; however, academic success is not only dependent on these strategies. The type of metacognition strategies and intrinsic motivation play a major role in the students' academic achievement. Furthermore, students with intrinsic motivation are capable of engagement in metacognition strategies for continuous planning, assessment, and evaluation of their progress in academic performance. The positive correlation between motivation and self-appeared to be one of the main pillars of the self-learning process.

According to Ibrahim et al. (2017) , the metacognitive strategy is further considered as one of the basic pillars of academic performance and learning excellence. This shows that metacognition assists a learner in appropriately planning, regulating, organizing, and calibrating his or her cognitive procedures and intellectual capabilities. Negovan et al. (2015) have classified metacognition into metacognitive regulation and metacognitive knowledge. Metacognition regulation indicates the actual activities of a learner to enhance memory and learning such as evaluating monitoring and planning. Metacognitive knowledge refers to a learner who identifies his or her own cognitive knowledge based on conditional knowledge and declarative process ( Young and Fry, 2008 ). These strategies are strongly associated with intrinsic motivations, learning advancement, the adoption of adequate strategies based on the task demands, learning outcomes and reading comprehension, and developing an association between previous and new knowledge.

Metacognition is also categorized as higher-order thinking that engages active control over the cognitive procedures involved in the learning process ( Barnes and Stephens, 2019 ). It is also an essential strategy associated with academic achievement and problem-solving abilities. The development of modeling strategies of students is influenced by metacognition when the effects of self-checking, cognitive strategy, awareness, and planning are considered ( Vettori et al., 2018 ). Students who carry-out better self-check reflect higher development in their modeling abilities as compared to those who are less skillful in self-checking. The development in modeling competencies is mediated by planning skills and cognitive strategy. Students with increased skills carried out modeling better after some experience is achieved. On the contrary, the metacognitive and cognitive activities did not occur sequentially in the procedure through which planning activities are most common, while prediction activities are least common ( Hidiroğlu and Bukova Güzel, 2016 ).

2.2. Academic achievement and metacognitive awareness

Some researchers have reported the influence of metacognitive on academic achievement ( Bogdanović et al., 2017 ; Abdellah, 2015 ), while others view that explicit metacognitive training can enhance students' metacognition skills and believed that metacognition skills promote and correlate significantly with students' academic performance or achievement ( Nbina, 2012 ; Nzewi and Ibeneme, 2011 ). Several studies have illustrated that students demonstrated high metacognitive awareness skills by reaching a high level of academic achievement, while students with poor metacognitive awareness skills have illustrated the lower level of academic success ( Narang and Saini, 2013 ; Kocak and Bayaci, 2011 ). Therefore, metacognition can be used as a strong predictor of academic level. Several studies have shown the positive impact of training on students with poor metacognitive strategies. Those students can benefit from training to improve their metacognitive and academic performance ( Nbina, 2012 ; Nzewi and Ibeneme, 2011 ; Rezvan et al., 2006 ). Other studies have shown a negative or no relationship between metacognitive awareness and academic achievement ( Cubukcu, 2009 ; Sperling et al., 2004 ).

Many studies illustrated the positive relationship between intrinsic motivation and academic achievement. These studies pointed out that, intrinsic motivation plays an essential role in the student's performance and academic achievement. These studies have also found that students with high academic intrinsic motivation had achieved academic success easier than others who have the lower academic intrinsic motivation ( Lepper et al., 2005 ; Deci and Ryan, 1998 ; Gottfried, 1985 , 1990 ).

Metacognition positively influences problem-solving skills, which comes from studies in other domains ( García et al., 2016 ). Differentiations are observed between inaccurate and accurate students in the metacognitive process during solving math problems, even though students spent little time representing or organizing information ( García et al., 2016 ). Accurate students pay substantial attention to time planning so they do not evaluate their results and progress. Astonishingly, metacognitive training is majorly beneficial for low achievers as it enables them to advance and solve a similar number of tasks ( Karaali, 2015 ). Students usually get help with self-reflective and metacognitive activities emphasized learning comprehensively and motivated and engaged within the study ( Karaali, 2015 ). On the contrary, the contribution of metacognition in the problem varies for students with and without learning complexities. Metacognition does not work well with learning complexities even when associated with the mathematics problem ( Al Shabibi and Alkharusi, 2018 ). For instance, students with learning complexities show a much lower mean score to identify the sequence of steps for solving the activities as compared to those regardless of learning complexities ( Al Shabibi and Alkharusi, 2018 ).

2.3. Metacognition awareness, academic achievement, and gender differences

Previous studies on gender differences in self-regulation and metacognition have been generally inconsistent. Jenkins (2018) has reported that male students use more superficial learning tactics as compared to female students, whereas Nunaki et al. (2019) have indicated that female students utilize self-monitoring goal setting and planning as compared to male students. Jenkins (2018) has studied gender differences to evaluate academic metacognition and motivation. The study has used strategies that are used by students to actively change their learning capabilities. Male students show higher scoring in their use of rote-learning strategies as compared to female students and indicate no gender differences in any of the other superficial learning strategies.

Alghamdi et al. (2020) have examined gender differences in self-regulated learning by identifying metacognition of students to several other self-regulated learning strategies, which include time management, elaboration and effort, rehearsal, and organization. In general, female students report higher scores as compared to male students in different strategies of self-regulated learning, which include metacognition. Arum (2017) has claimed that awareness must be owned by students at every step of his thinking for improving metacognition skills. The student will be aware of his thinking procedure and assess him or herself to the outcomes of his thought process so that it will reduce the mistake of a student to solve the problem. Purnomo and Nusantara (2017) have indicated that the concept of metacognition is an estimation of an individual's thinking, including metacognitive skills and metacognitive knowledge. In addition, Trisna et al. (2018) have indicated that metacognition allows a student to be aware of the thinking process by regulating and rechecking the thinking process. Sometimes, there is a concept error on the information acquired by the student in the learning process. The information provided by the lecturer is not like the information that is thought by students. In this instance, metacognition shows the thinking stage of students for reflecting on the way of thinking and the outcomes of thinking. There is an important role of metacognition in the procedure of academic learning, specifically in understanding the concept. A conceptual framework has been constructed to present the relationship between variables discussed aforementioned ( Figure 1 ).

Figure 1

Conceptual framework.

3. Material and methods

Ethical approval.

IRB # D-H-F-2020-May-28, Ajman University, United Arab Emirates.

3.1. Study design

The descriptive and correlational study design has been employed to determine the impact of metacognitive awareness, intrinsic motivation, and extrinsic motivation on the student's academic achievement.

3.2. Participants

A purposive sample consisted of 200 students (140 females and 60 males) studying sociology in the College of Mass Communication and Humanities at Ajman University, UAE during the academic year 2015–2016. The range of the age varies between 20 and 29 years, with an average age of 23 years. The survey was conducted between December 2016 and February 2017, covering students studying courses of social psychology and social problems (second, third and fourth years), who responded to the two questionnaires on a voluntary basis. Administration time ranged from 25-40 min. Student's names were not included to ensure confidentiality.

3.3. Instruments

3.3.1. academic motivation scale.

Regina (1998) has proposed this scale based on the results reached in several previous studies. This scale has been translated into Arabic by the researcher to be used in this study and facilitate the students. The scale consists of 56 items graded on a five-point rating scale. It covers six factors: four extrinsic motivation factors including authority expectations, peer acceptance, fear of failure and power motivation, and two intrinsic motivation factors including mastery goals and need for achievement. External motivation drives the intrinsic motivation as compared to undermine it and it has positive influence specifically when students possess low levels of intrinsic motivation in spite of the negative notions on extrinsic motivation.

The scale validation was made by sending the scale to six different arbitrators, who were educational experts specializing in psychology, language, and measurement. Based on the experts' suggestion, eight items were deleted from the original scale. Therefore, the final form of the scale consisted of 48 items, eight items for every factor. Consistency validity was tested by the correlation coefficients ranging from 0.31 to 0.68, which were all statistically significant. The scale reliability was found by using Cronbach alpha, which was: mastery goals (0.73), need for achievement (0.77), authority expectations (0.75), peer acceptance (0.71), fear of failure (0.73) and power motivation (0.72).

3.3.2. The metacognitive awareness inventory (MAI)

Schraw and Dennison (1994) have designed the MAI to determine the adults' metacognition. The MAI consists of 52 statements rated based on the Likert five-point scale, covering two factors of metacognitive: metacognitive knowledge (17 items) and metacognitive regulation (35 items).

3.3.3. The MAI validation and reliability

The MAI validation and reliability were tested and verified by educational experts in Psychology, language, and measurement. A few modifications were made in response to their suggestions. The reliability of the inventory has been found by using Cronbach alpha: The MAI knowledge was (0.78), MAI regulation was (0.8) and MAI total was (0.79).

3.4. Data analysis

The study has used PLS-SEM to analyze the data collected. Structural equation modeling was applied to identify the relationship between metacognitive awareness and academic motivation. Furthermore, this technique was used to examine the impact of metacognitive awareness and academic motivation on academic achievement.

4. Results and discussion

4.1. gender differences, metacognition, and academic achievement.

Table 1 presents the mean and standard deviation of each of the academic achievement as reflected on the students' cumulative grade point average (CGPA), metacognitive awareness (metacognitive knowledge and metacognitive regulation) and academic motivation (academic intrinsic motivation and extrinsic motivation), based on the data of 200 students. The significance levels of t-tests comparing males and females are also provided.

Table 1

Gender differences in academic achievement, metacognitive skills and academic motivation.

∗p > 0.05, ∗∗p > 0.01.

Results showed no significant differences between female and male students in academic achievements, where the academic achievement for female students was 77.1, while the academic achievement for male students was 80.1. Females obtained significantly higher levels than males on the two scales of metacognitive awareness, as shown in metacognitive knowledge (Female m = 79.1, Male m = 65.5, t (98) = 3.1708, p > 0.01). Also, in metacognitive regulation, females reported a higher score than males (Female M = 121.3, Male M = 111.2, t (98) = 3.7052, p > 0.01). These results are supported by Roeschl-Heils et al. (2003) and contradicted by Misu and Masi (2017) who attributed the differences in metacognitive awareness to gender differences. The activities related to metacognition allow students to develop an awareness of themselves, care about, and also give instructions ( Smith et al., 2017 ). In a classroom, teachers must be aware of the individual differences in the metacognitive awareness level and must provide the teaching by accounting their individual differences so that their metacognitive ability might improve well in the classrooms ( Jaleel, 2016 ). The importance of metacognitive knowledge is that it encompasses information regarding tactics that work effectively for most students and information of strategies that work for diverse learners. Therefore, at the beginning of the semester, students who receive metacognitive training learn early in the semester how to study for a specific subject, which may include activity or tasks strategies.

There were no significant differences in academic intrinsic motivation between female and male students. This result is consistent with the findings of Cerezo et al. (2004) . Interestingly, females also reported a higher academic extrinsic motivation than males (Female M = 156.29, Male M = 163.28, t (98) = 3.6399, p > 0.01), which differ than the result of Cerezo et al. (2004) , who found no difference between males and females in their intrinsic motivation. It should be noted that intrinsic motivation improves innovation, creativity, performance and intellectual ability, resilience and enjoyment, and deep learning process ( Fidan and Ozturk, 2015 ). It has been asserted that academic intrinsic motivation accounted for 19% of the total variance of the study variables. The extent of intrinsic motivation in the academic setting was even better as compared to the extrinsic motivation. However, both intrinsic and extrinsic motivation played a substantial role between academic achievement, metacognitive knowledge, and metacognitive regulation.

With respect to academic intrinsic motivation, no large difference was noticed between male and female students, but females reported a higher-level of academic extrinsic motivation than males. Findings also showed a significant correlation between metacognitive awareness and metacognitive regulation, which is confirmed with the results of Narang and Saini (2013) ; Kocak and Bayaci (2010) ; Young and Fry (2008) ; Coutinho (2007) ; Nietfeld et al. (2005) ; and Sperling et al. (2004) . These studies confirmed that students with high metacognitive awareness demonstrate perfect academic performance compared to students with poor metacognitive awareness. It was also found that students' learning strategies have more contribution to academic success than their awareness of metacognitive knowledge.

In all stages of the educational process, the implementation of metacognitive strategies will improve the cognitive performance and efforts of all students. Teaching should be rapid, understandable, and focused on all metacognition parameters based on the special and developmental learning children needs ( Mastrothanasis et al., 2016 ). To be precise, a greater amount of variance was explained by metacognition of the recognized regulatory learning style as compared to the other styles, which complement the importance of metacognition in order to achieve autonomy learning behavior and regulatory learning behavior ( Rosman et al., 2018 ).

Tables  2 , ​ ,3, 3 , ​ ,4, 4 , and ​ and5 5 present reflective higher-order construct of metacognitive knowledge, metacognition regulation, academic intrinsic motivation, and academic extrinsic motivation. From the findings, it is observed that declarative knowledge (0.72, p < 0.10), procedural knowledge (0.88, p < 0.10), and conditional knowledge (0.87, p < 0.10) are positively and significantly reflected from metacognitive knowledge ( Table 2 ). Similarly, planning (0.77, p < 0.10), information management (0.81, p < 0.10), and comprehension monetary (0.18, p < 0.10) are reflected from metacognition regulation ( Table 3 ). Needs for achievement (0.87, p < 0.10) and mastery (0.41, p < 0.10) are reflected from intrinsic motivation ( Table 4 ). Authority expectation (0.79, p < 0.10), peer acceptance (0.83, p < 0.10), fear of failure (0.73, p < 0.10), and power motivation (0.39, p < 0.10) are significantly and positively reflected from extrinsic motivation ( Table 5 ).

Table 2

Reflective Higher-Order Construct (Metacognitive knowledge).

Table 3

Reflective Higher-Order Construct (Metacognitive regulation).

Table 4

Reflective Higher-Order Construct (Intrinsic motivation).

Table 5

Reflective higher-order construct (Extrinsic motivation).

High metacognitive regulation students considered autonomy strategies as more influential and considered to manage their motivation. Autonomous regulatory learning and autonomous style positively affected performance anticipations and performance across the students' achievement ( Ibrahim et al., 2017 ). However, metacognitive knowledge was not an influential indicator of regulatory learning style and; therefore, it reported in school achievement directly. At this specific educational level, it is observed that students perceived the controlling behavior of parents as influential for their objectives to a significant extent.

It has been provided in the above table that metacognitive knowledge (0.13, p < 0.10) and metacognitive regulation (0.35, p < 0.10) have significant relationship with metacognitive awareness. Metacognitive awareness has a significant and positive relationship with academic motivation (0.29, p < 0.10) and academic achievement (0.41, p < 0.10). Academic intrinsic motivation (-0.20, p < 0.10) and academic extrinsic motivation (0.15, p < 0.10) have statistically significant relationship with academic motivation. Furthermore, academic motivation (0.19, p < 0.10) has statistically significant and positive impact on academic achievement. It is essential to develop influential strategies for facilitating the cognitive procedures as learning is a multifaceted process. Furthermore, a learner is represented by his or her accuracy experience, better judgment, significant ways for improving accuracy, and their metacognition and cognitive process (see Table 6 ).

Table 6

Path analysis.

There is a strong correlation between academic achievement and academic intrinsic motivation ( Pintrich, 2002 ; Ryan and Deci, 2000 ; Wu, 2003 ), and a significant correlation between academic achievement and academic extrinsic motivation. Furthermore, findings showed a high correlation between metacognitive knowledge awareness and academic intrinsic motivation, and a high correlation between metacognitive regulation awareness and academic intrinsic motivation, which agree with the studies of ( DePasque and Tricomi, 2015 ; Efklides, 2011 ; Pintrich and DeGroot, 1990 ). There is a weak correlation between academic extrinsic motivation and either metacognitive knowledge awareness and metacognitive regulation awareness.

4.2. Practical implications

The study has determined the relationship and impact of metacognitive awareness and academic motivation on student's academic achievement. The findings of the present study showed no significant differences between female and male students in academic achievement. However, there is a significant difference in metacognitive awareness. Female students showed a higher level of metacognitive knowledge and metacognitive regulation. Findings found that intrinsic and extrinsic motivations are essentially independent. However, extrinsic motivation does not suppress intrinsic motivation and both showed little compatibility in male students. In contrast, both motivations are compatible or even collaborative in female students. This result is consistent with the nature of females in Arab culture, which is patriarchal societies, in which men hold primary power and authority. In such a society, the female motivation is strongly influenced by many extrinsic factors including, family and professor expectation, peer acceptance, fear of failure and power motivation, which affect their motivation.

Both intrinsic and extrinsic reasons underlie the students' achievement behavior. In this instance, professors must adopt effective methods of teaching which include; interactive teaching and curiosity-based learning, using interesting materials and enjoyable tasks that promote academic intrinsic and extrinsic motivation. The present study incorporates independent assessments of both intrinsic and extrinsic motivations, based on the reasons why students engage in-class learning and provide a valuable complement to traditional assessment of motivation, such as how much students enjoy certain activities or content domains. To overcome poor academic performance, university professors can enhance students' intrinsic motivation and metacognition skills by helping them to set endurable goals, which facilitate learning acquisition and enhance constructive and meaningful involvement in academic activities.

Students' academic performance and achievement depend on the applied metacognitive strategies with respect to their intrinsic motivation. Therefore, these aspects with respect to students' intrinsic motivation in universities must be developed and promoted. Teaching strategies and techniques adopted by university professors should not be limited to deliver information but must encourage more interaction between professors and students and activate the use of metacognition skills as an effective tool of positive impacts on academic achievement. Supporting and improving students' intrinsic motivation by using different and enjoyable non-academic activities supports students' personalities and motivates them to participate and raise their self-concept. These improvements would raise their intrinsic motivations and give them the energy to face complex and multidimensional learning challenges and reach achievement.

Lastly, for better understanding of the effects of metacognitive awareness, academic intrinsic motivation, and academic extrinsic motivation on the university academic achievement, future studies should focus on their effect on the outcomes of the learning process, such as students' qualifications, achieved knowledge and skills, and development of social responsibility. Academic motivation is an important factor in college success. The motivations behind academic constancy vary through many intrinsic and extrinsic factors. Many university students lack the motivation needed to excel in their academic performance and to achieve their goals. Most of the students are studying majors they have not chosen, but because of their parent's desires, which make them lose motivation to learn and achieve.

The traditional teaching methods used by professors are not appropriate with a cognitive revolution that can influence the students' academic motivation. Therefore, professors have a great responsibility to support students to learn and achieve their academic degrees. Also, they must adopt successful methods of teaching to motivate them to learn as much as they can. Professors should use their experiences to design the context and tasks in an attractive way. This study has concluded that metacognitive awareness is a major contributor to success in learning and represents an excellent tool for the measurement of academic performance. This study has found a correlation between metacognitive awareness and intrinsic academic motivation. The findings have provided important implications with regards to the findings of mediation analysis. Firstly, self-extrinsic motivation, and intrinsic motivation are identified as determinants of academic motivation and related with metacognition in students in Ajman University. In addition, it should be realized that the likelihood of motivation and metacognition of students are possible approaches related with student's academic achievement.

4.3. Limitation and future studies

One of the limitations of this study was the sampled participants which belong to one academic program at Ajman University, UAE. Therefore, the findings of this study cannot be generalized to other locations or populations. This limitation, however, shines some light on how different locations and populations may influence the relationships between metacognition, intrinsic and extrinsic motivation and academic achievement. Future studies should adopt other measurement approaches such as the experimental approach. In addition, other sources of self-reported data may include parents, instructors, and peers. This will provide future research with different perspectives and holistically assesses students' learning activities. Future studies may also identify other key features such as causal relationships among the complex constructs that were not evident in the findings of this study. Therefore, it is strongly recommended that an experimental design or a mixed-method approach shall be used to gain more knowledge on how optimal learning occurs.

Declarations

Author contribution statement.

R. M Abdelrahman: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgements

The author is thankful to all the associated personnel, who contributed for this study.

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IMPACT OF ACADEMIC MOTIVATION ON ACADEMIC ACHIEVEMENT: A STUDY ON HIGH SCHOOLS STUDENTS

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In the present day world, it has been observed that there is an increase in lack of motivation among the students towards their academics especially when they reach high school because at this stage their attention is diverted and divided among many things like peer group, heterogeneous relations, fashion and incessant entertainment and this hampers their academic performance. So, the present paper is an attempt to find out the relationship between Academic Motivation and Academic Achievement of Class IX students of Assam, India. Tool used for the study is Academic Achievement Motivation Test by T.R. Sharma and for the academic achievement the final year examination results were taken. The findings of the study revealed a significant positive relationship between academic motivation and academic achievement. There is a significant difference in Academic Motivation between high and low achievers. But there is a significant sex difference within low achievers with respect to academic motivation.

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Achievement Motivation is an important ability that determines what a person can do and a motivation determines what a person will do. Thus motivation plays an important role in improving the level of performance. Here in this paper, it aimed to explore the actual relation between Achievement Motivation and Academic Achievement of the Secondary level students in Uttar Dinajpur District. The investigators adopted Deo-Mohan Achievement Motivation Scale (2011) to examine the Achievement Motivation and collected the final result of previous year of the secondary level student for Academic Achievement. The investigators analysed the collected data with Pearson's correlation method to explore the correlations. The present study found the non-significant relationship between Achievement Motivation and Academic Achievement of the students of Uttar Dinajpur District. A positive correlation (r = .06) between Achievement Motivation and Academic Achievement was found among the total samples specially among the girls (r = 0.147), urban (r = 0.032) and rural (r = 0.077) students and a negative but negligible correlation (r =-.042) was found among the boy students.

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Academic motivation is an essential part of learning and achievement. It is the most important factor that leads one to one’s goals. This drive is known as motivation. This study aims at investigating the effect of academic motivation on secondary school-students gender and habitat wise. The authors adopted a test (Bhattacharya, 1980) on academic motivation selecting 6 (six) dimensions. Internal consistency of test was found by Cronbach Alpha. The test was administered on 700 ninth grade students of both sexes drawn from different schools under West Bengal Board of Secondary Education. The analysis with ANOVA conclusively showed that students significantly differ sex wise on academic motivation, but there is no significant difference between urban & rural students or in any strata of them. Article visualizations:

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This study is aimed at finding the Nature of Relationship between Achievement Motivation and Academic Achievement of Senior Secondary School students. Through Purposive Random Sampling, a sample of 100 students—50 male and 50 female—was selected for this study. Deo Mohan Achievement Motivation Scale was employed to measure Achievement Motivation and the Aggregate Marks Percentage in 10th Standard was considered as the Academic Achievement of the 11th Standard students. Simple Arithmetic Mean, Standard Deviation and Pearson’s Product Moment Coefficient of Correlation (r) were used as statistical tools for data analyses. Achievement Motivation was found to be moderately and positively correlated with Academic Achievement of Senor Secondary School students irrespective of their gender.

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The present study investigated the relationship between achievement motivation and academic achievement of secondary school students. In addition, the study found out the students profile to ascertain the levels of achievement motivation, and their academic achievement. A total of 200 students selected from different govt. and private schools of Allahabad city participated in the study. The study-confirms the importance of achievement motivation to academic achievement and concluded by making insightful suggestions and recommendations to stakeholders in education in helping students to enhance their motivation tu improve on their academic performance.

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Academic achievement attains by motivation. There is great need of motivation for students, because motivation effects the academic achievement of the students. The present study is about the academic achievement motivation of the children. Academic motivation involves measuring items such as work habits and scholastic expectation. Achievement motivation plays an important role to achieve educational goals of the students. It was found that Private school students had more academic achievement motivation than government school students at senior secondary level.

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Mchunu, Makhosi Queeneth. "The relationship between motivational factors and school achievement among learners in the Further Education and Training Phase." Thesis, University of Zululand, 2017. http://hdl.handle.net/10530/1560.

Han, Ying. "Parenting Styles, Academic Motivation and Performance - Academically Successful Mainland Chinese Students' Perspectives." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1595325130158061.

Isa, Posiah Mohd. "A study of academic motivation, academic locus of control and academic performance of Malay and Chinese students in Malaysia." Thesis, Keele University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282631.

Böö, Rickard. "Video game playing, academic performance, educational activity, and motivation among secondary school students." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-35525.

Edwards, Nicole C. "School facilities and student achievement student perspectives on the connection between the urban learning environment and student motivation and performance /." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1164663224.

Zhang, Judy Zhe Cun. "Is debt bad for students? The effects of student debt on course selection, motivation, happiness, and academic performance." Thesis, University of Canterbury. Psychology, 2007. http://hdl.handle.net/10092/1468.

Ouyang, Li. "Motivation, cultural values, learning processes, and learning in Chinese students." Thesis, Kingston, Ont. : [s.n.], 2008. http://hdl.handle.net/1974/1340.

Weaver, Amber E. "The Relationship Between Students' Financial Responsibility for College and Levels of Academic Motivation and Success." Ashland University Honors Theses / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=auhonors1367979495.

Niño, de Guzmán Isabel, Arturo Calderón, and Mónica Cassaretto. "Personality and academic achievement in nniversity students." Pontificia Universidad Católica del Perú, 2003. http://repositorio.pucp.edu.pe/index/handle/123456789/99879.

Ho, Sin-ting, and 何倩婷. "Effects of personalization and action choices on students' intrinsic motivation towards completing assignments and learning performance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hdl.handle.net/10722/196503.

Lo, Kam-cheong. "A study of self-regulated learning and academic performance of high and low achieving students in Hong Kong." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22706197.

Wholuba, Benetta H. "Examination of the motivation for learning of gifted and nongifted students as it relates to academic performance." Thesis, The Florida State University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3625947.

This study sought to fill the gap in the literature concerning gifted students and academic motivation by examining the academic motivation in 126 non-gifted ( n = 66) and intellectually gifted ( n = 60) middle and high school students. The study used archival data to answer the following questions: What is the relationship between motivational variables, test anxiety, and student GPA for both non-gifted and gifted students? Are there differences in motivation across student group and across gender? And does a unique profile of motivation exist for intellectually gifted students? Study results revealed positive relationships between certain aspects of motivation and academic performance within the non-gifted students and the gifted students. Findings indicated that intellectually gifted middle and high school students tend to be more motivated than their non-gifted peers and experience significantly less test anxiety than their non-gifted peers. Gender differences in motivation were found only within the gifted group on intrinsic goal orientation, with gifted female students reporting more intrinsic goal orientation than their male counterparts. While a unique profile of motivation did not arise for intellectually gifted students, the gifted students were more likely to fall within cluster groups with high motivation, high sense of control over academic outcomes and high perception of their ability to successfully complete academic tasks. These students tended to have a higher GPA and experience very little test anxiety when compared to students with low motivation.

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Lau, San-fat. "A study of falling learning performance of students in a Hong Kong CMI school : perceptions of students and teachers /." View the Table of Contents & Abstract, 2005. http://sunzi.lib.hku.hk/hkuto/record/B35288449.

Lo, Kam-cheong, and 盧錦昌. "A study of self-regulated learning and academic performance of high and low achieving students in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31961824.

Al-Nabhani, Hilal Zahir Abdullah. "Factors influencing the academic performance of undergraduate students of Sultan Qaboos University (Oman) learning in English, with special reference to attitude, motivation and academic self concept." Thesis, University of Hull, 1996. http://hydra.hull.ac.uk/resources/hull:3938.

Stringer, JK IV. "Academic Self-Concept and Master Adaptive Learning in First Year Medical Students: A Validation and Scale Construction Study." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5579.

Todd, Pamela. "An explanatory study of teachers' perceptions of motivation, behaviors, and academic performance among foster care students in elementary and middle schools." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 2010. http://digitalcommons.auctr.edu/dissertations/156.

Mack, Ana. "DIFFERENCES IN ACADEMIC PERFORMANCE AND SELF-REGULATED LEARNING BASED ON LEVEL OF STUDENT PARTICIPATION IN SUPPLEMENTAL INSTRUCT." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2895.

Fishbaugh, Cherie A. "Effects of a clocklight motivation program on the off-task behavior and academic performance of first grade students during teacher-monitored boardwork." The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406731132.

Lau, San-fat, and 劉新發. "A study of falling learning performance of students in a Hong Kong CMIschool: perceptions of students andteachers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B45007834.

Wu, Si-cheong Gilbert, and 胡仕昌. "The environmental background, learning attitude and academic performance of Hakka and Hoklo students in an N.T. Secondary School inHong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1986. http://hub.hku.hk/bib/B38627723.

Ao, Yu. "The Effect of Visualized Student's Self-Set Learning Progress Goals on East Asian Chinese Student's Motivation and Self confidence In Learning." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5107.

Nichols, Anthony Jeffrey. "An Empirical Assessment of Attitude toward Computers, Motivation, Perceived Satisfaction from the e-learning System, and Previous Academic Performance and their Contribution to Persistence of College Student Athletes Enrolled in e-Learning Courses." NSUWorks, 2008. http://nsuworks.nova.edu/gscis_etd/261.

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Bourne, Anthony. "Development of the Academic Performance-Commitment Matrix (APCM): Understanding the effects of motivation and an engineering mathematics curricular intervention on student self-efficacy and success in engineering." Wright State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=wright1401759063.

Lindwall, Jennifer. "The Relationship Between Undergraduate Research Training Programs and Motivational Resources for Underrepresented Minority Students in STEM: Program Participation, Self-efficacy, a Sense of Belonging, and Academic Performance." PDXScholar, 2019. https://pdxscholar.library.pdx.edu/open_access_etds/5270.

Tang, Tricia. "A comparative analysis of college student spring break destinations an empirical study of tourism destination attributes." Honors in the Major Thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/626.

Gillig, Benjamin. "Academic motivation among college students: variance and predictors." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/6112.

Makar, Kathryn. "Predictors of Students' Academic Performance." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/216590.

Langeveldt, Faith. "Die erkenning van voorafleer as `n meganisme ter voorbereiding van ouer volwasse studente se sukses aan `n hoeronderwysinstelling." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85618.

Suzukawa-Tseng, Sophia. "Contributing Factors to Academic Motivation in Female Undergraduate Students." Scholarship @ Claremont, 2014. http://scholarship.claremont.edu/scripps_theses/494.

Beerline, Nora. "Academic Motivation in Online and Traditional Community College Students." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1582026665119838.

Rojas, Joanne P. "THE RELATIONSHIPS AMONG CREATIVITY, GRIT, ACADEMIC MOTIVATION, AND ACADEMIC SUCCESS IN COLLEGE STUDENTS." UKnowledge, 2015. http://uknowledge.uky.edu/edp_etds/39.

Luna, Alberto Daniel. "Predicting the Motivation in College-Aged Learning Disabled Students Based on the Academic Motivation Scale." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/306941.

Foley, Wing Teri L. "Renaissance schools / academic achievement and value implications of a corporate-sponsored academic motivation program /." Full Text accessible through UMI ProQuest Digital Dissertations, 1993.

Roby, Simone D. "Classism, Academic Self-Concept, and African American College Students' Academic Performance." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2138.

Huffine, John Harold. "Causal Attributions, Attributional Dimensions, and Academic Performance in a School Setting." Thesis, North Texas State University, 1987. https://digital.library.unt.edu/ark:/67531/metadc330619/.

Ng, Siu-ping Connie. "Do students' thinking styles predict students' motivation and performance in project learning?" Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29793038.

Olagbami, Abiola Olabisi. "Academic motivation and self-determination among three ethnic groups of Nigerian students." Thesis, Dallas Baptist University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3604152.

The need related behavioral dynamics that are revealed in self-determination and academic motivation research control factors which pinpoint and examine settings that facilitate self-motivation and well-being. This study examined differences in motivational and self-determination behaviors among three ethnic groups of Nigerian university students using a sample of students attending the University of Ibadan. The research continues the dialogue of the role of ethnicity in the motivational and self-determination behaviors by focusing on Nigerian students. Lastly, the study expands the current literature on motivation and self-determination by adding a study focusing on Nigerian students. Twenty-one hypotheses were tested to answer five research questions in the study. The research questions addressed whether significant statistical differences existed in academic motivation scores of Nigerian students based on their ethnicities or whether the parents' level of education affected the students' motivation, or self-determination. The questions also explored any statistical differences in self-determination of students based on their ethnicities or if there were differences between self-determination and gender, scholarship status, or number of children. Lastly, the questions addressed if there were differences in the type of prerequisites for entry to University of Ibadan. There were no statistically significant differences in means of the three broad types of academic motivation and perceived choice scores on the SDS based on ethnicity, parents' level of education, gender, scholarship status, number of children each participant had, and the kind of entry examinations that were taken. There were statistically significant differences in the mean of awareness of self scores based on parents' level of education and scholarship status. There was also a statistically significant difference in the mean perceived choice scores on the SDS based on the number of children each participant had. There were no statistically significant differences based on students' prerequisites.

Dowson, Martin, University of Western Sydney, of Arts Education and Social Sciences College, and School of Teaching and Educational Studies. "Relations between students' academic motivation, cognition and achievement in Australian school settings." THESIS_CAESS_TES_Dowson_M.xml, 2000. http://handle.uws.edu.au:8081/1959.7/729.

Lee, Kai-man Clement. "The academic motivation of Hong Kong secondary school students : a developmental perspective." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38297401.

Lee, Kai-man Clement, and 李啟文. "The academic motivation of Hong Kong secondary school students: a developmental perspective." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38297401.

Volbrecht, Adam A. "Residence hall discipline and academic performance." Virtual Press, 2006. http://liblink.bsu.edu/uhtbin/catkey/1339139.

Blair, Lucas. "The use of video game achievements to enhance player performance, self-efficacy, and motivation." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4740.

ORIGINAL RESEARCH article

The effects of online game addiction on reduced academic achievement motivation among chinese college students: the mediating role of learning engagement.

Rui-Qi Sun&#x;

  • 1 BinZhou College of Science and Technology, Binzhou, China
  • 2 Binzhou Polytechnic, Binzhou, China
  • 3 Faculty of Education, Beijing Normal University, Beijing, China
  • 4 National Institute of Vocational Education, Beijing Normal University, Beijing, China

Introduction: The present study aimed to examine the effects of online game addiction on reduced academic achievement motivation, and the mediating role of learning engagement among Chinese college students to investigate the relationships between the three variables.

Methods: The study used convenience sampling to recruit Chinese university students to participate voluntarily. A total of 443 valid questionnaires were collected through the Questionnaire Star application. The average age of the participants was 18.77 years old, with 157 males and 286 females. Statistical analysis was conducted using SPSS and AMOS.

Results: (1) Chinese college students’ online game addiction negatively affected their behavioral, emotional, and cognitive engagement (the three dimensions of learning engagement); (2) behavioral, emotional, and cognitive engagement negatively affected their reduced academic achievement motivation; (3) learning engagement mediated the relationship between online game addiction and reduced academic achievement motivation.

1. Introduction

Online games, along with improvements in technology, have entered the daily life of college students through the popularity of computers, smartphones, PSPs (PlayStation Portable), and other gaming devices. Online game addiction has recently become a critical problem affecting college students’ studies and lives. As early as 2018, online game addiction was officially included in the category of “addictive mental disorders” by the World Health Organization (WHO), and the International Classification of Diseases (ICD) was updated specifically to include the category of “Internet Gaming Disorder” (IGD). Prior research investigating Chinese college students’ online game addiction status mostly comprised regional small-scale studies. For example, a study on 394 college students in Chengde City, Hebei province, China showed that the rate of online game addiction was about 9% ( Cui et al., 2021 ). According to the results of an online game survey conducted by China Youth Network (2019) on 682 Chinese college students who played online games, nearly 60% of participants played games for more than 1 h a day, over 30% stayed up late because of playing games, over 40% thought that playing games had affected their physical health, over 70% claimed that games did not affect their studies, and over 60% had spent money on online games. This phenomenon has been exacerbated by the fact that smartphones and various portable gaming devices have become new vehicles for gaming with the development of technology. The increase in the frequency or time spent on daily gaming among adolescents implies a growth in the probability of gaming addiction, while an increase in the level of education decreases the probability of gaming addiction ( Esposito et al., 2020 ; Kesici, 2020 ). Moreover, during the COVID-19 pandemic, adolescents’ video game use and the severity of online gaming disorders increased significantly ( Teng et al., 2021 ).

A large body of literature on the relationship between problematic smartphone use and academic performance has illustrated the varying adverse effects of excessive smartphone obsession ( Durak, 2018 ; Mendoza et al., 2018 ; Rozgonjuk et al., 2018 ). These effects are manifested in three critical ways: first, the more frequently cell phones are used during study, the greater the negative impact on academic performance and achievement; second, students are required to master the basic skills and cognitive abilities to succeed academically, which are negatively affected by excessive cell phone use and addiction ( Sunday et al., 2021 ); third, online game addiction negatively affects students’ learning motivation ( Demir and Kutlu, 2018 ; Eliyani and Sari, 2021 ). However, there is currently a lack of scientifically objective means of effective data collection regarding online game addiction among college students in China, such as big data. Hong R. Z. et al. (2021) and Nong et al. (2023) suggested that the impact of addiction on students’ learning should be explored more deeply.

Since the 1990s, learning engagement has been regarded as a positive behavioral practice in learning in Europe and the United States, and plays an important role in the field of higher education research ( Axelson and Flick, 2010 ). Recently, studies on learning engagement among college students have also been a hot topic in various countries ( Guo et al., 2021 ). According to Fredricks et al. (2004) , learning engagement includes three dimensions: behavioral, emotional, and cognitive.

The concept of behavioral engagement encompasses three aspects: first, positive behavior in the classroom, such as following school rules and regulations and classroom norms; second, engagement in learning; and third, active participation in school activities ( Finn et al., 1995 ). Emotional engagement refers to students’ responses to their academic content and learning environment. The emotional responses to academic content include students’ emotional responses such as interest or disinterest in learning during academic activities ( Kahu and Nelson, 2018 ), while the emotional responses to the learning environment refer to students’ identification with their peers, teachers, and the school environment ( Stipek, 2002 ). Cognitive engagement is often associated with internal processes such as deep processing, using cognitive strategies, self-regulation, investment in learning, the ability to think reflectively, and making connections in daily life ( Khan et al., 2017 ). Cognitive engagement emphasizes the student’s investment in learning and self-regulation or strategies.

According to Yang X. et al. (2021) , learning engagement refers to students’ socialization, behavioral intensity, affective qualities, and use of cognitive strategies in performing learning activities. Besides, Kuh et al. (2007) argued that learning engagement was “the amount of time and effort students devote to instructional goals and meaningful educational practices.” Learning engagement is not only an important indicator of students’ learning process, but also a significant predictor of students’ academic achievement ( Zhang, 2012 ). It is also an essential factor in promoting college students’ academic success and improving education quality.

As one of the crucial components of students’ learning motivation ( Han and Lu, 2018 ), achievement motivation is the driving force behind an individual’s efforts to put energy into what he or she perceives to be valuable and meaningful to achieve a desired outcome ( Story et al., 2009 ). It can be considered as achievement motivation when an individual’s behavior involves “competing at a standard of excellence” ( Brunstein and Heckhausen, 2018 ). Students’ achievement motivation ensures the continuity of learning activities, achieving academic excellence and desired goals ( Sopiah, 2021 ). Based on the concept of achievement motivation, academic achievement motivation refers to the mental perceptions or intentions that students carry out regarding their academic achievement, a cognitive structure by which students perceive success or failure and determine their behavior ( Elliot and Church, 1997 ). Related research also suggests that motivation is one variable that significantly predicts learning engagement ( Xiong et al., 2015 ).

Therefore, it is worthwhile to investigate the internal influence mechanism of college students’ online game addiction on their reduced academic achievement motivation and the role of learning engagement, which is also an issue that cannot be ignored in higher education research. The present study explored the relationship between online game addiction, learning engagement, and reduced academic achievement motivation among college students by establishing a structural equation model (SEM) to shed light on the problem of online game addiction among college students.

2. Research model and hypotheses

2.1. research model.

Previous research usually regarded learning engagement as a variable of one or two dimensions, and scholars tend to favor the dimension of behavioral engagement. However, other ignored dimensions are inseparable parts of learning engagement ( Dincer et al., 2019 ). In a multi-dimensional model, the mutual terms of each dimension form a single composite structure. Therefore, the present study took the structure proposed by Fredricks et al. (2004) as a reference, divided learning engagement into behavioral, emotional, and cognitive dimensions as mediating variables, and explored the relationship between online game addiction, learning engagement, and reduced academic achievement motivation. The research frame diagram is shown in Figure 1 .

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Figure 1 . The research model.

2.2. Research questions

2.2.1. the relationship between online game addiction and learning engagement.

Learning engagement has been viewed as a multidimensional concept in previous studies. Finn (1989) proposed the participation-identification model to make pioneering progress in learning engagement study. Schaufeli et al. (2002) suggested that learning engagement was an active, fulfilling mental state associated with learning. Chapman (2002) pointed out affective, behavioral, and cognitive criteria for assessing students’ learning engagement based on previous research. Fredricks et al. (2004) systematically outlined learning engagement as an integration of behavioral, emotional, and cognitive engagement. The updated International Classification of Diseases [ World Health Organization (WHO), 2018a , b ] specifies several diagnostic criteria for gaming addiction, including the abandonment of other activities, the loss of interest in other previous hobbies, and the loss or potential loss of work and social interaction because of gaming. Past studies have shown the adverse effects of excessive Internet usage on students’ learning. Short video addiction negatively affects intrinsic and extrinsic learning motivation ( Ye et al., 2022 ). Students’ cell phone addiction negatively affects academic commitment, academic performance, and relationship facilitation, all of which negatively affect their academic achievement ( Tian et al., 2021 ). The amount of time spent surfing the Internet and playing games has been identified to negatively affect students’ cognitive ability ( Pan et al., 2022 ). College students’ cell phone addiction, mainly reflected in cell phone social addiction and game entertainment addiction, has also been noted to impact learning engagement; specifically, the higher the level of addiction, the lower the learning engagement ( Qi et al., 2020 ). Gao et al. (2021) also showed that cell phone addiction among college students could negatively affect their learning engagement. Choi (2019) showed that excessive use of cell phones might contribute to smartphone addiction, which also affects students’ learning engagement. Accordingly, the following three research hypotheses were proposed.

H1 : Online game addiction negatively affects behavioral engagement.
H2 : Online game addiction negatively affects emotional engagement.
H3 : Online game addiction negatively affects cognitive engagement.

2.2.2. The relationship between learning engagement and reduced academic achievement motivation

Achievement motivation is people’s pursuit of maximizing individual value, which embodies an innate drive, including the need for achievement, and can be divided into two parts: the intention to succeed and the intention to avoid failure ( McClelland et al., 1976 ). On this basis, Weiner (1985) proposed the attributional theory of achievement motivation, suggesting that individuals’ personality differences, as well as the experience of success and failure, could influence their achievement attributions and that an individual’s previous achievement attributions would affect his or her expectations and emotions for the subsequent achievement behavior while expectations and emotions could guide motivated behavior. Birch and Ladd (1997) indicated that behavioral engagement involved positive behavioral attitudes such as hard work, persistence, concentration, willingness to ask questions, and active participation in class discussions to complete class assignments. Students’ attitudes toward learning are positively related to achievement motivation ( Bakar et al., 2010 ). Emotional engagement involves students’ sense of identity with their peers, teachers, and the school environment ( Stipek, 2002 ). Students’ perceptions of the school environment influence their achievement motivation ( Wang and Eccles, 2013 ). Cognitive engagement encompasses the ability to use cognitive strategies, self-regulation, investment in learning, and reflective thinking ( Khan et al., 2017 ). Learning independence and problem-solving abilities predict student motivation ( Saeid and Eslaminejad, 2017 ). Hu et al. (2021) indicated that cognitive engagement had the most significant effect on students’ academic achievement among the learning engagement dimensions, and that emotional engagement was also an important factor influencing students’ academic achievement. Therefore, the following three research hypotheses were proposed:

H4 : Behavioral engagement significantly and negatively affects the reduced academic achievement motivation.
H5 : Emotional engagement significantly and negatively affects the reduced academic achievement motivation.
H6 : Cognitive engagement significantly and negatively affects the reduced academic achievement motivation.

2.2.3. The relationship between online game addiction, learning engagement, and reduced academic achievement motivation

Past studies have demonstrated the relationship between online game addiction and students’ achievement motivation. For example, a significant negative correlation between social network addiction and students’ motivation to progress has been reported ( Haji Anzehai, 2020 ), and a significant negative correlation between Internet addiction and students’ achievement motivation has been reported ( Cao et al., 2008 ). Students addicted to online games generally have lower academic achievement motivation because they lack precise academic planning and motivation ( Chen and Gu, 2019 ). Yayman and Bilgin (2020) pointed out a correlation between social media addiction and online game addiction. Accordingly, there might be a negative correlation between online game addiction and academic achievement motivation among college students.

Students addicted to online games generally have lower motivation for academic achievement because they lack precise academic planning and learning motivation ( Chen and Gu, 2019 ). Similarly, Haji Anzehai (2020) reported a significant negative correlation between social network addiction and students’ motivation to progress.

Learning engagement is often explored as a mediating variable in education research. Zhang et al. (2018) found that learning engagement was an essential mediator of the negative effect of internet addiction on academic achievement in late adolescence and is a key factor in the decline in academic achievement due to students’ internet addiction. Li et al. (2019) noted that college students’ social networking site addiction significantly negatively affected their learning engagement, and learning engagement mediated the relationship between social networking addiction and academic achievement. Accordingly, the following research hypothesis was proposed.

H7 : Learning engagement mediates the relationship between online game addiction and reduced academic achievement motivation.

3. Research methodology and design

3.1. survey implementation.

The present study employed the Questionnaire Star application for online questionnaire distribution. Convenience sampling was adopted to recruit Chinese college students to participate voluntarily. The data were collected from October 2021 to January 2022 from a higher vocational college in Shandong province, China. Participants were first-and second-year students. According to Shumacker and Lomax (2016) , the number of participants in SEM studies should be approximately between 100 and 500 or more. In the present study, 500 questionnaires were returned, and 443 were valid after excluding invalid responses. The mean age of the participants was 18.77 years. There were 157 male students, accounting for 35.4% of the total sample, and 286 female students, accounting for 64.6%.

3.2. Measurement instruments

The present empirical study employed quantitative research methods by collecting questionnaires for data analysis. The items of questionnaires were adapted from research findings based on corresponding theories and were reviewed by experts to confirm the content validity of the instruments. The distributed questionnaire was a Likert 5-point scale (1 for strongly disagree , 2 for disagree , 3 for average , 4 for agree , and 5 for strongly agree ). After the questionnaire was collected, item analysis was conducted first, followed by reliability and validity analysis of the questionnaire constructs using SPSS23 to test whether the scale met the criteria. Finally, research model validation was conducted.

3.2.1. Online game addiction

In the present study, online game addiction referred to the addictive behavior of college students in online games, including mobile games and online games. The present study adopted a game addiction scale compiled by Wu et al. (2021) and adapted the items based on the definition of online game addiction. The adapted scale had 10 items. Two examples of the adapted items in the scale were: “I will put down what should be done and spend my time playing online games” and “My excitement or expectation of playing an online game is far better than other interpersonal interactions.”

3.2.2. Learning engagement

In the present study, learning engagement included students’ academic engagement in three dimensions: behavioral, emotional, and cognitive. The learning engagement scale compiled by Luan et al. (2020) was adapted based on its definition. The adapted scale had 26 questions in three dimensions: behavioral, emotional, and cognitive engagement. Two examples of the adapted items in the scale are: “I like to actively explore unfamiliar things when I am doing my homework” and “I will remind myself to double-check the places where I tend to make mistakes in my homework.”

3.2.3. Reduced academic achievement motivation

Reduced academic achievement motivation in the present study refers to the reduction in college students’ intrinsic tendency to enjoy challenges and achieve academic goals and academic success. The achievement motivation scale developed by Ye et al. (2020) was adapted to measure reduced academic achievement motivation. The adapted scale had 10 items. Two examples of the adapted items in the scale are: “Since playing online games, I do not believe that the effectiveness of learning is up to me, but that it depends on other people or the environment” and “Since I often play online games, I am satisfied with my current academic performance or achievement and do not seek higher academic challenges.”

4. Results and discussion

4.1. internal validity analysis of the measurement instruments.

In the present study, item analysis was conducted using first-order confirmatory factor analysis (CFA), which can reflect the degree of measured variables’ performance within a smaller construct ( Hafiz and Shaari, 2013 ). The first-order CFA is based on the streamlined model and the principle of independence of residuals. According to Hair et al. (2010) and Kenny et al. (2015) , it is recommended that the value of χ 2 / df in the model fitness indices should be less than 5; the root mean square error of approximation (RMSEA) value should be greater than 0.100; the values of the goodness of fit index (GFI) and adjusted goodness of fit index (AGFI) should not be lower than 0.800; the factor loading (FL) values of the constructs should also be greater than 0.500. Based on the criteria above, the items measuring the online game addiction construct were reduced from 10 to seven; the items measuring the behavioral engagement construct were reduced from nine to six; the items measuring the emotional engagement construct were reduced from nine to six; the items measuring the cognitive engagement construct were reduced from eight to six; and the items measuring the reduced academic achievement motivation construct was reduced from 10 to six, as shown in Table 1 .

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Table 1 . First-order confirmatory factor analysis.

4.2. Construct reliability and validity analysis

In order to determine the internal consistency of the constructs, the reliability of the questionnaire was tested using Cronbach’ s α value. According to Hair et al. (2010) , a Cronbach’ s α value greater than 0.700 indicates an excellent internal consistency among the items, and the constructs’ composite reliability (CR) values should exceed 0.700 to meet the criteria. In the present study, the Cronbach’ s α values for the constructs ranged from 0.911 to 0.960, and the CR values ranged from 0.913 to 0.916, which met the criteria, as shown in Table 2 .

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Table 2 . Construct reliability and validity of constructs.

In the present study, convergent validity was confirmed by two types of indicators, FL and average variance extracted (AVE). According to Hair et al. (2011) , an FL value should be greater than 0.500, and items with an FL value less than 0.500 should be removed; and AVE values should be greater than 0.500. In the present study, the FL values of the constructs ranged from 0.526 to 0.932, and the AVE values ranged from 0.600 to 0.805; all dimensions met the recommended criteria, as shown in Table 2 .

According to Awang (2015) and Hair et al. (2011) , the square root of the AVE of each construct (latent variable) should be greater than its correlation coefficient values with other constructs to indicate the ideal discriminant validity. The results of the present study showed that the three constructs of online game addiction, learning engagement, and reduced academic achievement motivation had good discriminant validity in the present study, as shown in Table 3 .

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Table 3 . Discriminant validity analysis.

4.3. Correlation analysis

Pearson’s correlation coefficient is usually used to determine the closeness of the relationship between variables. A correlation coefficient greater than 0.8 indicates a high correlation between variables; a correlation coefficient between 0.3 and 0.8 indicates a moderate correlation between variables; while a correlation of less than 0.3 indicates a low correlation. Table 4 shows the Correlation Analysis results. Online game addiction was moderately negatively correlated with behavioral engagement ( r  = −0.402, p  < 0.001), moderately negatively correlated with emotional engagement ( r  = −0.352, p  < 0.001), slightly negatively correlated with cognitive engagement ( r  = −0.288, p  < 0.001), and slightly positively correlated with reduced academic achievement motivation ( r  = 0.295, p  < 0.001). Behavioral engagement was moderately positively correlated with emotional engagement ( r  = 0.696, p  < 0.001), moderately positively correlated with cognitive engagement ( r  = 0.601, p  < 0.001), and moderately negatively correlated with reduced academic achievement motivation ( r  = −0.497, p  < 0.001). Emotional engagement was moderately positively correlated with cognitive engagement ( r  = 0.787, p  < 0.001) and moderately negatively correlated with reduced academic achievement motivation ( r  = −0.528, p  < 0.001). Cognitive engagement was moderately negatively correlated with reduced motivation for academic achievement ( r  = −0.528, p  < 0.001).

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Table 4 . Correlation analysis.

4.4. Analysis of fitness of the measurement model

According to Hair et al. (2010) and Abedi et al. (2015) , the following criteria should be met in the analysis for measurement model fitness: the ratio of chi-squared and degree of freedom ( χ 2 / df ) should be less than 5; the root mean square error of approximation (RMSEA) should not exceed 0.100; the goodness of fit index (GFI), adjusted goodness of fit index (AGFI), normed fit index (NFI), non-normed fit index (NNFI), comparative fit index (CFI), incremental fit index (IFI) and relative fit index (RFI) should be higher than 0.800; and the parsimonious normed fit index (PNFI) and the parsimonious fitness of fit index (PGFI) should be higher than 0.500. The model fitness indices in the present study were χ 2  = 1434.8, df  = 428, χ 2 / df  = 3.352, RMSEA = 0.073, GFI = 0.837, AGFI = 0.811, NFI = 0.899, NNFI = 0.920, CFI = 0.927, IFI = 0.927, RFI = 0.890, PNFI = 0.827, and PGFI = 0.722. The results were in accordance with the criteria, indicating a good fitness of the model in the present study ( Table 5 ).

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Table 5 . Direct effects analysis.

4.5. Validation of the research model

Online game addiction had a negative effect on behavioral engagement ( β  = −0.486; t  = −9.143; p < 0.001). Online game addiction had a negative effect on emotional engagement ( β  = −0.430; t  = −8.054; p < 0.001). Online game addiction had a negative effect on cognitive engagement ( β  = −0.370; t  = −7.180; p < 0.001). Online game addiction had a positive effect on reduced academic achievement motivation ( β  = 0.19; t = −2.776; p < 0.01). Behavioral engagement had a negative effect on reduced academic achievement motivation ( β  = −0.238; t  = −3.759; p < 0.001). Emotional engagement had a negative effect on reduced academic achievement motivation ( β  = −0.221; t  = −2.687; p < 0.01), and cognitive engagement had a negative effect on reduced academic achievement motivation ( β  = −0.265; t  = −3.581; p < 0.01), as shown in Figure 2 Table 6 .

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Figure 2 . Validation of the research model. *** p  < 0.001.

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Table 6 . Indirect effects analysis.

Cohen’ s f 2 is an uncommon but valuable standardized effect size measure that can be used to assess the size of local effects ( Selya et al., 2012 ). When f 2 reaches 0.02 it represents a small effect size, 0.150 represents a medium effect size, and 0.350 represents a high effect size ( Hair et al., 2014 ). The explanatory power of online game addiction on behavioral engagement was 23.6%, and f 2 was 0.309. The explanatory power of online game addiction on emotional engagement was 18.5%, and f 2 was 0.227. The explanatory power of online game addiction on cognitive engagement was 13.7%, and f 2 was 0.159. The explanatory power of behavioral, emotional, and cognitive engagement on reduced academic achievement motivation was 23.9%, and f 2 was 0.314. Figure 2 illustrates the above findings.

4.6. Indirect effects analysis

Scholars are often interested in whether variables mediate the association between predicting and outcome variables. Therefore, mediating variables can partially or entirely explain the association ( Hwang et al., 2019 ). In research fields such as psychology and behavior, where the research situation is often more complex, multiple mediating variables are often required to clearly explain the effects of the independent variables on the dependent variables ( MacKinnon, 2012 ). Scientific quantitative research requires tests of confidence interval (CI; Thompson, 2002 ), and the standard value of the test numbers is often determined by 95% CI ( Altman and Bland, 2011 ). CI value not containing 0 indicates the statistical significance of the analysis results ( Nakagawa and Cuthill, 2007 ). According to the statistical results shown in Table 4 , behavioral engagement significantly positively mediated the relationship between online game addiction and reduced academic achievement motivation with a path coefficient of 0.230 and 95% CI ranging from 0.150 to 0.300 (excluding 0), p < 0.01; emotional engagement positively mediated the relationship between online game addiction and reduced academic achievement motivation with a path coefficient of 0.209, 95% CI ranging from 0.130 to 0.292 (excluding 0), p < 0.01; cognitive engagement positively mediated the relationship between online game addiction and reduced academic achievement motivation with a path coefficient of 0.170, 95% CI ranging from 0.100 to 0.250 (excluding 0), p < 0.01, as shown in Table 6 .

4.7. Discussion

4.7.1. analysis of the relationship between online game addiction and learning engagement.

Online game addiction is often negatively associated with students’ learning. For example, the problematic use of short videos was reported as negatively affecting students’ behavioral engagement, while behavioral engagement positively affected students’ emotional and cognitive engagement ( Ye et al., 2023 ). Meral (2019) highlighted that students’ learning attitudes and academic performance had a negative relationship with students’ addiction to online games. Demir and Kutlu (2018) found that online game addiction negatively affects students’ learning motivation. As the level of students’ game addiction increased, the level of their communication skills decreased ( Kanat, 2019 ). Furthermore, Tsai et al. (2020) pointed out a negative correlation between online game addiction and peer relationships as well as students’ learning attitudes. According to the results of the research model validation, it can be observed that: online game addiction negatively affected behavioral engagement, emotional engagement, and cognitive engagement. Therefore, it can be stated that online game addiction had significant and negative effects on all dimensions of learning engagement.

Online game addiction in the present study included aspects of computer game addiction and mobile phone game addiction. The results of the present study are consistent with the findings of Gao et al. (2021) , Choi (2019) , and Qi et al. (2020) , who pointed out that college students’ addiction to cell phones negatively affected their learning engagement.

4.7.2. Analysis of the relationship between learning engagement and reduced academic achievement motivation

For technology education in higher education, students’ intrinsic motivation for academic study predicts their learning engagement ( Dunn and Kennedy, 2019 ). In addition, learning engagement is positively correlated with academic achievement ( Fredricks and McColskey, 2012 ). Based on the research model validation results, behavioral, emotional, and cognitive engagement all negatively affected reduced academic achievement motivation. The findings are consistent with Hu et al.’s (2021) study which pointed out that cognitive engagement in the learning engagement dimension had the most significant effect on students’ academic achievement, and that emotional engagement was also an essential factor influencing students’ academic achievement. Lau et al. (2008) showed that achievement motivation positively predicted cognitive engagement in the learning engagement dimension. Mih et al. (2015) noted that achievement motivation positively predicted behavioral and emotional engagement in the learning engagement dimension. The present study supported the above discussion by confirming the association between learning engagement and reduced academic achievement motivation.

4.7.3. Analysis of the mediating role of learning engagement

According to the indirect effects analysis results of the present study, learning engagement negatively mediated the relationship between online game addiction and reduced academic achievement motivation. The findings support Haji Anzehai’s (2020) conclusion that social network addiction negatively correlated with students’ motivation to progress ( Haji Anzehai, 2020 ). It is also consistent with the findings of Chen and Gu (2019) that students addicted to online games generally had lower academic achievement motivation due to a lack of precise academic planning and motivation. Cao et al. (2008) found a significant negative correlation between Internet addiction and students’ achievement motivation. Similarly, Zhang et al. (2018) explored the intrinsic influencing mechanism of students’ Internet addiction on academic achievement decline in their late adolescence by identifying learning engagement as the important mediating variable. Li et al. (2019) proposed that social networking site addiction among college students significantly negatively affected learning engagement and that learning engagement mediated the relationship between social network addiction and students’ academic achievement. The present study findings also support the discussion above.

5. Conclusion and suggestions

5.1. conclusion.

Currently, the problem of online game addiction among college students is increasing. The relationship between online game addiction, learning engagement, and reduced academic achievement motivation still needs to be explored. The present study explored the relationships between the three aforementioned variables by performing SEM. The results of the study indicated that: (1) online game addiction negatively affected behavioral engagement; (2) online game addiction negatively affected emotional engagement; (3) online game addiction negatively affected cognitive engagement; (4) behavioral engagement negatively affected reduced academic achievement motivation; (5) emotional engagement negatively affected reduced academic achievement motivation; (6) cognitive behavioral engagement negatively affected reduced academic achievement motivation; (7) learning engagement mediated the relationship between online game addiction and reduced academic achievement motivation.

According to the research results, when college students are addicted to online games, their learning engagement can be affected, which may decrease their behavioral, emotional, and cognitive engagement; their academic achievement motivation may be further reduced and affect their academic success or even prevent them from completing their studies. The mediating role of learning engagement between online game addiction and reduced academic achievement motivation indicates that reduced academic achievement motivation influenced by online game addiction could be prevented or weakened by enhancing learning engagement.

5.2. Suggestions

Universities and families play a crucial role in preventing online game addiction among college students. One of the main reasons college students play online games may be that they lack an understanding of other leisure methods and can only relieve their psychological pressure through online games ( Fan and Gai, 2022 ). Therefore, universities should enrich college students’ after-school leisure life and help them cultivate healthy hobbies and interests. Besides, a harmonious parent–child relationship positively affects children’s learning engagement ( Shao and Kang, 2022 ). Parents’ stricter demands may aggravate children’s game addiction ( Baturay and Toker, 2019 ). Therefore, parents should assume a proper perspective on the rationality of gaming and adopt the right approach to guide their children.

One key factor influencing the quality of higher education is students’ learning engagement. The integration of educational information technology has disrupted traditional teaching methods. This trend has accelerated in the context of COVID-19. College students’ growth mindset can impact their learning engagement through the role of the perceived COVID-19 event strength and perceived stress ( Zhao et al., 2021 ). Moreover, students’ self-regulated learning and social presence positively affect their learning engagement in online contexts ( Miao and Ma, 2022 ). Students’ liking of the teacher positively affects their learning engagement ( Lu et al., 2022 ). Their perceived teacher support also positively affects their learning engagement ( An et al., 2022 ). Hence, educators should focus on teacher support and care in the teaching and learning process.

Students’ motivation for academic achievement can often be influenced by active interventions. Cheng et al. (2022) noted that the cumulative process of students gaining successful experiences contributed to an increased sense of self-efficacy, motivating them to learn. Zhou (2009) illustrated that cooperative learning motivated students’ academic achievement. In addition, Hong J. C. et al. (2021) showed that poor parent–child relationships (such as the behavior of “mama’ s boy” in adults) had a negative impact on students’ academic achievement motivation, and they concluded that cell phone addiction was more pronounced among students with low academic achievement motivation. Hence, enhancing students’ academic achievement motivation also requires family support.

5.3. Research limitations and suggestions for future research

Most of the past studies on the impact of online game addiction on academics have used quantitative research as the research method. The qualitative research approach regarding students’ online game addiction should not be neglected. By collecting objective factual materials in the form of qualitative research such as interviews a greater understanding of students’ actual views on games and the psychological factors of addiction can be achieved. Therefore, future studies could introduce more qualitative research to study online game addiction.

To pay attention to the problem of students’ online game addiction, universities and families should not wait until they become addicted and try to remedy it, but should start to prevent it before it gets to that stage. In terms of developing students’ personal psychological qualities, students’ sensation-seeking and loneliness can significantly affect their tendency to become addicted to online games ( Batmaz and Çelik, 2021 ). Adolescents’ pain intolerance problems can also contribute to Internet overuse ( Gu, 2022 ). Emotion-regulation methods affect the emotional experience and play a vital role in Internet addiction ( Liang et al., 2021 ). In this regard, it is necessary to pay attention to students’ mental health status and to guide them to establish correct values and pursue goals through psychological guidance and other means.

In addition to individual factors, different parenting can considerably impact adolescents. Adolescents who tend to experience more developmental assets are less likely to develop IGD ( Xiang et al., 2022a ), and external resources can facilitate the development of internal resources, discouraging adolescents from engaging in IGD ( Xiang et al., 2022b ). Relevant research indicates that the most critical factor in adolescents’ game addiction tendency comes from society or their parents rather than being the adolescents’ fault ( Choi et al., 2018 ). Adolescents who tend to be addicted to online games may have discordant parent–child relationships ( Eliseeva and Krieger, 2021 ). Better father-child and mother–child relationships predict lower initial levels of Internet addiction in adolescents ( Shek et al., 2019 ). Family-based approaches such as improved parent–child relationships and increased communication and understanding among family members can be a direction for adolescent Internet addiction prevention ( Yu and Shek, 2013 ).

At the school level, a close teacher-student relationship is one of the main factors influencing students’ psychological state. Students’ participation in and control over the teaching and learning process as well as their closeness to teachers can increase their satisfaction and thus enhance their learning-related well-being ( Yang J. et al., 2021 ). More school resources can lead to higher adolescent self-control, attenuating students’ online gaming disorders ( Xiang et al., 2022c ).

Data availability statement

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

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

R-QS, and J-HY: concept and design and drafting of the manuscript. R-QS, and J-HY: acquisition of data and statistical analysis. G-FS, and J-HY: critical revision of the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by Beijing Normal University First-Class Discipline Cultivation Project for Educational Science (Grant number: YLXKPY-XSDW202211). The Project Name is “Research on Theoretical Innovation and Institutional System of Promoting the Modernization of Vocational Education with Modern Chinese Characteristics”.

Conflict of interest

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

Publisher’s note

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

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Keywords: college students, online game addiction, learning engagement, reduced academic achievement motivation, online games

Citation: Sun R-Q, Sun G-F and Ye J-H (2023) The effects of online game addiction on reduced academic achievement motivation among Chinese college students: the mediating role of learning engagement. Front. Psychol . 14:1185353. doi: 10.3389/fpsyg.2023.1185353

Received: 13 March 2023; Accepted: 08 June 2023; Published: 13 July 2023.

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Copyright © 2023 Sun, Sun and Ye. 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: Jian-Hong Ye, [email protected]

† These authors have contributed equally to this work and share first authorship

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.

Proceedings of the Positive Technology International Conference 2023 Positive Technology: Possible Synergies between Emerging Technologies and Positive Psychology (PT 2023)

The Relationship Between Positive Education, Learning with Happiness, Motivation in Learning and Academic Performance in Hong Kong

Research has shown a relationship between learning with happiness and motivation in learning, but the relationship between these aspects of learning and academic performance is not clear. This study’s purpose was to understand these associations in sample of first grade students in Hong Kong. The students’ parents completed a questionnaire designed for this study. The overall score for constituted two subscales (learning with happiness, intrinsic motivation in learning) that were moderately correlated. Two groups of first graders (N = 155) were assessed for the first time in either the 2018/19 and in 2020/21 school year and followed to second grade in the 2019/2020 or 2021/2022 school year. However, academic performance between first grade and second grade in the academic year 2018/19 was not significantly different, in the academic year 2020/21 second grade academic performance was better than first grade. The results have implications for understanding longitudinal effects of happiness in in learning, motivation in learning, and internal/external motivation in predicting academic performance. This study is significant because it is the first study in this line of research to examine first graders.

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