Learning and motivation. Which motivation is important and how to foster it

Steinmayr, Ricarda Learning and motivation. Which motivation is important and how to foster it Becker, Michael [Hrsg.]; Dignath, Charlotte [Hrsg.]; Bihler, Lilly-Marlen [Hrsg.]; Gaspard, Hanna [Hrsg.]; McElvany, Nele [Hrsg.]: Socio-emotional and cognitive development in learning. Educational goals in competition?! Münster ; New York : Waxmann 2026, S. 93-109. - (Dortmunder Symposium der Empirischen Bildungsforschung; 9) Quellenangabe/ Reference: Steinmayr, Ricarda: Learning and motivation. Which motivation is important and how to foster it - In: Becker, Michael [Hrsg.]; Dignath, Charlotte [Hrsg.]; Bihler, Lilly-Marlen [Hrsg.]; Gaspard, Hanna [Hrsg.]; McElvany, Nele [Hrsg.]: Socio-emotional and cognitive development in learning. Educational goals in competition?! Münster ; New York : Waxmann 2026, S. 93-109 - URN: urn:nbn:de:0111-pedocs-345098 - DOI: 10.25656/01:34509 https://nbn-resolving.org/urn:nbn:de:0111-pedocs-345098 https://doi.org/10.25656/01:34509 in Kooperation mit / in cooperation with: http://www.waxmann.com Nutzungsbedingungen Terms of use Dieses Dokument steht unter folgender Creative Commons-Lizenz: http://creativecommons.org/licenses/by-nc-sa/4.0/deed.de - Sie dürfen das Werk bzw. den Inhalt unter folgenden Bedingungen vervielfältigen, verbreiten und öffentlich zugänglich machen sowie Abwandlungen und Bearbeitungen des Werkes bzw. Inhaltes anfertigen: Sie müssen den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen. Dieses Werk bzw. der Inhalt darf nicht für kommerzielle Zwecke verwendet werden. Die neu entstandenen Werke bzw. Inhalte dürfen nur unter Verwendung von Lizenzbedingungen weitergegeben werden, die mit denen dieses Lizenzvertrages identisch oder vergleichbar sind. 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Kontakt / Contact: peDOCS DIPF | Leibniz-Institut für Bildungsforschung und Bildungsinformation Informationszentrum (IZ) Bildung E-Mail: [email protected] Internet: www.pedocs.de Ricarda Steinmayr Learning and motivation Which motivation is important and how to foster it Both laypersons and experts widely regard motivation as a key determinant of academic performance, seeing it as both a prerequisite and an educational goal in its own right (Spinath, 2022). In a world characterized by rapid change, sustained motivation to acquire new knowledge is essential for active partici‐ pation in society. Furthermore, motivation is thought to be relatively amenable to influence, making it a potentially powerful lever in educational settings. This raises an important empirical question: Does the commonly held belief in the importance of motivation for academic achievement hold up under scientific scrutiny? 1. The power of motivation in explaining interindividual differences in academic achievement In academic literature, motivation refers to the entirety of processes that gov‐ ern the initiation, direction, and intensity of behavior. It encompasses both internal states (such as needs or goals) and external influences that drive or guide behavior (Heckhausen & Heckhausen, 2018). Empirical evidence sup‐ ports a relationship between motivation and academic achievement. Meta-anal‐ yses consistently demonstrate positive associations between these constructs (e.g., Cerasoli et al., 2014; Howard et al., 2021; Wirthwein et al., 2013). How‐ ever, it is crucial to note that “motivation” is an umbrella term that encom‐ passes a variety of constructs (for a detailed overview see Spinath, 2022). These constructs are typically categorized into three main groups: goal orientations, achievement motives, and expectancy-value variables (Elliot & Church, 1997; Murphy & Alexander, 2000; Pintrich et al., 2003). Of these, constructs within the expectancy-value category—such as ability self-concepts, self-efficacy, in‐ terest, and intrinsic motivation—show the strongest correlations with academic achievement, with medium to high effect sizes (Cerasoli et al., 2014; Howard et al., 2021; Möller et al., 2009; Multon et al., 1991; Schiefele et al., 1993; Stajkovic et al., 2018; Steinmayr et al., 2018). Achievement motives, generally conceptu‐ 94 Ricarda Steinmayr alized as trait-like dispositions subdivided into “hope for success” and “fear of failure” are also associated with academic performance (Bjørnebekk et al., 2013; Steinmayr et al., 2019b; Steinmayr & Spinath, 2009). Hope for success correlates positively with academic outcomes, while fear of failure shows a negative corre‐ lation. Goal orientations, however, tend to exhibit only small correlations with academic achievement; specifically, work-avoidance goals are negatively asso‐ ciated with performance (particularly in mathematics), whereas approach-per‐ formance and learning goals have small positive correlations with achievement (Noordzij et al., 2021; van Yperen et al., 2015; Wirthwein et al., 2013). Figure 1 illustrates the varying correlations between different motivational constructs and academic achievement. While these findings indicate that motivational constructs share a meaning‐ ful proportion of variance with academic performance, bivariate correlations alone provide an incomplete picture. Motivation is related to other power‐ ful predictors such as intelligence (Bergold & Steinmayr, 2016) and person‐ ality (Steinmayr et al., 2011). These are also related to academic outcomes and the associations are sometimes even stronger (see Roth et al., 2015, for a meta-analysis on the association between intelligence and grades). Thus, some researchers even argue that motivation lacks predictive power for academic achievement beyond what is explained by intelligence (e.g. Gagné & St Père, 2002). Consequently, establishing the importance of motivation for academic success requires demonstrating its incremental validity—namely, the extent to which motivation contributes to the explanation of academic performance over and above other key predictors. Figure 1: Correlations between different motivational constructs and academic achievement (goals: Noordzij et al., 2021; van Yperen et al., 2015; Wirthwein et al., 2013; achievement motives: Bjørnebekk et al., 2013; Steinmayr et al., 2019b; Steinmayr & Spinath, 2009; expectancy-value constructs: Cerasoli et al., 2014; Howard et al., 2021; Möller et al., 2009; Multon et al., 1991; Schiefele et al., 1993; Stajkovic et al., 2018; Steinmayr et al., 2018). Goals pertain to learning goals, performance-avoidance and -ap‐ proach goals and work avoidance. Learning and motivation 95 Figure 2: Graphical illustration of the incremental validity of motivation in predicting academic achieve‐ ment above and beyond intelligence. Figure 2 illustrates this critical concept in understanding the role of motivation in academic performance. As depicted, some variance in academic achievement is unaccounted for by both constructs. Some variance in academic achievement is only explained by interindividual differences in intelligence, some only by interindividual differences in motivation and some by both. In several stud‐ ies, we explored the incremental validity of motivation beyond intelligence, personality, or a combination of both when predicting academic achievement (Lauermann et al., 2020; Steinmayr & Spinath, 2007, 2009; Steinmayr et al., 2011, 2018, 2019; Steinmayr & Meißner, 2013). One such study (Steinmayr & Spinath, 2009) exemplifies this relationship. Figure 3 displays the main results. Figure 3: Graphical illustration of the commonality analysis results performed by Steinmayr and Spinath (2009). In Figure 3, the medium grey portions of the bars in the results represent the unique variance in academic achievement attributed to motivation alone, even 96 Ricarda Steinmayr after controlling for intelligence. Similar findings have been observed when controlling for personality alone or both intelligence and personality (Stein‐ mayr & Spinath, 2007; Steinmayr et al., 2011). In all studies motivation ac‐ counted for variance in academic achievement which was not accounted for by the other constructs. As indicated by the light grey portions of the bars, some motivational constructs predict academic achievement in conjunction with in‐ telligence. This shared variance—particularly strong for the construct “hope for success” in the displayed study—suggests an interaction effect between in‐ telligence and achievement motives. Supporting this, other studies (Bergold & Steinmayr, 2018; Hufer-Thamm et al., 2023) demonstrated that intelligence was positively related to school grades only when accompanied by a certain level of achievement motivation, highlighting an interplay between these constructs in predicting academic success. Besides its incremental validity in explaining variance in academic achieve‐ ment, the importance of motivation is also underlined by its power to explain change in academic achievement. When asked about their child’s academic struggles, many parents attribute it to a lack of motivation, believing that re‐ newed motivation could lead to improved performance. Research supports this perception (see Lesperance et al., 2022). Across several studies, motivational constructs have been shown to predict changes in academic achievement, mea‐ sured by both school grades and standardized achievement tests (Steinmayr et al., 2018, 2019; Steinmayr & Spinath, 2009). This is particularly noteworthy given that individual differences in academic performance tend to stabilize over time, limiting the potential for change. Remarkably, the effect of motivational constructs on changes in academic achievement was up to four times greater than the effect of intelligence, underscoring motivation’s critical role in foster‐ ing academic growth (Steinmayr et al., 2019b; Steinmayr & Spinath, 2009). 2. Evaluating the relative importance of motivational constructs in academic achievement Given the diverse array of motivational constructs it is crucial to empirically identify which are most predictive of academic success. Since the specific char‐ acteristics of a sample can influence the observed correlations, it is advisable to examine multiple motivational constructs within a single sample to rule out the possibility that differences in correlations are attributable to the sample’s char‐ acteristics. Furthermore, expectancy-value constructs are often domain-specif‐ ically operationalized, while achievement motives are generally treated as more stable, trait-like characteristics assessed in a broader context. Since contextual‐ Learning and motivation 97 ization affects the association between constructs (cf. Michel et al., 2022), it is essential to assess both motivational constructs and achievement outcomes at equivalent levels of specificity. To ensure the generalizability of findings across academic domains, it is also recommended to cross-validate results in multiple areas. Another challenge in motivational research is multicollinearity among constructs. Certain constructs, such as ability self-concepts, self-efficacy, and expectations for success, often overlap to the extent that they are nearly indis‐ tinguishable empirically (e.g., Marsh et al., 2019). Similarly, intrinsic values, intrinsic motivation, and interest are also closely related to each other but also to expectancy variables. Appropriate statistical techniques, such as commonal‐ ity analysis and relative weight analysis, are therefore necessary to disentangle the specific contributions of these constructs (for specifics on relative weight analysis see Johnson & LeBreton, 2004; Tonidandel & LeBreton, 2011). Last but not least, to develop effective educational policies and implement meaningful school reforms, it is essential to gather strong empirical evidence on whether different motivational constructs can account for variations in school perfor‐ mance beyond the effects of other important variables, such as intelligence and prior achievement. Excluding these latter factors risks overstating the role of motivation in academic success. Taking into account these thoughts, Steinmayr et al. (2019) sought to address these methodological considerations by examining various motivational con‐ structs alongside prior performance and intelligence, with all variables assessed at comparable levels of specificity. We evaluated constructs from expectancyvalue theory, including values and ability self-concepts, as well as achievement motives (hope for success and fear of failure), achievement goals (learningapproach, performance-approach, performance-avoidance), and work avoid‐ ance. Each variable was measured across different academic domains (general academic ability, math, and German), enabling cross-validation across sub‐ ject areas and ensuring consistent specificity levels. A relative weight analy‐ sis, which quantifies the relative importance of each predictor in explaining variance in a criterion (LeBreton & Tonidandel, 2008), was employed due to the high intercorrelations among motivational constructs. The relative weight ε can be interpreted as an indicator of the relative importance of each motiva‐ tional construct compared to other predictors and its thought to represent the share of explained variance in the specific grade by this specific motivational construct. In the study by Steinmayr et al. (2019), prior math grades emerged as the most significant predictor of subsequent math grades (explaining 45 % of the unique variance), followed by math self-concept (19 % ). Students’ math task values (9 % ), learning goals (5 % ), work avoidance (7 % ), fear of failure, and hope for success (6 % ) did not significantly differ in their contributions. Notably, 98 Ricarda Steinmayr performance goals and intelligence did not significantly predict achievement when analyzed alongside prior grades. Similar patterns were observed for over‐ all school achievement and language arts grades. Here again, among all moti‐ vational variables ability self-concepts and values were the strongest predictors (for more details see Steinmayr et al., 2019b). In a similar study, considering further variables such as grit – the consistency of interests and persistence in the pursuit of long-term goals (Duckworth et al., 2007) – and personality – operationalized as the Big Five of personality (Costa & McCrae, 1995) – Stein‐ mayr et al. (2018) also found expectancy-value variables to be strongest pre‐ dictors of academic achievement among all considered motivational variables. However, in both studies, expectancy-variables were more strongly associated with academic achievement than value variables. These findings held regardless of whether prior performance and intelligence were controlled (see also Stein‐ mayr & Spinath, 2009; Steinmayr et al., 2011, 2018). Thus, regarding the ques‐ tion of which motivational constructs are particularly influential in shaping academic performance these studies demonstrated that domain-specific ability self-concepts, especially in math, are critical predictors of academic achieve‐ ment. However, when it comes to achievement-related choices—such as voca‐ tional or academic pathways—values, particularly intrinsic values, become as significant as ability self-concepts (Steinmayr & Spinath, 2010). Consequently, in the following discussion, I will focus on expectancy-value constructs, which are both central to academic achievement-related criteria. 3. How to foster ideal expectancies and values Before fostering expectancy constructs, it is essential to first determine the de‐ sired level of development. The question whether individuals should strive for a realistic or an optimistically biased self-view has especially been discussed with regard to ability self-concepts. Unlike intrinsic motivation, which is broadly beneficial if it is high, the optimal self-concept is less straightforward. Should one aim for a self-perception that aligns with actual abilities, or is a positively biased (overestimated) self-view more advantageous? If so, shall I overestimate myself just a little or greatly? The results of empirical research on self-estima‐ tion biases and academic performance are mixed, with some studies finding positive effects and others finding negative ones. These inconsistencies may arise from theoretical and statistical issues that often confound self-estima‐ tion bias with self-view effects (Humberg et al., 2018, 2019). Recent work by Paschke et al. (2020, 2023) clarifies that positively viewing one’s competen‐ cies—irrespective of actual ability levels—can have a favorable impact on aca‐ demic achievement. Learning and motivation 99 If a positive self-concept (and intrinsic motivation) is beneficial, what fos‐ ters such an outlook? Several models explain the development of ability selfconcepts across different domains, including the I/E model (Marsh, 1986), the big-fish-little-pond effect (e.g. Marsh et al., 2004), and the situated expectancyvalue model (e.g. Eccles & Wigfield, 2020). According to the latter model, so‐ cializers’ beliefs and behaviors—particularly those of teachers (cf. Steinmayr et al., 2019a)—affect students’ self-concepts, which are empirically equivalent to expectations of success, and also values. However, the practical significance of teacher expectancy effects on students’ development has occasionally been questioned due to their relatively small size (Jussim, 2017; Jussim & Harber, 2005). But most studies have concentrated on school performance, thus, this critique may overlook the possibility that teacher expectations influence nu‐ merous other important aspects of students’ lives beyond academic achieve‐ ment, for example ability self-concepts and values, as hypothesized by the sit‐ uated expectancy-value model. The study by Bergold and Steinmayr (2023) in‐ vestigated how teachers’ expectations regarding students’ abilities impact var‐ ious student outcomes, including expectancy-value constructs. The longitudi‐ nal study involved 1,092 ninth-grade students from vocational track schools in Germany. Students completed assessments of their math and reading com‐ petencies, ability self-concepts, intrinsic motivation, academic and vocational aspirations, and subjective well-being. Teachers rated students’ abilities in math and German using a seven-point scale based on national performance distribu‐ tions. Our analysis revealed unique effects of math teachers’ expectations on students’ change in math performance, ability self-concepts, and educational aspirations. Though the effect of math teachers’ expectations on intrinsic mo‐ tivation was marginally insignificant (p = .07), intrinsic motivation at base‐ line predicted changes in math performance. German but not math teachers’ expectancies affected students’ life satisfaction. Teacher judgments thus affect many student outcomes at the same time, among them expectancy-value con‐ structs, underscoring their practical importance for students’ lives. Further‐ more, they seem to be more important for expectancies than for intrinsic values. Thus, the question remains which variables additionally contribute to change of value variables. The situated expectancy-value model (Eccles & Wigfield, 2020) does not only explain differences in intrinsic values but also in other values such as utility or attainment values and costs. Recently there has been a lot of research on inter‐ ventions, especially minimal interventions to change values but also expectan‐ cies especially of disadvantaged groups (cf. Rosenzweig et al., 2020, 2022). Moreover, there is also increased interest in domain-specific beliefs about the nature of abilities, following research in implicit intelligence theories / mindsets. 100 Ricarda Steinmayr Implicit beliefs about the nature of intelligence—whether viewed as fixed or malleable—are also pertinent to understanding motivational dynamics. Mind‐ sets refer to an individual’s subjective beliefs about whether specific attributes, such as intelligence or mathematical ability, are unchangeable or can be devel‐ oped and improved (e.g., Dweck & Yeager, 2019). The belief that an attribute is fixed represents a fixed mindset, while the belief that it can be cultivated reflects a growth mindset. Unlike a fixed mindset, a growth mindset is theorized to enhance students’ motivation and academic performance, especially among those facing challenges. Research indicates that when teachers view intelligence as unchangeable, their behavior tends to be more achievement-oriented, focus‐ ing on performance outcomes rather than the learning process (e.g., LaCosse et al., 2021; Park et al., 2016). However, such performance-oriented behavior has been linked to lower levels of student motivation, as it may create a highpressure environment that prioritizes results over effort and improvement (e.g., Ames, 1992; Wirthwein et al., 2021). Furthermore, the nature of the learning environment significantly influences the way success is perceived and pursued. In achievement-oriented learning environments, the emphasis is on demon‐ strating one’s competencies, with success often being defined in comparison to the accomplishments of peers (Ciani et al., 2010; Dweck & Leggett, 1988). This focus on relative performance strengthens the association between indi‐ vidual success and external validation. In contrast, learning-oriented environ‐ ments prioritize the process of acquiring knowledge and improving personal competencies in those environments, success is measured by individual growth and mastery, leading to a weaker association with external comparisons or peer-relative outcomes (Dweck & Leggett, 1988; Meece et al., 2006). The selfdetermination theory explains individual differences in intrinsic motivation, among others, by different feeling of competences. Thus, in a learning-oriented environment, even students who receive performance feedback (in Germany mostly operationalized by grades) suggesting they are not performing as well as their peers can remain intrinsically motivated. Here, irrespective of a stu‐ dents’ performance level, all students have the possibility to increase their com‐ petencies and thus perceive themselves as successful and competent learners which should weaken the association between grades and intrinsic values. In contrast, in a performance-oriented environment, only students outperforming their peer might be motivated which should strengthen the association between performance feedback and intrinsic motivation. Following these rationales, Heyder et al. (2020) investigated whether mathspecific implicit intelligence beliefs affected the association between students’ intrinsic motivation and performance feedback (grades) in math in a large sample of German fourth graders and their 56 teachers. Findings revealed that Learning and motivation 107 nology. Contemporary Educational Psychology, 25(1), 3–53. https://doi.org/10.1006/ ceps.1999.1019 . Nishen, A. K., Streck, H., Kessels, U., & Steinmayr, R. (2024). Feeling joy ×feeling com‐ petent: Predicting math-related occupational aspirations from math grades, gender, and parents’ occupational background via motivational beliefs. Journal of Educa‐ tional Psychology, 116(5), 785–804. https://doi.org/10.1037/edu0000872 . Noordzij, G., Giel, L., & van Mierlo, H. (2021). A meta-analysis of induced achievement goals: the moderating effects of goal standard and goal framing. Social Psychology of Education, 24(1), 195–245. https://doi.org/10.1007/s11218-021-09606-1 . Park, D., Gunderson, E. A., Tsukayama, E., Levine, S. C., & Beilock, S. L. (2016). Young children’s motivational frameworks and math achievement: Relation to teacher-re‐ ported instructional practices, but not teacher theory of intelligence. Journal of Edu‐ cational Psychology, 108(3), 300–313. https://doi.org/10.1037/edu0000064 . Paschke, P., Weidinger, A. F., & Steinmayr, R. (2020). Separating the effects of selfevaluation bias and self-view on grades. Learning and Individual Differences, 83–84, 101940. https://doi.org/10.1016/j.lindif.2020.101940 . Paschke, P., Weidinger, A. F., & Steinmayr, R. (2023). Linear and nonlinear relationships between self-evaluation and self-evaluation bias with grades. 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Steinmayr, R., & Spinath, B. (2007). Predicting school achievement from motivation and personality. Zeitschrift für Pädagogische Psychologie / German Journal of Educational Psychology, 21(3/4), 207–216. https://doi.org/10.1024/1010-0652.21.3.207 . Steinmayr, R., & Spinath, B. (2009). The importance of motivation as a predictor of school achievement. Learning and Individual Differences, 19(1), 80–90. https://doi. org/10.1016/j.lindif.2008.05.004 . Steinmayr, R., & Spinath, B. (2010). Konstruktion und erste Validierung einer Skala zur Erfassung subjektiver schulischer Werte (SESSW) [Construction and first validation of a scale assessing subjective educational task values]. Diagnostica, 56(4), 195–211. https://doi.org/10.1026/0012-1924/a000023 . Steinmayr, R., Bipp, T., & Spinath, B. (2011). Goal orientations predict academic per‐ formance beyond intelligence and personality. Learning and Individual Differences, 21(2), 196–200. https://doi.org/10.1016/j.lindif.2010.11.026 . 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