Socio-emotional and cognitive development in learning. Educational goals in competition?!

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, 109 S. - (Dortmunder Symposium der Empirischen Bildungsforschung; 9) Quellenangabe/ Reference: 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, 109 S. - (Dortmunder Symposium der Empirischen Bildungsforschung; 9) - URN: urn:nbn:de:0111-pedocs-345032 - DOI: 10.25656/01:34503 https://nbn-resolving.org/urn:nbn:de:0111-pedocs-345032 https://doi.org/10.25656/01:34503 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. This document is published under following Creative Commons-License: http://creativecommons.org/licenses/by-nc-sa/4.0/deed.en - You may copy, distribute and transmit, adapt or exhibit the work in the public and alter, transform or change this work as long as you attribute the work in the manner specified by the author or licensor. You are not allowed to make commercial use of the work. If you alter, transform, or change this work in any way, you may distribute the resulting work only under this or a comparable license. Mit der Verwendung dieses Dokuments erkennen Sie die Nutzungsbedingungen an. By using this particular document, you accept the above-stated conditions of use. Kontakt / Contact: peDOCS DIPF | Leibniz-Institut für Bildungsforschung und Bildungsinformation Informationszentrum (IZ) Bildung E-Mail: [email protected] Internet: www.pedocs.de Dortmunder Symposium der Empirischen Bildungsforschung edited by Nele McElvany Volume 9 Michael Becker, Charlotte Dignath, Lilly-Marlen Bihler, Hanna Gaspard, and Nele McElvany (Eds.) Socio-emotional and cognitive development in learning Educational goals in competition?! Waxmann 2026 Münster ⋅New York Bibliographic information published by Die Deutsche Nationalbibliothek Die Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the internet at http://dnb.dnb.de. Dortmunder Symposium der Empirischen Bildungsforschung, volume 9 ISSN 2366-6439 Print-ISBN 978-3-8188-0096-3 E-Book-ISBN 978-3-8188-5096-8 Waxmann Verlag GmbH, 2026 Steinfurter Straße 555, 48159 Münster, Germany Waxmann Publishing Co. P. O. Box 1318, New York, NY 10028, U. S. A. www.waxmann.com [email protected] Cover Design: Anne Breitenbach, Münster Cover photo: N. Lawrenson/peopleimages.com – stock.adobe.com Setting: satz & sonders GmbH, Dülmen This work is available under the license CC BY-NC-SA 4.0: Attribution – Non Commercial – Share Alike 4.0 international ( https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.de ) This licence applies only to the original material. All marked third-party content (e.g., illustrations, photos, quotations, etc.) is excluded from the CC licence, and it may be necessary to obtain further permission from the copyright holder for its reuse. The free availability of the e-book edition of this publication was made possible by the Federal Ministry of Research, Technology and Space, the “Specialised Information Service for Educational Science and Research (FID)”, and a net‐ work of scientific institutions and libraries promoting open access in the edu‐ cational sciences. Bibliothek der Hochschule der Bundesagentur für Arbeit Mannheim Bibliothek der Katholischen Hochschule Nordrhein-Westfalen Köln Bibliothek der Pädagogischen Hochschule Freiburg Bibliothek der Pädagogischen Hochschule Zürich Bibliothek für Bildungsgeschichtliche Forschung des DIPF Berlin Bibliotheksund Informationssystem der Carl von Ossietzky Universität Oldenburg DIPF I Leibniz-Institut für Bildungsforschung und Bildungsinformation Frankfurt a.M. FOM – Hochschule für Oekonomie & Management Essen Freie Universität Berlin / Universitätsbibliothek Gottfried Wilhelm Leibniz Bibliothek – Niedersächsische Landesbibliothek Hannover Hochschulbibliothek der Pädagogischen Hochschule Karlsruhe Hochschulbibliothek der Pädagogischen Hochschule Ludwigsburg IU Internationale Hochschule GmbH Erfurt Justus-Liebig-Universität Gießen / Universitätsbibliothek KIT-Bibliothek Karlsruhe Landesbibliothek Oldenburg Leibniz Institut für Bildungsmedien I Georg-Eckert-Institut Braunschweig Leuphana Universität Lüneburg Pädagogische Hochschule Heidelberg Pädagogische Hochschule Thurgau / Campus-Bibliothek RPTU Kaiserslautern-Landau / Universitätsbibliothek Landau Sächsische Landesbibliothek – Staatsund Universitätsbibliothek Dresden Staatsund Universitätsbibliothek Halle Technische Informationsbibliothek (TIB) Hannover Technische Universität Braunschweig Universitätsund Landesbibliothek Münster Universitätsund Stadtbibliothek Köln Universitätsbibliothek Augsburg Universitätsbibliothek Bayreuth Universitätsbibliothek Bern, Bibliothek von Roll Universitätsbibliothek Bielefeld Universitätsbibliothek Bochum Universitätsbibliothek Chemnitz Universitätsbibliothek der Technischen Universität Berlin Universitätsbibliothek der FernUniversität Hagen Universitätsbibliothek der Humboldt-Universität zu Berlin Universitätsbibliothek der LMU München Universitätsbibliothek Dortmund Universitätsbibliothek Duisburg-Essen Universitätsbibliothek Erlangen-Nürnberg Universitätsbibliothek Graz Universitätsbibliothek Greifswald Universitätsbibliothek Hildesheim Universitätsbibliothek Johann Christian Senckenberg Frankfurt a.M. Universitätsbibliothek Kassel Universitätsbibliothek Klagenfurt Universitätsbibliothek Leipzig Universitätsbibliothek Mainz Universitätsbibliothek Mannheim Universitätsbibliothek Marburg Universitätsbibliothek Passau Universitätsbibliothek Regensburg Universitätsbibliothek Trier Universitätsbibliothek Tübingen Universitätsbibliothek Vechta Universitätsbibliothek Würzburg Universitätsbibliothek Wuppertal Zentralund Hochschulbibliothek Luzern Table of Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Part 1: Designing socio-emotional learning for heterogeneous groups Sara E. Rimm-Kaufman Socio-emotional learning Promising synergies with academic development in early adolescence . . . 15 Elsbeth Stern Learning and performance do not have to come at the expense of socio-emotional development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Florian Schmiedek Learning for at-risk students A dynamic perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Part 2: Emotions, motivation and well-being – the role of socio-emotional aspects in learning Anne C. Frenzel & Thomas Goetz Learning and emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Carmen Zurbriggen Well-being and learning Taking inclusive education as a prominent example . . . . . . . . . . . . . . . . 81 Ricarda Steinmayr Learning and motivation Which motivation is important and how to foster it . . . . . . . . . . . . . . . . 93 . Sara E. Rimm-Kaufman Socio-emotional learning Promising synergies with academic development in early adolescence 1. Introduction Picture Spring Middle School1, a school serving youth between age 11 and 13 in a small northeast city in the United States. Historically, the city was mostly populated by white, middleand working-class families and their children. Since 2000, the city experienced an influx of immigrants and now roughly 1 in 10 people in the city are immigrants (Office of New Americans Report, 2023). Now, Spring Middle School has about 480 students with 32 % born outside of the U.S., representing 28 different home countries. 47 % of the students speak a first language other than English and this school has 21 home languages rep‐ resented. Students come from Guatemala, Burundi, Angola, Sudan, Ethiopia, Iraq, Honduras, Namibia, Gabon, Rwanda among others (LeClaire, 2022). Like most U.S. middle schools, the school strives to meet the needs of early adolescents who are experiencing dramatic developmental changes in physical, emotional, cognitive, and social domains. What makes Spring Middle School particularly unique is the tremendous ethnic diversity of students and the var‐ ied needs of youth and families who are very recent immigrants. This scenario calls attention to a question: How can schools meet psychological needs and promote academic growth among youth with such a wide range of backgrounds and experiences? The solution for this school has been complex. Instead of focusing on aca‐ demic achievement as the sole focus, they broadened their goals to advance a three-dimensional vision of student achievement that includes mastery of skills and knowledge, cultivation of character attributes, and high-quality student work on topics that are relevant and engaging to their students (Berger et al., 2021). Engaging in Crew – daily meetings with about 15–20 students and one adult—is one practice that contributes to this multifaceted vision of student 1 Spring Middle School is a pseudonym. 16 Sara E. Rimm-Kaufman success. Crew creates an opportunity for students to develop positive relation‐ ships with adults and other students, have courageous conversations about is‐ sues in students’ lives, create a sense of community, experience belonging, and reflect on the purpose of school learning (Berger et al., 2021). This anecdote sets the stage for a conversation about how schools can cul‐ tivate social and emotional competencies and create meaningful academic ex‐ periences. This chapter describes the basic definitions of social and emotional learning (SEL), explores typical trajectories of growth of social and emotional competencies (SEC), and examines the ways in which meaningful learning cre‐ ates classroom experiences that optimize academic learning while also meeting the psychological needs of youth. The paper closes with implications for educa‐ tors striving to meet the needs of early adolescents. 2. What is social and emotional learning? The Collaborative for Academic, Social and Emotional Learning (CASEL) is an organization at the University of Illinois-Chicago that is the central-most hub in the U.S. for SEL research, practice and policy. The CASEL definition of SEL is “the process through which all young people and adults acquire and apply the knowledge, skills, and attitudes to develop healthy identities, manage emotions and achieve personal and collective goals, feel and show empathy for others, establish and maintain supportive relationships, and make responsible and caring decisions” (CASEL, 2025). There are two attributes of this definition that require careful attention. The definition points out that all young people and adults can experience SEL. This is important because many people assume that SEL is just for children and youth. In fact, SEL also occurs in adults. For example, when educators learn and apply new intercultural skills for working with students of color or recent immigrants, that represents an aspect of adult SEL. A second attribute of this definition refers to what people learn and can ap‐ ply – SEL involves the development of more than skills. SEL also involves new knowledge and attitudes that people acquire. For instance, in the U.S. there has been a movement to increase cultural responsiveness of SEL. To achieve this goal, educators have had to learn and apply new skills, develop new knowledge about cultures, and change their attitudes about people who were unfamiliar to them in the past (Wanless et al., 2023). All this is necessary before teachers are ready to apply these competencies in their classrooms. By definition, SECs occur as a product of SEL. SECs fall into three broad cat‐ egories: self-skills, social skills, and decision-making (CASEL, 2025), as shown in Figure 1. The broad category of self-skills corresponds to the intrapersonal Socio-emotional learning 17 Figure 1: CASEL model showing the development of social and emo‐ tional competencies competencies of self-awareness and self-management. Self-awareness refers to a person’s ability to understand their own values, thoughts, and emotions and the ways these ideas link to their behavior. Self-awareness involves identifying one’s own emotions, their personal and cultural assets, examination of one’s own prejudices and biases, developing self-efficacy, a growth mindset and a sense of purpose. When an eighth grader describes themselves as viewing mistakes as a normal part of learning and indication that they need to try a different idea to solve a problem, they are demonstrating growth mindset, which is one aspect of self-awareness. Self-management refers to the ability of a person to regulate and manage their thoughts, behaviors, and emotions and to adjust those behav‐ iors to match different contexts. Use of stress management strategies, managing strong emotions, existing self-discipline, showing initiative and directing their attention to personal and collective goals are examples of self-management. Codeswitching, the modification of one’s behavior or way of speaking to match their immediate cultural environment, also requires self-management skills. The broad category of social skills also has two subcategories and includes social awareness and relationship skills. Social awareness refers to the ability to understand the perspective of others, even if they are from cultures and backgrounds that are different than oneself. It involves an understanding that 18 Sara E. Rimm-Kaufman other people have had different experiences than oneself and have beliefs and actions that derive from those experiences. It means showing compassion for others and understanding what was considered right in one historical period is not necessarily considered noble in another. Being able to identify injustices fits within social awareness. Relationship skills refer to people’s ability to create and maintain relationships and manage social situations with diverse groups. A person with strong relationship skills can communicate well, listen carefully to others, work with others to solve problems, navigate conflict well, lead or follow, when the situation calls for it. Relationship skills can mean showing cultural competency, resisting negative social pressure, seeking or offering help. The fifth competency is responsible decision-making, reflecting people’s abil‐ ity to make effective choices about one’s own behavior in a variety of different situations. Responsible decision-making involves being able to think through the consequences of one’s actions and how those actions will influence oneself, others, and society collectively. Responsible decision-making requires openmindedness, learning how to size up complex situations and make judgments, and identifying and enacting solutions for social and personal problems. Social and emotional skills, knowledge and attitudes are multiply deter‐ mined. People learn these at home, school, neighborhood, through social media and via other social interactions. Because classrooms and schools are malleable, a great deal of attention has been given to SEL in those contexts, corresponding to the classroom and school effects described in Figure 1. 3. Educating for social and emotional learning SEL is certainly not new. Educators have been teaching students manners, kind‐ ness, empathy, self-control, and other related skills for decades—even centuries (Osher et al., 2016). What is new is that efforts to improve youth SECs have become an explicit aspect of the school experience of children and youth. There are two main ways that SEL takes shape in schools – by introducing programs designed to enhance SEL or by using SEL-practices organically as a part of the culture of the school. SEL programs are prevalent in the U.S. as evidenced by the $ 765 million spent on such programs between November 2019 and April 2021 (Tyton Part‐ ners, 2021). SEL programs typically include a paper or online manual, training, and coaching that can be purchased and used for in-service professional devel‐ opment. These programs can be described in a few main ways; some integrate SEL into academic instructional content, others offer free-standing lessons on SEL topics, yet others are school-wide approaches that involve classroom and Socio-emotional learning 19 school-level activities designed to enhance students’ attitudes, knowledge and skills while also striving to create a school culture that is conducive to learning social and emotional competencies. Programs can be implemented in individ‐ ual classrooms or school-wide, depending on the program and district. The adoption of SEL programs contrasts to SEL that occurs without pro‐ grams in place. SEL can be naturally embedded into the culture of a school as teachers model social and emotional competencies toward students. For in‐ stance, when youth see respectful interactions among teachers and between teachers and other students, children learn how to interact with peers. Teachers cultivate emotion learning through the daily experiences of classroom life (e.g., a teacher getting frustrated and then talking aloud to calm themselves down when the technology is not working). Whether SEL is delivered through a program or organically via the culture of the school, SEL can be universal and delivered to all students or targeted and designed for people with moderate or chronic, severe needs requiring inten‐ sive, one-on-one work between a school counselor or teacher and the student. Universal SEL resembles the idea of fluoride in water – it is an intervention that can benefit each and every student. However, many students need more intensive support to develop SECs, in which cases offering more one-on-one opportunities to touch base with adults or access to clinical services can address those needs. 4. Trends in social and emotional learning Researchers at the RAND Corporation conducted a nationally representative survey of almost 28,954 teachers and 12,954 principals asking them to rate the importance of teaching a range of social and emotional skills (Hamilton et al., 2019). The survey asked about self-skills including understanding and managing emotions, setting and achieving positive goals, developing a sense of identity, relationships skills including establishing and maintaining positive re‐ lationships and feeling and showing empathy toward others, as well as decisionmaking skills such as making responsible decisions. For all skills measured, over 90 % of principals and teachers described these skills as fairly or very important. For most skills, 97–99 % of educators reported them as fairly or very important. The importance of SEL has been evident in recent policies with 27 states having state standards for SEL for grades K-12 in place as of 2022 (Dermody & Dusen‐ bury, 2022). On one hand, the U.S. has shown a rise in SEL priorities and policies yet there are counterforces at work, as well. SEL has emerged as a political flash‐ point. Many conservative-leaning politicians and conservative parent groups 20 Sara E. Rimm-Kaufman have pushed back on SEL stating that teaching social and emotional compe‐ tencies is outside of the scope of schools and that youth should be learning those values from their families and religious organizations. Most left-leaning politicians and parent groups agree with the premises of SEL. Yet, some leftleaning scholars and educators raise issues because typical SEL is too focused on “mainstream” white U.S. values and does too little to adapt to cultural diversity present in the schools. For example, many strengths in students of color are not recognized as strengths nor are amplified by typical SEL (e.g., the ability of Black students to codeswitch) (Rimm-Kaufman et al., 2023). SEL is not only a U.S. phenomenon. Many schools in Germany use SEL. Some programs focus narrowly on anti-bullying and anti-violence, for instance, whole school anti-bullying problems using approaches by Olweus (1993), the ‘Faustlos’ curriculum-violence prevention, the ProACT+E approach and use of the ‘Fairplayer Manual’ (Scheithauer & Bull, 2008). Other schools focus more broadly on cultivating SECs. For instance, Second Step has been adapted from the U.S. program to be used in Germany, as has the Mindmatters program. Yet other schools have focused on mental health needs of students by adopting programs such as Lion’s Quest that supports adolescents’ development of sense of self, teaches listening, empathy, and wise decision-making through a series of lessons (Cefai et al., 2018). These are all examples of efforts to enhance social and emotional compe‐ tencies. New meta-analytic work shows the ways in which SEL contributes to shortand long-term gains in social, emotional, academic and behavioral out‐ comes (Cipriano et al., 2023; Taylor et al., 2017). SEL approaches have garnered widespread attention impacting both policy and practice. Despite this focus, we know too little about how SECs change over time, especially during adoles‐ cence. 5. The development of social and emotional competencies It is easy to assume that children and youth learn steadily and gradually as they mature. Achievement provides a basis for that assumption in that we know, on average, youth show gains in reading and math skills over time with the greatest magnitude of growth annually in early childhood and then smaller increases during the middle and high school years (Hill et al., 2008). Unlike achievement, youth development of SECs does not show a simple, upward trend. Instead, there are qualitative shifts in development that can lead to interruption and even U-shaped patterns (Soto & Tackett, 2015). Take self-management as an example. One longitudinal study (Ross et al., 2019) showed that self-manage‐ ment (e.g., deciding on a goal and sticking to it) was best modeled quadratically; Socio-emotional learning 21 self-management decreased from age 10 to age 17 followed by an increase to age 18. Findings like this question assumptions in the field. Before investigating fur‐ ther, there are important issues related to measurement of these constructs to raise. Some SECs, such as self-efficacy or growth mindset, can only be assessed through student-report which means outcomes reflect actual presence of SECs as well as students’ perception of themselves relative to others around them. Interpretation of student-report data has some advantages, for instance, the work elevates youth voice and helps researchers understand students’ actual lived experience. However, student-report also introduces bias based on the variation in students’ internal appraisal of their competencies. To pursue inquiry about how SECs develop, the author (S. Rimm-Kaufman) and two colleagues (J. Soland and M. Kuhfeld) identified an ideal data set. A group of California school districts called the CORE districts started conduct‐ ing annual surveys about youth’s development of SECs in 2015 (West et al., 2018). These data were available to researchers for analysis and were ideal be‐ cause of the large sample and use of standardized measures. Thus, the paper by Rimm-Kaufman and colleagues (2024) was designed to better understand developmental trends in SECs. The study was informed by the Stage-Environment Fit Theory (Eccles et al., 1993) which posits that devel‐ opmental declines in motivation and perception of self in adolescence “result from a mismatch between the needs of developing adolescents and the oppor‐ tunities afforded them by their social environments” (Eccles et al., 1993, p. 91). Despite the theory being thirty years old, early adolescents still experience a problematic stage-environment fit. Early adolescent youth develop more need for autonomy and become very sensitive to social comparison, yet they con‐ tinue to need close relationships with adults (National Academies of Sciences, Engineering, and Medicine [NASEM], 2019; Yu et al., 2018). Further, young adolescents desire respect and status and traditional schooling can seem un‐ interesting as they develop a fundamental need to use their skills to improve the contribute to the world around them (Fuligni, 2019). Just as these devel‐ opmental changes are occurring, most youth are moving from elementary to middle school. In elementary school, students typically stay in a classroom with one adult and roughly 25 students. In middle school, students typically move from classroom to classroom and teachers have as many as 130 students in any given day. The demands of middle school are high – teachers expect students to come to class prepared, open only certain tabs on their computer without being distracted by the internet, and work on long term projects without waiting until last minute to complete them. Middle school also means high-stakes grading, ability grouping, more disciplinary action, and an increase in the public eval‐ 22 Sara E. Rimm-Kaufman uation of academic work (Deutsch, 2022). Taken together, this new context creates a challenge for the developing child. To pursue deeper understanding of development of SECs, analyses were conducted to examine how much growth occurs in social and emotional competencies from Grades 4–12? 6. Quantitative analysis and results The CORE data collection included nine districts in California that adminis‐ tered surveys to all students each year starting in 2015, resulting in a sample of 95,998 students in grades four through 12 (roughly ages nine to 18). Collecting data each year resulted in longitudinal data for up to four years. Thus, the analy‐ sis included students who took the survey at least once between 2015 and 2018. The sample of students was almost evenly split between girls (49 % ) and boys (51 % ).2 Roughly 25 % were from families with parents with low educational attainment (i.e., high school or below). The sample was 73 % Latine, 11 % White, 10 % Black, and 7 % Asian. Table 1. Measures Collected in the CORE Districts with Selected Items Constructs Example Items # items Alphas Growth Mindset I can change my intelligence with hard work. I am capable of learning anything. 4 .70 Self-Efficacy I can master the hardest topics in my classes. I can do well on my tests, even when they are difficult. 4 .86 Self-Management I came to class prepared. I paid attention, even when there were distractions. 5 .85 Social Awareness How often did you compliment others’ accomplishments? How well did you get along with students who are different from you? 5 .81 The 18-item measure administered assessed growth mindset, self-efficacy, selfmanagement, and self-awareness, as shown in Table 1. To address the key re‐ 2 Gender data were limited to binary categories of girl and boy. Socio-emotional learning 23 search question, an Accelerated Longitudinal Design (ALD) Growth Models was used. First, multilevel growth curve models were estimated and then lin‐ ear, quadratic, and cubic growth curves were tested. See Rimm-Kaufman et al. (2024) for more details. For three of the four constructs, findings showed a shift in students’ per‐ ceptions of their intrapersonal and interpersonal competencies around 6 th or 7 th grade. Figure 2 shows the patterns. Results for growth mindset generally showed increases in scores over time. Self-efficacy showed a somewhat different pattern: students tended to show consistent declines as they moved from 5 th to 6 th grade and beyond, but that trajectory flattened or even curved upwards in 11 th and 12 th grade. For self-management, students were level in 4 th and 5 th grade, then showed declines between 6 th and 8 th grade, followed by increases in late high school. For social awareness, students showed declines between 6 th and 8 th grade, and, then increases from 9 th through 12 th . Yet another point requires attention. The models indicate considerable stu‐ dent-level variability in the latent slope parameters suggesting that growth in Figure 2: Plots of Model-Based Growth Estimates by Construct Note: The dotted black line represents the model-implied trajectories using the accelerated longitudi‐ nal design. The other lines each represent cohort-specific estimates. The Y-Axis is the number of stan‐ dard deviations from the average in grade 4. Reprinted from Rimm-Kaufman et al. (2024, p. 366). 24 Sara E. Rimm-Kaufman these constructs differs considerably dependent on the student. For example, while students tended to decline in self-efficacy in the early grades, a non‐ negligible percentage showed growth during those years. To describe this phe‐ nomenon quantitatively, as an example, the mean of the linear slope for growth mindset was .17 SDs. If one were to take the square root of the slope variance, it would equal .4 SDs. Thus, a student approximately 2 SDs above the slope mean would have a linear coefficient of .97 SDs, and a student 2 SDs below the slope mean would have a linear coefficient of -.63 SDs. Examples like these indicate that relying only on averages masks considerable variability in these SEC trajectories, a point discussed further in Rimm-Kaufman et al. (2024). 7. Changes in SECs SECs develop in relation to one another and changes in one developmental domain influence another domain. As an example, the findings show a gradual decrease in self-management from 4 th through 11 th grade with a slight uptick between 11 th and 12 th grade, findings that match other longitudinal work (Ross et al., 2019). Also, during early adolescence, the demands of school and the need for self-management intensifies as students shift to middle school. Middle schoolers may perceive drops in their self-management because of the height‐ ened demands. In the presence of dips in self-management, self-efficacy is also likely to drop (Musci et al., 2022). Lower self-management and feeling ineffica‐ cious may create stressful situations that means youth will put their own needs first and show lower social awareness. These developmental changes occur in context of students’ school experi‐ ences. Middle schools tend to be larger than elementary schools. Youth are rating themselves in comparison to others just as their comparison group grows to include a wider social circle at school and likely, online. Growth mindset is an anomaly in that it shows continued growth. Growth mindset interventions have become more common in school, which may be one possible explanation. Another plausible explanation is that some aspects of intelligence (e. g., vocabu‐ lary, working memory) increase during the teen years (Hartshorne & Germine, 2015). The declines in self-efficacy, self-management and social awareness have consequences for action among the educators who are a part of youths’ devel‐ opmental experience. Youth are at a turning point. If adults offer scaffolding to meet the new demands and show positive regard for youth, students will re‐ main engaged in learning. If adults respond with discipline and blame, negative relationships will emerge and students will disengage from school (Engels et al., Socio-emotional learning 31 Durlak, J. (2023). The state of evidence for social and emotional learning: A con‐ temporary meta-analysis of universal school-based SEL interventions. Child Devel‐ opment, 94(5), 1181–1204. https://doi.org/10.1111/cdev.13968 . Collaborative for Academic, Social and Emotional Learning. (2025). CASEL’s SEL frame‐ work. https://casel.org/fundamentals-of-sel/what-is-the-casel-framework/ . Dermody, C. M., & Dusenbury, L. (2022). 2022 social and emotional learning state score‐ card scan. https://casel.org/2022-state-scan/?view=1 . Deutsch, N. L. (2022). ‘You just have to get through it’: Letting go of enduring stereo‐ types about middle school. Phi Delta Kappan, 104(2), 6–10. https://doi.org/10.1177/ 00317217221130624 . Durlak, J. A., Domitrovich, C. E., & Mahoney, J. L. (2025). Handbook of social and emotional learning (2nd ed.). Guilford. . Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & MacIver, D. (1993). Development during adolescence: The impact of stage-envi‐ ronment fit on young adolescents’ experiences in schools and in families. American Psychologist, 48(2), 90–101. https://doi.org/10.1037/0003-066X.48.2.90 . Engels, M. C., Colpin, H., Van Leeuwen, K., Bijttebier, P., Van Den Noortgate, W., Claes, S., Goossens, L., & Verschueren, K. (2016). Behavioral engagement, peer status, and teacher-student relationships in adolescence: A longitudinal study on reciprocal in‐ fluences. Journal of Youth and Adolescence, 45, 1192–1207. https://doi.org/10.1007/ s10964-016-0414-5 . Fuligni, A. J. (2019). The need to contribute during adolescence. Perspectives on Psycho‐ logical Science, 14(3), 331–343. https://doi.org/10.1177/1745691618805437 . Hamilton, L. S., Doss, C. J., & Steiner, E. D. (2019). Teacher and principal perspectives on social and emotional learning in America’s schools. Findings from the American Educator Panels. https://doi.org/10.7249/RR2991 . Hartshorne, J. K., & Germine, L. T. (2015). When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psychological Science, 26(4), 433–443. https://doi.org/10.1177/0956797614567339 . Hill, C. J., Bloom, H. S., Black, A. R., & Lipsey, M. W. (2008). Empirical benchmarks for interpreting effect sizes in research. Child Development Perspectives, 2(3), 172–177. https://doi.org/10.1111/j.1750-8606.2008.00061.x . Hughes, J. N., & Cao, Q. (2018). Trajectories of teacher-student warmth and conflict at the transition to middle school: Effects on academic engagement and achievement. Journal of School Psychology, 67, 148–162. https://doi.org/10.1016/j.jsp.2017.10.003 . LeClaire, C. (2022, April). We are crew: School climate and culture. In S. E. RimmKaufman (Chair), Creating good human beings: New perspectives on cultivating eth‐ ical character in expeditionary learning middle schools. Symposium at the American Education Research Association, San Diego, CA. . Musci, R. J., Voegtline, K., Raghunathan, R., Ialongo, N., Johnson, S. (2022). Differential impact of a universal prevention program on academic self-efficacy: The moderating 32 Sara E. Rimm-Kaufman role of baseline self-control. Prevention Science, 23, 513–522. http://doi.org/10.1007/ s11121-021-01315-7 . National Academies of Sciences, Engineering, and Medicine. (2019). The promise of adolescence: Realizing opportunity for all youth. National Academies Press. https:// doi.org/10.17226/25388 . Office of New Americans Report. (2023). Plan to establish the Maine Office of New Americans. https://www.maine.gov/future/sites/maine.gov.future/files/inline-files/ ONA%20Report%20V.FinalLayout.pdf . Olweus, D. (1993). Bullying at school: What we know and what we can do. Blackwell. . Osher, D., Kidron, Y., Brackett, M., Dymnicki, A., Jones, S., & Weissberg, R. P. (2016). Advancing the science and practice of social and emotional learning: Looking back and moving forward. Review of Research in Education, 40(1), 644–681. https://doi. org/10.3102/0091732X16673595 . Rimm-Kaufman, S. E., Strambler, M. J., & Schonert-Reichl, K. A. (2023). In S.E. RimmKaufman, M. J. Strambler, & K. A. Schonert-Reichl (Eds.), Social and emotional learning in action: Creating systemic change in schools (pp. 1–32). Guilford. . Rimm-Kaufman, S. E., Soland, J., & Kuhfeld, M. (2024). Social and emotional compe‐ tency development from fourth to 12th grade: Relations to parental education and gender. American Psychologist. Advance online publication. https://doi.org/10.1037/ amp0001357 . Ross, K. M., Kim, H., Tolan, P. H., & Jennings, P. A. (2019). An exploration of normative social and emotional skill growth trajectories during adolescence. Journal of Applied Developmental Psychology, 62, 102–115. https://doi.org/10.1016/j.appdev.2019.02. 006 . Scheithauer, H., & Bull, H. D. (2008). fairplayer.manual: Förderung von sozialen Kom‐ petenzen und Zivilcourage – Prävention von Bullying und Schulgewalt. Vandenhoeck & Ruprecht. . Soto, C. J., & Tackett, J. L. (2015). Personality traits in childhood and adolescence: Structure, development, and outcomes. Current Directions in Psychological Science, 24(5), 358–362. http://doi.org/10.1177/0963721415589345 . Taylor, R. D., Oberle, E., Durlak, J. A., & Weissberg, R. P. (2017). Promoting positive youth development through school-based social and emotional learning interven‐ tions: A meta-analysis of follow-up effects. Child Development, 88(4), 1156–1171. https://doi.org/10.1111/cdev.12864 . Thomas, K., Ross, L., & Ruzek, E. (2025). Teacher discrimination and student en‐ gagement in the context of classroom quality: The mediating effect of relational trust. Journal of Educational Psychology. Advance online publication. https://doi.org/ 10.1037/edu0000920 . Tyton Partners. (2021). Finding your place 2021: Social emotional learning takes cen‐ ter stage in K-12. https://tytonpartners.com/app/uploads/2021/10/Tyton-Partners_ Finding-Your-Place-2021_SEL-Takes-Center-Stage-in-K12.pdf Socio-emotional learning 33 . Wanless, S., Spear, C. F., Artinger, J. V., & Briggs, J. O. (2023). Creating spaces for adult learning and professional development: SEL skills for facilitating racial equity discus‐ sions. In S. Rimm-Kaufman, M. J. Strambler, & K. A. Schonert-Reichl (Eds.), Social and emotional learning in action: Creating systemic change in schools (pp. 138–156). Guilford Press. . Ward-Seidel, A. R., Rimm-Kaufman, S. E., & Sandilos, L. E. (2024). Schoolwork with purpose: A mixed methods study on youths’ perspective on what makes learning meaningful. Journal of Adolescent Research. Advance online publication. https://doi. org/10.1177/07435584241280266 . West, M. R., Buckley, K., Krachman, S. B., & Bookman, N. (2018). Development and implementation of student social-emotional surveys in the CORE Districts. Journal of Applied Developmental Psychology, 55, 119–129. https://doi.org/10.1016/j.appdev. 2017.06.001 . Yu, M. V. B., Johnson, H. E., Deutsch, N. L., & Varga, S. M. (2018). “She calls me by my last name”: Exploring adolescent perceptions of positive teacher-student rela‐ tionships. Journal of Adolescent Research, 33(3), 332–362. https://doi.org/10.1177/ 0743558416684958 . Elsbeth Stern Learning and performance do not have to come at the expense of socio-emotional development 1. Introduction All subjects taught in school aim to foster meaningful learning and conceptual understanding. The knowledge acquired in the classroom is supposed to enable students to better understand the world around them, as well as to develop and achieve short-term and long-term goals. The reality, however often looks different: Students acquire knowledge that is bound to the narrow context of the classroom, which means they do not achieve a deeper understanding of the content dealt with. If at all, students can retrieve the knowledge in the next exam, but its isolated representation in long-term memory makes it unlikely to be activated in new contexts inside and outside of school (Mähler & Stern, 2006). This is detrimental for students, teachers, parents and society as a whole. It is particularly frustrating for students to experience day after day that they have to learn things that they do not understand and whose intellectual or practical benefits they cannot comprehend. These experiences will most likely create feelings of helplessness and belonging uncertainty (Cohen, 2022), which has a long-term impact on students’ socio-emotional development and impairs their further academic development (Baumert et al., 2023). Providing an education system that meets the needs of society and at the same time benefits the broad variety of learners, is one of the greatest challenges in the modern world. Scientific progress made in various areas related to learn‐ ing and education can help to better understand the challenges of providing learning environments that support children in developing positive attitudes to performance and equipping them with self-efficacy. Three lines of research, which will be discussed in detail in this article, can contribute to this goal: a) Preparation for future learning instead of short-term effects: Promoting the acquisition of usable and transferable knowledge. Schools were founded be‐ cause skills such as reading, writing and arithmetic have evolved over the course of cultural development and need professional instruction to be passed to the next generation. Therefore, learning at school is by definition more demanding than the acquisition of skills for which evolution has prepared us. At the same 36 Elsbeth Stern time, we have not yet exhausted the opportunities for facilitating learning that the learning sciences offer us. By providing learning environments that meet the affordances and constraints of human information processing (Schumacher & Stern, 2022), learners are more likely to have successful experience and thereby gain self-confidence and self-efficacy (Yu & Schunn, 2024). b) Creating motivation by promoting mastery instead of performance ori‐ entation: In contrast to everyday concepts held also by many teachers, for Psy‐ chologists motivation is rather a state than a trait. This means that learners’ motivation can be influenced by teachers, and this does not have to be done by lowering standards. The self-determination theory (Deci & Ryan, 2012) pro‐ vides the framework for this. c) Managing diversity in cognitive abilities, prior knowledge, interest and social background. When entering a class, students do not start at the same level, and they often end up even more diverse. In particular, cognitive abilities as measured with intelligence tests are a stable personality trait with a strong impact on learning outcomes (Stern, 2017; 2024). Managing diversity to the benefit of all is a major challenge for teachers, which needs more attention. 2. Preparation for future learning instead of short-term effects: Promoting the acquisition of usable and transferable knowledge Effective learning environments have to consider affordances and constraints of the human minds. Thanks to scientific progress in cognitive science, we now have a solid understanding of how the human mind works and how learning can be promoted. To avoid frustration among students and teachers, learning material and instructional interventions must consider how incoming infor‐ mation is encoded, stored and retrieved. In the interests of efficient learning environments, the multi-store model of human information processing agreed on by psychologists should be taken into account in all educational activities. Sensory memory is the earliest stage of processing the large amount of contin‐ uously incoming information from sight, hearing and other senses. In order to allow goal directed behavior and selective attention, only a fractional amount of incoming information passes into the working memory, which is responsible for temporarily maintaining and manipulating information during cognitive activity. The special architecture of working memory is one of the unique fea‐ tures of humans. What the cell is for Biology and the molecule is for Chem‐ istry is working memory for Psychology, as this construct can broadly explain what controls human behavior (Stern, 2024). The limited capacity of working memory allows control of attention and thereby enabling goal directed and Learning and performance 37 conscious information processing. Working memory is the gatekeeper to the long-term memory, which is assumed to have an unlimited capacity. Here, in‐ formation acquired through experience and reasoning can be stored in different modalities as well as in symbol systems (e.g., language, script, mathematical no‐ tation systems, pictorials, music prints). Working memory enables intentional information processing by focused attention, but it can also process incidental information. The multi-store model of human information processing is not at all a one-way street, and long-term memory is not to be seen as a storeroom or a hard-disk where information remains unaltered once it has been deposited. A more appropriate model of long-term memory is a self-organizing network, in which verbal concepts, images, or procedures are represented as interlinked nodes with varying associative strength (Stern, 2017). Working memory regulates the interaction between incoming information from sensory memory and knowledge activated from long-term memory. In case of very strong incoming stimuli (e.g., a loud noise or a harsh light), working memory activities will be interrupted. For the most part, however, working memory is guarding against incoming information to make sure that goals that have been set will be achieved appropriately. This means, working memory is continuously busy with selecting incoming information, aligning it to knowledge retrieved from long-term memory, and preparing responses for accomplishing requirements demanded by the environment. Inappropriate and unsuitable information intruding from sensory as well as from long-term memory has to be inhibited, while appropriate and suitable information from both sources has to be updated. These working memory activities permanently change knowledge represented in long-term memory by adding new nodes and by altering the associative strength between them. Working memory activities are intertwined with the context as well as with the knowledge stored in the long-term memory. One of the most important insights from the learning sci‐ ences is that prior knowledge is the most important predictor of future learning. Successful teaching means to provide learning opportunities that meet with prior knowledge. Psychologists have agreed on distinguishing between declarative (knowing that) and procedural (knowing how) knowledge. Declarative knowledge can be communicated because it is represented on the basis of symbol systems (lan‐ guage, script, mathematical or visual-spatial representations). It is the basis for constructing meaningful conceptual knowledge through processes of inference and elaboration. In contrast to declarative knowledge, procedural knowledge can be directly applied to perform a task, and it includes motoric actions like driving cars as well as mental actions like algebraic transformations. Proce‐ dural knowledge emerges as a consequence of repetition, by which single ac‐ 38 Elsbeth Stern tions become increasingly integrated into a coordinated series of actions. If the association between these actions is strong enough, they can activate each other and therefore only need a minimum of working memory functions. Be‐ cause automated knowledge is at most partly open to conscious inspection, it can hardly be verbalized, and is resistant to modification. Mastery of any aca‐ demic content area requires procedural and conceptual knowledge. Both kinds of knowledge are strongly intertwined (Schneider & Stern, 2010), but they are acquired through different mechanisms. Procedural knowledge is acquired by condensing single pieces of knowledge into broader units. The repeated exe‐ cution of actions composed of single steps results in automated procedures. The repeated presentation of sets of small single stimuli may result in putting them together to a meaningful whole, which is called a chunk. Once chunks and procedures are created, they can be processed with low memory requirements. Transforming single letters into words is an example of chunking. Conceptual knowledge, on the other hand, represents meaning, what can show up in classifications, principles, generalizations, theories or models in a content area. It can be communicated via symbol systems and it is constructed through reasoning that has been stimulated in interactions with other individ‐ uals, written material or other media. Engaging with content in this way is a challenge to working memory – in particular for areas that are mainly based on abstract concepts, as it is the case for many STEM areas. How to promote usable knowledge The goal of academic learning is the acquisition of usable knowledge that is flex‐ ible enough for transfer and problem solving. Usable knowledge is composed of procedures and chunks to relieve working memory functions which can then be used for grasping the gist of a problem. Content areas and academic disciplines differ in the composition of both kinds of knowledge, and this may affect the importance of intelligence for achieving expertise. Typical areas of expertise which are primarily characterized by the formation of chunks are reading and chess. Learning from texts is effective if working memory resources are avail‐ able for processing the content instead of decoding letters. Experienced readers have chunked letters into words and patterns of words and therefore can con‐ centrate on the content. Expertise in chess is gained by chunking single chess positions into broader units which can be recognized and stored with minimum working memory resources. Also, in broader disciplines, chunking is essential and reflects expertise. The audience of my lecture on “Human Learning” are master’s students from different STEM areas. To demonstrate the impact of prior knowledge on memory, I show my students pictures presented in Figure 1 Learning and performance 39 Figure 1: The pictures presented in the left column present important concepts from Chemistry (choles‐ terol molecule), Biology (animal cell) and Mathematics (first and second derivative of a polynomial function). The pictures were presented for a few seconds to STEM students with different disciplinary backgrounds, who were asked to draw them afterwards. Examples of the drawings from the students are presented in the cells to demonstrate the importance of prior knowledge for memorizing complex information. for a few seconds and then ask my students to draw them. The pictures repre‐ sent concepts that make sense to experts in the field, while being more or less random to others. This becomes obvious in their drawings. Depending on their disciplinary background, they either reproduce the image exactly or provide a rough sketch that lacks key conceptual features. Automated procedures and chunks are necessary but not sufficient for mas‐ tering broader academic areas. STEM disciplines as well as humanities are cul‐ tural achievements and mastering them means understanding statements ex‐ pressed in symbolic systems such as language, writing, formal notations, pic‐ torial and graphical representations. Competence is evident in these areas by building a network of interconnected but distinct concepts, which provide the basis for deductive, inductive, and analogical reasoning. Such cognitive pro‐ cesses enable transfer of knowledge to new situations and problems, which is a challenge for all learners, no matter how intelligent they are. The difficulties have been best explored in mathematics and the natural sciences, especially in physics. Here students not only have to learn new concepts, as it is the case for all academic fields, but they must also restructure the meaning of many expressions used in daily life, among them concepts like force or energy. Math‐ 40 Elsbeth Stern ematics is based on theorems and relations between numbers and figures that are derived by deductive reasoning. Learning environments are effective if they meet with students’ prior knowl‐ edge and address learners’ deficits either in terms of conceptual misunderstand‐ ings or in terms of a lack of chunking and automation. In the past decades, learning sciences have developed and systematically evaluated so called means of cognitive activation that can be implemented in instructional units. These means have in common that they intend to face learners with deficits and mis‐ understandings regarding their prior knowledge (Schumacher & Stern, 2022). Presenting learning material that requires systematic comparison between su‐ perficially similar but conceptually different situations can help to extract ab‐ stract concepts. Prompting self-explanations or asking metacognitive questions raises learners’ awareness of their prior knowledge, including deficits and mis‐ conceptions. Thanks to progress in digitization, teachers now have methods like formative assessment at their disposal which allow them to diagnose their students’ deficits and provide them with appropriate learning opportunities. A second grader may answer “7” when asked: “What is a quarter of 32”. A teacher may conclude that the student does not master multiplication tables properly and therefore asks the student to just practice. However, by asking the student to explain why he ended up with seven may shed light on severe conceptual deficits: “A quarter is always 25, and 32-25 results in 7”. In this case, the concept of “quarter” has to be addressed. “Knowing what students know” is the key to better adapt learning opportunities to students needs and saves a lot of frustra‐ tion for students and teachers. This kind of cognitive empathy on the part of the teachers can be expected to promote students’ social-emotional development. 3. Motivation: Promoting mastery instead of performance orientation Many teachers complain about their students’ lack of motivation, as they have the feeling that no matter what efforts they make, students are not willing to get involved. At best, they develop extrinsic motivation, i.e. they work towards passing the exam or getting the best grade with minimal effort. Such experi‐ ence suggests that motivation is a fixed individual characteristic rather than a malleable state. Such attitudes are supported by messages delivered by some psychologists, e.g. by Duckworth et al. (2019), who emphasizes that a student’s grit – their passion and perseverance for long-term goals – is decisive for her learning outcomes. Precondition for developing grit is to develop passion and interests and to cultivate deliberate practice. The emphasis on passion and in‐ Learning and performance 47 Changing school systems is a long and tedious process, and in Germany sev‐ eral attempts to overcome the disadvantages of tracking were doomed to failure. Future changes need to be better planned. In the meantime, to mitigate the impact of social background on admission to university education, educational sciences should carefully evaluate under what conditions intelligence tests can help to make decisions as fair as possible. Ad 2: The central role of working memory functions in academic learning from a universal, a developmental, and a differential perspective should de‐ termine further research. Thanks to the cognitive load theory, the universal perspective is ready to be implemented in learning material and environments. More research, however, is needed concerning constraints in brain develop‐ ment affecting working memory functions. While early childhood and adoles‐ cence got sufficient attention, more has to be learned about the affordances and constraints of working memory among elementary school children in order to optimally exploit their learnability in the first years of school, when the foun‐ dations for literacy as well as for mathematical and scientific reasoning are laid. As discussed above, working memory and reasoning abilities measured in in‐ telligence tests are closely related, but they are not identical. Reasoning abilities need cognitive challenges to develop, facilitated by a well-functioning working memory. While measures of reasoning and working memory show high cor‐ relations, there are deviations in both directions. Some individuals with high working memory functions do not fully translate these into reasoning. Oth‐ ers are able to develop reasoning abilities that exceed their working memory functions. Future research should focus on reasons for these patterns as well as consequences for future learning. Regarding the implications for practice, the fruitful collaboration between learning scientists and subject-specific experts should be continued. Teacher education programs as well as training programs for in-service-teachers should focus on the implementation of proven means for useful knowledge construc‐ tion in core content areas of their subject. Awareness for the mission of forma‐ tive assessment and implementing it into daily classroom practice should be a top priority. Understanding what exactly causes students’ difficulties can then help to offer the appropriate learning opportunities. References . Allensworth, E. M., Clark, K. (2020). High school GPAs and ACT scores as predictors of college completion: Examining assumptions about consistency across high schools. Educational Researcher, 49, 198–211. https://doi.org/10.3102/0013189X20902110 . Baumert, B., Rau, F., Bauermeister, T., Döhrmann, M., Ewig, M., Friederich, Y., Haas, 48 Elsbeth Stern T., Küthe, E., Loth, G., Rusert, K., Schaller, M., Schröder, L., Schweer, M. K. W., Stein, M., Vierbuchen, M.-C. (2023). Lost in Transformation? Chancen und Heraus‐ forderungen für inklusiven Unterricht im Angesicht der digitalen Transformation. In D. Ferencik-Lehmkuhl, I. Huynh, C. Laubmeister, C. Lee, C. Melzer, I. Schwank, H. Weck, & K. Ziemen (Eds.), Inklusion digital! Chancen und Herausforderungen inklu‐ siver Bildung im Kontext von Digitalisierung (pp. 33–48). Verlag Julius Klinkhardt. https://doi.org/10.35468/5990-03 . Berkowitz, M., Edelsbrunner, P., Stern, E. (2022). The relation between working mem‐ ory and mathematics performance among students in math-intensive STEM pro‐ grams. Intelligence, 92, 101649. https://doi.org/10.1016/j.intell.2022.101649 . Blossfeld, H. P., Buchholz, S., Skopek, J., & Triventi, M. (Eds.). (2016). Models of sec‐ ondary education and social inequality: An international comparison. Edward Elgar Publishing. https://doi.org/10.4337/9781785367267 . Cohen, G. (2022). Belonging: The science of creating connection and bridging divides. WW Norton & Company. . Deary, I. J. (2004). The impact of childhood intelligence on later life: Following up the Scottish mental surveys of 1932 and 1947. Journal of Personality and Social Psychol‐ ogy, 86, 130–147. https://doi.org/10.1037/0022-3514.86.1.130 . Deci, E. L., & Ryan, R. M. (2012). Motivation, personality, and development within embedded social contexts: An overview of self-determination theory. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (pp. 85–107). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195399820.013.0006 . Duckworth, A. L., Quirk, A., Gallop, R., Hoyle, R. H., Kelly, D. R., & Matthews, M. D. (2019). Cognitive and noncognitive predictors of success. Proceedings of the Na‐ tional Academy of Sciences, 116(47), 23499–23504. https://doi.org/10.1073/pnas. 1910510116 . Goldthorpe, J. (2016). Social class mobility in modern Britain: changing structure, con‐ stant progress. Journal of the British Academy, 4, 89–111. https://doi.org/10.5871/ jba/004.089 . Lazarides, R., Schiefele, U., Hettinger, K., & Frommelt, M. C. (2023). Tracing the signal from teachers to students: How teachers’ motivational beliefs longitudinally relate to student interest through student-reported teaching practices. Journal of Educational Psychology, 115, 290–308. https://doi.org/10.1037/edu0000777 . Lichtenberger, A., Hofer, S., Stern, E., Vaterlaus, A. (2024). Enhanced conceptual under‐ standing through formative assessment: results of a randomized controlled interven‐ tion study in physics classes. Educational Assessment, Evaluation and Accountability 37, 5–33. https://doi.org/10.1007/s11092-024-09445-6 . Mähler, C., & Stern, E. (2006). Transfer. In D. Rost (Ed.), Handwörterbuch Pädagogische Psychologie (pp. 782–793). Beltz. . Peteranderl, S., Edelsbrunner, P. A., Deiglmayr, A., Schumacher, R., & Stern, E. (2023). What skills related to the control-of-variables strategy need to be taught, and who Learning and performance 49 gains most? Differential effects of a training intervention. Journal of Educational Psy‐ chology, 115, 813–835. https://doi.org/10.1037/edu0000799 . Ridley, M. (2003). Nature via nurture: Genes, experience, and what makes us human. Harper Collins Publishers. . Roth, B., Becker, N., Romeyke, S., Schäfer, S., Domnick, F., & Spinath, F. M. (2015). Intelligence and school grades: A meta-analysis. Intelligence, 53, 118–137. https:// doi.org/10.1016/j.intell.2015.09.002 . Schneider, M., & Stern, E. (2010). The developmental relations between conceptual and procedural knowledge: A multimethod approach. Developmental Psychology, 46(1), 178–192. https://doi.org/10.1037/a0016701 . Schneider, W., Niklas, F., Schmiedeler, S. (2014). Intellectual development from early childhood to early adulthood: The impact of early IQ differences on stability and change over time. Learning and Individual Differences, 32, 156–162. https://doi.org/ 10.1016/j.lindif.2014.02.001 . Schumacher, R., Stern, E. (2022). Promoting the construction of intelligent knowledge with the help of various methods of cognitively activating instruction. Frontiers in education, 7, 979430. https://doi.org/10.3389/feduc.2022.979430 . Shipstead, Z., Harrison, T. L., Engle, R. W. (2016). Working memory capacity and fluid intelligence: Maintenance and disengagement. Perspectives on Psychological Science 11, 771–799. https://doi.org/10.1177/1745691616650647 . Steinberg, L. (2017). Adolescent brain science and juvenile justice policymaking. Psy‐ chology, Public Policy, and Law, 23, 410–420. https://doi.org/10.1037/law0000128 . Stern, E. (2017). Individual differences in the learning potential of human beings. npj Science Learn 2, 2. https://doi.org/10.1038/s41539-016-0003-0 . Stern, E. (2024). Research on intelligence and learning: How to unite estranged siblings. Personality and Individual Differences, 221, 112555. https://doi.org/10.1016/j.paid. 2024.112555 . Sweller J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22, 123–138. https://doi.org/10.1007/s10648010-9128-5 . Tucker-Drob, E. M., & Bates, T. C. (2016). Large cross-national differences in gene x socioeconomic status interaction on intelligence. Psychological Science, 27, 138–149. https://doi.org/10.1177/0956797615612727 . Turkheimer E., Haley A., Waldron M., D’Onofrio B., Gottesman I. (2003). Socioeco‐ nomic status modifies heritability of IQ in young children. Psychological Science, 14, 623–628. https://doi.org/10.1046/j.0956-7976.2003.psci_1475.x . Woltereck, R. (1909). Weitere experimentelle Untersuchungen über Artveränderung, speziell über das Wesen quantitativer Artunterschiede bei Daphniden. Verhandlun‐ gen der deutschen zoologischen Gesellschaft, 19, 110–173. . Yu, Q., & Schunn, C. D. (2024). Which experiences are consistently associated with other-regulation of peer feedback length? Journal of Educational Psychology, Advance online publication. https://doi.org/10.1037/edu0000913 . Florian Schmiedek Learning for at-risk students A dynamic perspective In recent years, there has been growing concern about the socio-emotional wellbeing and mental health of children and adolescents. These concerns add to the ongoing concerns about the academic achievement of these age groups, par‐ ticularly with regard to basic competencies, and especially for children from disadvantaged backgrounds. For an education system that not only aims to promote the development of such competencies, but also takes responsibility as an important environment for the identification and prevention of mental health problems and the general improvement of socio-emotional well-being, the question arises as to how these two general domains of outcomes are re‐ lated. Are they positively intertwined, such that promoting one domain will automatically benefit the other – and failing to do so will increase risk in the other? Or are these outcome domains in conflict, requiring decisions about how to allocate limited educational resources? In what follows, I will argue that attempts to answer these questions at a general level are limited. Rather, the dynamic processes that link learning and well-being and that may place learners at risk for academic failure and mental health problems should be understood as potentially highly individualized and therefore deserving of investigation from a within-person perspective that takes this individuality into account. 1 Students at risk for academic failure and mental health problems Since the sobering results of the first PISA studies, there has been increased monitoring of the educational system, resulting in worrying figures on the prevalence of students at risk of academic failure. For example, there are the large and increasing percentages of students failing to meet minimum stan‐ dards in basic competencies as documented by the IQB Bildungstrend (Stanat et al., 2023) and similarly concerning trends in the number of students drop‐ ping out of school, as reported in the most recent National Report on Educa‐ tion (Autor:innengruppe Bildungsberichterstattung, 2024). Asking about the risk factors that in turn predict such indicators of academic failure, there is strong empirical evidence for general (background or distal) factors like, for 52 Florian Schmiedek example, poverty (Selvitopu & Kaya, 2023) and migration background (Heath & Brinbaum, 2014). These contribute to more proximal risk factors at the level of the individual student, like lack of language competences (Snowling et al., 2021), socio-emotional competences (McLeod & Kaiser, 2004), or self-regula‐ tion competences (Robson et al., 2020). If the expression of such individual fac‐ tors is pronounced, it can lead to fulfilling the diagnostic criteria for high-risk categories such as learning disorders or attention deficit hyperactivity disorder (Caviola et al., 2024). With regard to socio-emotional well-being, there is growing evidence of a disturbingly high and increasing prevalence of mental health problems among children and adolescents. Reports by both international (McGorry et al., 2024) and national expert commissions (Deutsche Akademie der Naturforscher Leopoldina, 2024) have recently reviewed the empirical literature and noticed the magnitude and urgency of the problem. Among the most important risk factors for mental health problems, again background factors such as poverty (Klasen et al., 2017) are prominent. As more proximal factors, familial factors (e.g., mental health problems of parents; Beardslee et al., 2011), experience of violence and abuse (Baldwin et al., 2023), and mobbing (Duffy & Sperry, 2012), for example show relevant roles in predicting mental health problems, which can manifest in diagnoses of mental disorders in the extreme case. In children and adolescents, the disorder categories of depression, anxiety, and psychosomatic problems are particularly prevalent (Deutsche Akademie der Naturforscher Leopoldina, 2024). In summary, the current numbers and longitudinal trends in both domains, academic achievement vs. failure and socio-emotional well-being vs. mental health problems, are alarming and call for increased political and societal efforts to address and reduce these problems. The fact that important risk factors for both problem domains are partly the same may lead to the expectation that there should be a positive relationship between both problem domains. So, what is the scientific evidence for such an association? 2. The relation of academic achievement and well-being: Theoretical arguments and empirical findings Regarding theoretical considerations, one can find arguments for, both, a pos‐ itive (“win-win”) relation, as well as a negative (“trade-off”) relation between academic achievement and well-being. Arguments for a positive relation of‐ ten posit positive reciprocal effects between both domains. For example, the Broaden-and-Build Theory (Fredrickson, 2001) proposes that well-being can Learning for at-risk students 53 foster learning processes through a broadening of thought-action repertoires, which can allow to build personal resources that, in turn, enhance well-being. Self-determination theory (Ryan & Deci, 2000) also assumes reciprocal rela‐ tions, with academic success satisfying the need for competence. Being one of the basic psychological needs according to this theory, its satisfaction con‐ tributes to well-being. Furthermore, it serves a basis for the development of in‐ trinsic motivation, which in turn is again beneficial for academic performance. Reciprocal effects are also central to Developmental Cascades Theory (Moilanen et al., 2010), which posits that externalizing and internalizing symptoms and academic failure can mutually enhance each other. However, arguments can also be found for a negative relationship between the two domains (see Högberg, 2023, for a summary). The simple fact that time is limited potentially leads to opportunity costs: students who invest a lot of time in learning have less time left for other (e.g., leisure and social) activi‐ ties that are beneficial for their socio-emotional well-being. In addition, highachieving contexts (such as elite schools) are often characterized by a competi‐ tive climate and a disproportionate emphasis on performance, which can have negative emotional consequences. Given that these different theoretical mecha‐ nisms may all contribute to the overall relationship between academic achieve‐ ment and well-being and thus (partially) counteract each other, it is perhaps not surprising that empirical evidence is mixed and quantitative meta-analytic summaries suggest rather weak relationships. A meta-analysis by Bücker et al. (2018) reports an average correlation of .16 and another one by Kaya and Erdem (2021) results in an average relationship (Fisher’s z) of .17. Looking at these numbers alone, one might be tempted to conclude that academic achievement and socio-emotional well-being are relatively independent from each other and therefore could also be addressed by independent measures. In the following, however, I would like to highlight that such an average relation of betweenperson differences may not be informative about the mechanisms that link aca‐ demic achievement and well-being at the level of individual students, that these mechanisms may in fact be quite heterogeneous across students, and that there is therefore much to be gained by also considering relationships at the level of within-person variability by taking a dynamic perspective on the interplay between academic achievement and well-being. 54 Florian Schmiedek 3. The relationship between academic achievement and well-being: Within-person relationships A central insight that has received increasing attention in psychology in recent decades is that relations observed at the level of between-person differences may not be informative about relations at the level of the individuals underlying these between-person differences. Only under strict conditions (i.e., ergodicity; Molenaar, 2004), inferences from between-person relations to within-person relations are possible. Individuals may differ from each other in their withinperson relations and the average within-person relation may differ from the between-person relation. Figure 1 provides an illustrative example. It shows results from the FLUX study (Dirk & Schmiedek, 2016), in which 110 third and fourth graders participated in smartphone-based ambulatory assessments several times a day for up to 31 days. On these occasions, they completed dif‐ ferent working memory tasks and several self-report measures, including rat‐ ings of task-related enjoyment. The main scatterplot shows the positive rela‐ tionship between working memory performance and enjoyment averaged over all measurement occasions for each child and indicates a positive relation‐ ship (r= .33) – children who perform better on average tend to enjoy work‐ ing on the tasks more. However, when looking at the level of within-person variation across the different measurement occasions for individual students, the picture can be quite different. In the example, two students (Child 1 and Child 2) with very similar average levels of performance and enjoyment show radically different within-person relationships – for Child 1 this relationship is quite strong (r= .56), for Child 2 there is no systematic relationship at all (r= -.01). Neither one is well characterized by the between-person correlation of .33. Murayama et al. (2017) provide further illustrative examples of hetero‐ geneous within-person relationships in educational psychology research con‐ texts. Such heterogeneity of within-person relationships suggests that the causal mechanisms producing the observed relationships may also differ across indi‐ viduals. And even when similar within-person relationships are observed, the underlying causal mechanisms may still be different. Considering the relation‐ ship between academic achievement and socio-emotional well-being, there may be achievement-oriented students for whom achievement is a primary source of self-esteem and well-being. For others, their success in learning may be highly dependent on the absence of negative emotions – which would also produce a positive within-person relationship between achievement and well-being, yet based on a different causal mechanism. It may also be the case that an observed positive relationship is produced by third variables that mutually influence both Learning for at-risk students 55 Figure 1. Comparison of the between-person relationship of working memory performance and taskrelated enjoyment in N= 110 children participating in up to 31 days of ambulatory assessment in the FLUX study (Dirk & Schmiedek, 2016) with the within-person relationships of two selected children across study occasions. Working memory (WM) performance is a composite of accuracy on numerical and figural-spatial WM updating tasks with different memory load conditions (see Dirk & Schmiedek, 2016). Task-related joy is a composite of self-rating on the items “I just had a lot of fun working on the (tasks and) questions overall.”, “I just enjoyed working on the (tasks and) questions.”, and “I just found working on the (tasks and) questions boring.” (reversed item) with 5-point Likert scales. achievement and well-being, such as day-to-day variations in sleep quality or health status. It is even possible that the relationship may be negative for in‐ dividual students. Consider a child who performs best at times when parental pressure to perform well is high, which for this child is associated with lower levels of well-being due to stress and test anxiety. Or consider an adolescent who goes through periods of spending a lot of time with peers, which has a positive impact on socio-emotional well-being by meeting the need for social inclusion, but a negative impact on time spent learning. While these examples are speculative, they should serve to illustrate that a weak between-person cor‐ relation between achievement and well-being may mask a large heterogeneity of individual relations and underlying causal mechanisms. If known, these dif‐ ferent mechanisms would also suggest different individual starting points for interventions aimed at improving achievement and well-being. However, it is only by examining within-person variation with measurement-intensive longi‐ tudinal studies that we may be able to identify such between-person variability in relationships and mechanisms. 56 Florian Schmiedek With the growth of measurement-intensive studies, empirical evidence of such between-person variability is beginning to accumulate in several research areas of psychology, including educational psychology. For example, Niepel et al. (2022) investigated the cross-lagged relationships between mathematical self-concept and perceived mathematics achievement in 372 secondary school students, using self-report measures after each mathematics lesson over a threeweek period. They found significant average cross-lagged effects in both direc‐ tions (i.e., better performance in one lesson predicted better self-concept in the next lesson and vice versa). There were also significant random effects, indicat‐ ing between-person differences in the strength of these within-person effects. Interestingly, the correlation of these random effects was negative (r= -.56), indicating that the reciprocal effects tended to be stronger in one direction or the other for different groups of students. Such an indication of potential heterogeneity in within-person relations of constructs related to performance and well-being was also reported by Neubauer et al. (2019). Using data from the FLUX study (see above) and mixture modeling, they were able to identify latent classes with different patterns of relations between working memory performance and four di‐ mensions of momentary affective well-being (positive and negative affect, activation and deactivation). While two of the latent classes showed rel‐ atively strong (or weak) within-person relations of performance with all affect dimensions, the other two latent classes were characterized by per‐ formance being specifically associated with negative affect and deactiva‐ tion or exclusively with activation. Again, such heterogeneity may sug‐ gest different promising approaches when considering individualized in‐ terventions (e.g., reducing negative affect or increasing levels of activa‐ tion). 4. From bivariate within-person relations to complex dynamic networks In clinical psychology, research on mental disorders has in recent years increas‐ ingly adopted measurement-intensive studies and focused on within-person processes, typically assessed in patients’ everyday life contexts using ambula‐ tory assessment (Trull & Ebner-Priemer, 2013). This growth has fostered a parallel new conceptual development based on dynamic systems theory and network modeling approaches (Borsboom, 2017). The general motivation be‐ hind this is to move away from the traditional view in psychiatry and clinical psychology, which conceptualizes mental disorders as separate categories that Learning for at-risk students 63 educational attainment. American Sociological Review, 69(5), 636–658. https://doi. org/10.1177/000312240406900502 . Moeller, J., Viljaranta, J., Tolvanen, A., Kracke, B., & Dietrich, J. (2022). Introducing the DYNAMICS framework of moment-to-moment development in achievement moti‐ vation. Learning and Instruction, 81, 101653. https://doi.org/10.1016/j.learninstruc. 2022.101653 . Moilanen, K. L., Shaw, D. S., & Maxwell, K. L. (2010). Developmental cascades: Ex‐ ternalizing, internalizing, and academic competence from middle childhood to early adolescence. Development and Psychopathology, 22(3), 635–653. https://doi.org/10. 1017/S0954579410000337 . Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bring‐ ing the person back into scientific psychology, this time forever. Measurement: In‐ terdisciplinary Research and Perspectives, 2(4), 201–218. https://doi.org/10.1207/ s15366359mea0204_1 . Murayama, K., Goetz, T., Malmberg, L.-E., Pekrun, R., Tanaka, A., & Martin, A. J. (2017). Within-person analysis in educational psychology: Importance and illustra‐ tions. In D. W. Putwain & K. Smart (Eds.), BJEP Monograph Series II: Part 12 The Role of Competence and Beliefs in Teaching and Learning. British Psychological Society. https://doi.org/10.53841/bpsmono.2017.cat2023.6 . Neubauer, A. B., Dirk, J., & Schmiedek, F. (2019). Momentary working memory per‐ formance is coupled with different dimensions of affect for different children: A mixture model analysis of ambulatory assessment data. Developmental Psychology, 55(4), 754–766. https://doi.org/10.1037/dev0000668 . Niepel, C., Marsh, H. W., Guo, J., Pekrun, R., & Möller, J. (2022). Revealing dynamic relations between mathematics self-concept and perceived achievement from lesson to lesson: An experience-sampling study. Journal of Educational Psychology, 114(6), 1380–1393. https://doi.org/10.1037/edu0000716 . Robson, D. A., Allen, M. S., & Howard, S. J. (2020). Self-regulation in childhood as a predictor of future outcomes: A meta-analytic review. Psychological Bulletin, 146(4), 324–354. https://doi.org/10.1037/bul0000227 . Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of in‐ trinsic motivation, social development, and well-being. The American Psychologist, 55(1), 68–78. https://doi.org/10.1037//0003-066x.55.1.68 . Selvitopu, A., & Kaya, M. (2023). A meta-analytic review of the effect of socioeconomic status on academic performance. Journal of Education, 203(4), 768–780. https://doi. org/10.1177/00220574211031978 . Snowling, M. J., Moll, K., & Hulme, C. (2021). Language difficulties are a shared risk factor for both reading disorder and mathematics disorder. Journal of Experimental Child Psychology, 202, 105009. https://doi.org/10.1016/j.jecp.2020.105009 . Stanat, P., Schipolowski, S., Schneider, R., Weirich, S., Henschel, S., & Sachse, K. A. (Eds.). (2023). IQB-Bildungstrend 2022. Sprachliche Kompetenzen am Ende der 64 Florian Schmiedek 9. Jahrgangsstufe im dritten Ländervergleich. Waxmann. https://doi.org/10.31244/ 9783830997771 . Tamura, A., Ishii, R., Yagi, A., Fukuzumi, N., Hatano, A., Sakaki, M., Tanaka, A., & Murayama, K. (2022). Exploring the within-person contemporaneous network of motivational engagement. Learning and Instruction, 81, 101649. https://doi.org/10. 1016/j.learninstruc.2022.101649 . Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9(1), 151–176. https://doi.org/10.1146/annurev-clinpsy-050212185510 . van de Leemput, I. A., Wichers, M., Cramer, A. O. J., Borsboom, D., Tuerlinckx, F., Kuppens, P., van Nes, E. H., Viechtbauer, W., Giltay, E. J., Aggen, S. H., Derom, C., Jacobs, N., Kendler, K. S., van Der Maas, H. L. J., Neale, M. C., Peeters, F., Thiery, E., Zachar, P., & Scheffer, M. (2014). Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences, 111(1), 87–92. https://doi.org/10.1073/pnas.1312114110 . Wichers, M., Wigman, J. T. W., & Myin-Germeys, I. (2015). Micro-level affect dynamics in psychopathology viewed from complex dynamical system theory. Emotion Review, 7(4), 362–367. https://doi.org/10.1177/1754073915590623 Part 2: Emotions, motivation and well-being – the role of socio-emotional aspects in learning . Anne C. Frenzel & Thomas Goetz Learning and emotions In this contribution, we set out to take our readers on a journey to better un‐ derstand the nature of emotions, as well as their antecedents and learning out‐ comes. The presented emotion theory and existing empirical evidence points to close links between emotions and learning; hence it seems that classrooms designed to optimize students’ emotions should also be conducive to effective cognitive development. Nevertheless, we conclude by realizing that it is not trivial to align socio-emotional and cognitive development goals in classrooms. Instead, the typical, performance-oriented classroom settings seem to make those goals operate in competition. By emphasizing mastery-orientation and detaching the roles of “instructor” versus “examiner”, a better alignment of the two goals does seem achievable. But now, into the jungle of human emotional experiences . . . 1. Emotions in educational contexts: Conceptualization Emotions function as an “interface between an organism and its environment” (Scherer & Moors, 2019, p. 721), comprising various components such as sit‐ uational evaluations, action tendencies, physiological responses, expressive be‐ haviors, and subjective feelings. For example, the subjective feeling of pride can arise when an individual has achieved an academic success due to their per‐ sonal ability or effort. This goes in line with a motivation to engage in similar activities, and is accompanied by physiological changes like increased heart rate and body temperature, as well as expressive behaviors such as smiling, tilting the head back, expanding the chest, and potentially raising arms. In contrast, the subjective feeling of anxiety emerges in face of a potential academic failure, and goes in line with a desire to escape the situation, as well as physiological reactions like a rapid heart rate, sweating, wide eyes, and crouched posture, and worries about negative consequences of the potential academic failure. Emotions are typically of short duration and directed toward specific objects, setting them apart from moods, which tend to be longer-lasting, less focused on specific objects, and associated with weaker physiological responses and expressions (Coppin & Sander, 2021). The extensive vocabulary around emo‐ tions illustrates their diversity and the varying functions emotions can take on. 68 Anne C. Frenzel & Thomas Goetz For example, we can describe how we feel with adjectives like cheerful, elated, confident, joyful, proud, irritated, annoyed, restless, angry, anxious, bored, sad, trepidatious, sorrowful, weary, or dull. Furthermore, emotions can be conceptualized along a continuum from state to trait (Lazarus, 1994). Emotional states represent momentary responses aris‐ ing from interactions with the environment, whereas emotional traits reflect the tendency of individuals to experience certain emotions more frequently or intensely over time and situations than others. By definition, emotional traits are relatively stable, as highlighted in research distinguishing state and trait components of achievement emotions (Nett et al., 2017). 1.1 Types of emotions in educational settings Clearly the best-researched type of emotions experienced in educational con‐ texts are achievement emotions, which emerge during evaluations of one’s per‐ formance in relation to achievement standards (Pekrun, 2006). One can either achieve those standards – hence, succeed; or not – hence, fail. It is a basic human emotional response to feel positive about success and negative about failure. Examples of discrete achievement emotions include test anxiety, shame, relief, boredom, anger, pride and enjoyment. Educational settings involve extremely frequent and very salient encounters of achievement standards – students are constantly told what they are supposed to accomplish, and provided with feed‐ back as to whether they achieved the proposed standards. Accordingly, they constantly experience successes and failures, which inevitably bring about cor‐ responding positive and negative achievement emotions. Further, there are also epistemic emotions, such as surprise, curiosity, and confusion. Epistemic emotions do not directly pertain to success and failure in relation to a desired achievement standard, but still involve subjective judg‐ ments of one’s gaps and changes in knowledge (Pekrun, Vogl, et al., 2017). They arise in contexts requiring engagement with novel or non-routine tasks, such as problem-solving or research projects, as they typically emerge when being con‐ fronted with unexpected information or cognitive incongruity. Confusion and frustration are particularly salient epistemic emotions in educational research (Di Leo et al., 2019; Muis et al., 2018). Another category, topic-related emotions, is triggered directly by the subject matter of learning tasks. Examples include feelings of sadness when learning about political conflicts, or disgust when engaging with certain scientific ma‐ terials. Lastly, social emotions relate to others’ actions or achievements. These emotions encompass admiration, envy, or sympathy, becoming especially rele‐ vant in collaborative learning environments (Järvelä, 2012). Learning and emotions 69 In sum, educational settings, and the activity – and duty – of learning in‐ volves a rich array of emotions. Clearly, achievement emotions are quite fre‐ quent here: The pleasure of success, and pain of failure are pervasive in educa‐ tional settings. However, learning in terms of expanding and reorganizing one’s knowledge base also bears nuanced epistemic emotions, learning topics can arouse emotions in and of themselves, and given that educational settings are typically organized in communities of similarly aged learners, social emotions also accompany learning. 1.2 Appraisal antecedents of achievement emotions A key theory in educational psychology is Pekrun’s Control-Value Theory (CVT; e.g., Pekrun, 2018). This theory is grounded in appraisal perspectives, suggesting that an individual’s cognitive assessments of a situation play a cru‐ cial role in emotional experiences (Scherer & Moors, 2019). CVT integrates elements from transactional theories of stress-related emotions (Lazarus & Folkman, 1984) and attribution theory concerning emotions (Graham & Tay‐ lor, 2014), alongside conceptual overlaps with expectancy-value theories of achievement motivation (Eccles, 2005; Rosenzweig et al., 2019). Initially, CVT concentrated on achievement emotions (see Pekrun, 2024, for a revised version that encompasses multiple groups of emotions). According to CVT, two ap‐ praisals are particularly influential in eliciting achievement emotions: subjec‐ tive control over learning and performance activities and the subjective value of these activities and outcomes. Different discrete emotions form based on distinct patterns of control and value appraisals; for example, enjoyment arises when control is high and success is anticipated, while test anxiety increases when control is low and potential failure is a possibility. Furthermore, control and value appraisals interact, implying that the impact of one appraisal on an emotion can be assumed to depend on the level of the other. For instance, a student taking a low-stakes test (little value) may find that their perceived con‐ trol (i.e., low confidence in success) has less influence on test anxiety compared to when the test is high-stakes (e.g. crucial for college entry; hence has a high value). Extensive empirical research supports the links between students’ control and value appraisals and a range of achievement emotions. Traditional class‐ room studies are summarized by Pekrun and Perry (2014), while Loderer et al. (2020) address technology-based learning environments. 70 Anne C. Frenzel & Thomas Goetz 1.3 Social-cognitive antecedents of achievement emotions CVT adopts a social-cognitive perspective, positing that the perceived social environment plays a significant role in shaping students’ control and value ap‐ praisals, and consequently, their achievement emotions. Pekrun (2018) identi‐ fies key aspects of the social environment, including (1) facets of instruction, (2) value induction, (3) autonomy support, (4) goal structures and expectations established by teachers or classrooms, and (5) achievement feedback and its consequences. Empirical evidence demonstrates correlational and predictive links between these environmental factors and students’ emotions, with control and value appraisals serving as mediators. For example, Lazarides and Buchholz (2019) found that students’ perceptions of instructional elements like teacher sup‐ port, cognitive activation, and classroom management were linked to their emotions of enjoyment, anxiety, and anger. Further, Flunger et al. (2019) conducted a field experiment contrasting autonomy-supportive instruction with traditional teacher-centered methods in physics education, revealing that autonomy support—characterized by offering choices and informational language—enhanced positive achievement emotions while reducing negative ones, especially among students with stronger prior performance. In addition, achievement feedback, often conveyed as grades in formal educational settings, significantly impacts students’ control and value appraisals regarding their learning activities and subsequently affects their achievement emotions (see Goetz et al., 2018, for a review on feedback and emotions). Beyond the classroom, family dynamics, peer groups, and broader macrocontexts (such as culturally shaped attitudes towards education) influence stu‐ dents’ achievement emotions. For instance, research by Ansong et al. (2017) and Dong et al. (2020) highlight that parents and peers significantly shape val‐ ues for adolescents in Ghana and China, affecting their engagement, enjoyment, and boredom in school. At a macro-system level, high-stakes testing has been consistently shown to undermine children’s well-being (Cho & Chan, 2020). 2. Relevance of emotions for learning and performance Drawing from basic psychological research on the functions of emotions, it is evident that emotions are linked with learning in many different ways, and hence are linked with performance outcomes. Specifically, the connection be‐ tween emotions and performance is mediated by various cognitive, self-regu‐ latory, and motivational mechanisms (Pekrun, 2018). There is empirical evi‐ dence that negative emotions during learning—such as anger, anxiety, shame, Learning and emotions 71 boredom, and hopelessness—are associated with task-irrelevant thinking, a tendency to use shallow learning strategies (e.g., rehearsal), and less frequent use of metacognitive strategies (Pekrun et al., 2002). Conversely, enjoyment in learning is linked to lower levels of task-irrelevant thinking, enhanced focus, and more effective self-regulation, contributing to deeper and more sustainable learning (Ahmed et al., 2013; Obergriesser & Stoeger, 2020). Furthermore, substantial empirical evidence exists regarding the relation‐ ships between achievement emotions and achievement motivation during the learning process (see Huang, 2011, for a meta-analysis on the links between achievement goals and emotions). Emotions also influence both intrinsic and extrinsic forms of motivation; for instance, positive emotions, particularly en‐ joyment, are significant drivers of intrinsic motivation (Isen & Reeve, 2005). In contrast, negative emotions like anxiety and anger can lead learners to focus on the task’s adverse aspects or the consequences of potential failure, correlating positively with extrinsic motivation or less self-determined types of motivation. It is important to note, however, that research on achievement emotions has rarely been integrated with studies on intrinsic versus extrinsic motivation from a self-determination theory perspective (an exception is Sutter-Brandenberger et al., 2018). Further, numerous studies have examined direct relationships between achievement emotions and academic performance. Generally, these links are positive for pleasant emotions and negative for unpleasant emotions, as evi‐ denced by multiple meta-analyses including various different discrete achieve‐ ment emotions, including enjoyment, anger / frustration, boredom, and the most prominent (test) anxiety (Camacho-Morles et al., 2021; Tze et al., 2016; von der Embse et al., 2018). Longitudinal studies have shown that these corre‐ lations are often reciprocal, with emotions influencing academic performance and vice versa (Forsblom et al., 2022; Lichtenfeld et al., 2022; Pekrun, Lichten‐ feld, et al., 2017; Pekrun et al., 2023). In summary, there is compelling evidence that emotions significantly influ‐ ence achievement, while achievement outcomes also affect emotions. There is a complex interplay among emotions, cognitions, motivation, learning behav‐ iors, and achievement, which creates positive and negative cycles. 3. Designing learning environments considering students’ emotions Based on the insights into the antecedents of students’ emotions, it is possible to derive implications for how learning environments can be designed so that students experience more positive and less negative emotions. Given the strong connections between students’ emotions and their learning and achievement 72 Anne C. Frenzel & Thomas Goetz Figure 1. Principles for optimizing students’ control and value appraisals to design emotionally healthy learning environments. Abbildung verfügbar unter: 10 . 6084 / m9 . figshare . 30103837 , unter CC license ( https://creativecommons.org/licenses/by/4.0/ ) outcomes, such learning environments should also be conducive to effective learning and optimal achievement outcomes. The following principles outline ways to achieve this, aligning with and expanding upon the “design principles for adaptive motivation and emotion in education” proposed by LinnenbrinkGarcia et al. (2016). A visual summary is depicted in Figure 1. 3.1 Promoting enjoyment of learning Fostering enjoyment of learning should be a priority to optimize the emotional atmosphere in the classroom. Without resorting to overly entertaining meth‐ ods, teachers can focus on making topics and learning activities engaging and enjoyable. It is also advisable for teachers to focus on their own enjoyment dur‐ ing teaching, and to remind themselves of what they themselves find fascinating about the lesson content: There is consistent evidence that teachers’ own enjoy‐ ment and enthusiasm during teaching can transmit to students (Frenzel et al., 2018; Frenzel et al., 2021; Frenzel et al., 2024). Of course, simply creating a joy‐ ful classroom atmosphere likely will not suffice to promote learning. However, students’ willingness to maintain their attention and to exert mental effort will likely be enhanced when the learning tasks are enjoyable. Learning and emotions 79 . Obergriesser, S., & Stoeger, H. (2020). Students’ emotions of enjoyment and boredom and their use of cognitive learning strategies – How do they affect one another? Learning and Instruction, 66, 101285. https://doi.org/10.1016/j.learninstruc.2019.101285 . Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psy‐ chology Review, 18, 315–341. https://doi.org/10.1007/s10648-006-9029-9 . Pekrun, R. (2018). Control-value theory: A social-cognitive approach to achievement emotions. In G. A. D. Liem & D. M. McInerney (Eds.), Big theories revisited 2: A volume of research on sociocultural influences on motivation and learning (pp. 165– 190). Information Age Publishing. . Pekrun, R. (2024). Control-Value Theory: From Achievement Emotion to a General Theory of Human Emotions. Educational Psychology Review, 36(3). https://doi.org/ 10.1007/s10648-024-09909-7 . Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in stu‐ dents’ self-regulated learning and achievement: A program of qualitative and quan‐ titative research. Educational Psychologist, 37, 91–105. https://doi.org/10.1207/ S15326985EP3702_4 . Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achieve‐ ment emotions and academic performance: Longitudinal models of reciprocal ef‐ fects. Child Development, 88, 1653–1670. https://doi.org/10.1111/cdev.12704 . Pekrun, R., Marsh, H. W., Suessenbach, F., Frenzel, A. C., & Goetz, T. (2023). School grades and students’ emotions: Longitudinal models of within-person reciprocal ef‐ fects. Learning and Instruction, 83, 101626. https://doi.org/10.1016/j.learninstruc. 2022.101626 . Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In R. Pekrun & E. A. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 120–141). Routledge. https://doi.org/10.4324/9780203148211 . Pekrun, R., Vogl, E., Muis, K. R., & Sinatra, G. M. (2017). Measuring emotions during epistemic activities: the epistemically-related emotion scales. Cognition and Emotion, 31(6), 1268–1276. https://doi.org/10.1080/02699931.2016.1204989 . Pianta, R. C., Hamre, B. K., & Allen, J. P. (2012). Teacher-student relationships and engagement: Conceptualizing, measuring, and improving the capacity of classroom interactions. In S. Christenson, A. Reschly & C. Wylie (Eds.), Handbook of research on student engagement (pp. 365–386). Springer. https://doi.org/10.1007/978-1-46142018-7_17 . Putwain, C. W., Nicholson, L. J., & Kutuk, G. (2022). Warning students of the con‐ sequences of examination failure: An effective strategy for promoting student en‐ gagement? Journal of Educational Psychology, 115(1), 36–54. https://doi.org/10.1037/ edu0000741 . Quin, D. (2017). Longitudinal and contextual associations between teacher-student re‐ lationships and student engagement: A systematic review. Review of Educational Re‐ search, 87(2), 345–387. https://doi.org/10.3102/0034654316669434 80 Anne C. Frenzel & Thomas Goetz . Reeve, J. (2009). Why teachers adopt a controlling motivating style toward students and how they can become more autonomy supportive. Educational Psychologist, 44, 159– 175. https://doi.org/10.1080/00461520903028990 . Roorda, D., Koomen, H., Spilt, J., & Oort, F. (2011). The influence of affective teacherstudent relationships on students’ school engagement and achievement: A meta-an‐ alytic approach. Review of Educational Research, 81(4), 493–529. https://doi.org/10. 3102/0034654311421793 . Rosenzweig, E. Q., Wigfield, A., & Eccles, J. S. (2019). Expectancy-value theory and its relevance for student motivation and learning. In K. A. Renninger & S. E. Hidi (Eds.), The Cambridge handbook of motivation and learning (pp. 617–644). Cambridge Uni‐ versity Press. https://doi.org/10.1017/9781316823279.026 . Rottweiler, A.-L., Stockinger, K., & Nett, U. E. (2023). Students’ regulation of anxiety and hope – A multilevel latent profile analysis. Emotion, 23(7), 1891–1903. https:// doi.org/https://doi.org/10.1037/emo0001200 . Scherer, K. R., & Moors, A. (2019). The emotion process: Event appraisal and compo‐ nent differentiation. Annual Review of Psychology, 70, 719–745. https://doi.org/10. 1146/annurev-psych-122216-011854 . Stockinger, K., Dresel, M., Marsh, H., & Pekrun, R. (2025). Strategies for regulating achievement emotions: Conceptualization and relations with university students’ emotions, well-being, and health. Learning and Instruction, 98, 102089. https://doi. org/10.1016/j.learninstruc.2025.102089 . Sutter-Brandenberger, C. C., Hagenauer, G., & Hascher, T. (2018). Students’ self-deter‐ mined motivation and negative emotions in mathematics in lower secondary educa‐ tion – Investigating reciprocal relations. Contemporary Educational Psychology, 55, 166–175. https://doi.org/10.1016/j.cedpsych.2018.10.002 . Tze, V. M. C., Daniels, L. M., & Klassen, R. M. (2016). Evaluating the relationship be‐ tween boredom and academic outcomes: A meta-analysis. Educational Psychology Review, 28(1), 119–144. https://doi.org/10.1007/s10648-015-9301-y . von der Embse, N., Jester, D., Roy, D., & Post, J. (2018). Test anxiety effects, predictors, and correlates: A 30-year meta-analytic review. Journal of Affective Disorders, 227, 483–493. https://doi.org/10.1016/j.jad.2017.11.048 . Vygotsky, L. S. (1980). Mind in society: The development of higher psychological pro‐ cesses. Harvard University Press. https://doi.org/10.2307/j.ctvjf9vz4 Carmen Zurbriggen Well-being and learning Taking inclusive education as a prominent example 1. Introduction Over the last two decades, student well-being has increasingly moved into the spotlight of educational research. An examination of the rapidly growing body of literature reveals that student well-being is considered both particularly rel‐ evant for academic functioning and as a favourable educational outcome in itself (Steinmayr et al., 2018; Suldo et al., 2011). It is widely assumed that wellbeing and learning are intricately linked. On the one hand, students’ schoolrelated well-being serves as an indicator of the quality of a learning environ‐ ment, which facilitates positive learning processes. On the other hand, it is gen‐ erally expected that successful learning fosters students’ well-being (Hascher, 2012). Similar assumptions apply to the context of inclusive education, where students’ well-being is recognised not only as a key indicator of the quality of inclusion but also as an aim of inclusive education (Powell & Hadjar, 2018; Venetz, 2015; Wächter et al., 2024). Nonetheless, a recent systematic review regarding the association between well-being and academic achievement of school-aged children has uncovered inconsistencies in findings, ranging from positive associations to no associ‐ ation or contradictory results (Amholt et al., 2020). Conflicting results are also reported regarding students’ well-being in relation to inclusive education (Dalgaard et al., 2022; Goldan et al., 2022). Furthermore, several studies have demonstrated a general decline in subjective well-being from childhood to ado‐ lescence (Bücker et al., 2023; Casas & González-Carrasco, 2019), and from pri‐ mary to lower secondary education (Knickenberg & Zurbriggen, 2021; Ober‐ meier et al., 2022). These apparent contradictions raise the question of a pos‐ sible trade-off between fostering students’ well-being and promoting academic learning and achievement (Clarke, 2020). Against this backdrop, this chapter aims first to briefly elucidate the concept of student well-being and its measurement, and second, to provide concise in‐ sights into the state of research concerning the associations between students’ 82 Carmen Zurbriggen subjective well-being and academic achievement, as well as on relevant factors of the learning environment within a classroom. Additionally, exemplary find‐ ings on students’ subjective well-being in relation to inclusive education are presented, followed by a (preliminary) conclusion and suggestions for future directions. 2. Focus on students’ subjective well-being 2.1 Conceptualising well-being Since well-being is a broad, multifaceted construct (Tov, 2018), it may explain inconsistencies in findings regarding the association of students’ well-being and academic achievement. Corresponding to an umbrella term, which is frequently used interchangeably with other terms (e.g., happiness, mental health, life sat‐ isfaction) and applied in many different contexts, well-being is often viewed as an ambiguous concept (Joos et al., 2018). The variety of terms or definitions describing well-being can be broadly grouped into objective and subjective aspects or dimensions of well-being (Voukelatou et al., 2021). While objective well-being reflects people’s material living conditions and their observable quality of life – including aspects such as physical health, safety, environment, and academic achievement – subjective well-being captures people’s own perceptions or evaluations of their overall quality of life or of specific domains or situations. Empirical educational research has mainly focused on students’ subjective well-being. The most widely acknowledged conception of subjective well-being is the one by Ed Diener (Schimmack, 2008). According to Diener (1984), sub‐ jective well-being consists of the reflective cognitive judgements people make about their life in general or in specific domains (e.g., work, school, family), as well as positive and negative emotional responses to ongoing life experiences or specific situations (Diener et al., 2018). 2.2 Measuring students’ subjective well-being in school When conceptualising subjective well-being, it is essential to not only differen‐ tiate between components (i.e., cognitive, affective) and life domains (i.e., gen‐ eral, domain-specific), but also between time frames as in the general state-trait model (Eid, 2008). Accordingly, the framework model for the measurement of subjective well-being by Lischetzke and Eid (2006) distinguishes between ha‐ bitual and momentary or situation-specific subjective well-being. Well-being and learning 83 This distinction is also important when measuring students’ subjective wellbeing in school. One prominent example of subjective well-being as a trait is the conception by Hascher (2010). She defined students’ subjective well-being in school as a multicomponent construct that includes positive attitudes towards school, enjoyment in school, the absence of worries about school, a positive academic self-concept, and the absence of social problems as well as physical complaints in school. Measuring students’ subjective well-being as a state pri‐ marily concerns its affective component, or more specifically, positive and neg‐ ative affective experience in specific (learning) situations. Affective experience or state-like emotions can be assessed via momentary data techniques such as the experience sampling method (ESM; Hektner et al., 2007), which allows to capture state characteristics in situ and in real-time. In educational studies, researchers have typically relied on retrospective selfreports when measuring students’ subjective well-being. However, such mem‐ ory-based reports of emotional aspects are often overor underestimated (Con‐ ner & Barrett, 2012). To investigate retrospection effects of students’ emotional experience in the classroom and possible changes during early adolescence, we compared retrospectively assessed affective experience (i.e., positive and nega‐ tive activation) with in situ via ESM reported affective experience (Zurbriggen et al., 2021). Our findings indicated to a positive recall bias (i.e., ‘rosy view’) of students’ affective experience at the end of primary education and to a negative shift in recall bias (i.e., ‘blue view’) by the end of lower secondary education. 3. Subjective well-being, academic achievement, and the learning environment While about twenty years ago researchers had to make a plea for the relevance of well-being in school (Fend & Sandmeier, 2004), it is nowadays generally as‐ sumed that students’ subjective well-being reflects a school environment that facilitates learning processes and promotes students’ academic achievement. Successful learning, in turn, is thought to be essential for experiencing satisfac‐ tion and positive emotions in school (Hascher & Hagenauer, 2011). Thus, sub‐ jective well-being is viewed as a predictor of students’ engagement and motiva‐ tion to learn (Hascher, 2012). Since school and the classroom are also sources of negative experiences (e.g., academic failure, rejection by peers), subjective wellbeing can also serve a preventive function. 84 Carmen Zurbriggen 3.1 Associations of subjective well-being with academic achievement and engagement Based on such theoretical considerations, several studies have examined the re‐ lationship between students’ subjective well-being and academic achievement. The first meta-analysis on this topic showed a positive, but only small cor‐ relation (r= 0.164) between academic achievement and subjective well-being (Bücker et al., 2018). The correlation was stable across various levels of de‐ mographic variables, different components, and domains of subjective wellbeing, as well as across different contents and alternative measures of academic achievement. The authors concluded that high-achieving students do not nec‐ essarily report high subjective well-being, and low-achieving students do not automatically report low subjective well-being. As students’ engagement has been described as an important strength lead‐ ing to well-being and positive learning outcomes, Wong and colleagues (2024) investigated in their recent meta-analysis the associations of students’ academic engagement with subjective well-being and academic achievement. Their metaanalysis revealed medium correlations between students’ engagement and sub‐ jective well-being, being most closely related to affective engagement (r= .40), followed by cognitive (r= .35) and behavioural (r= .31) engagement. The aver‐ age correlation of engagement with subjective well-being (r= .35) was similar to the one with academic achievement (r= .33). 3.2 Factors of the learning environment affecting subjective well-being Although the classroom is considered the most influential school context both on students’ subjective well-being and their learning (Hascher, 2012), empiri‐ cal evidence supporting these theoretical assumptions is still scarce. However, several studies have explored the impact of certain aspects of the learning envi‐ ronment within a classroom on students’ subjective well-being. A large part of these aspects corresponds to social characteristics of the learning environment, but also a teacher’s instructional practice plays a decisive role in developing and fostering students’ well-being. Social factors within a classroom that positively affect students’ subjec‐ tive well-being include positive relationships with peers and peer support (Goswami, 2012; Hoferichter et al., 2021), a positive student-teacher relation‐ ship (Zheng, 2022), and a supportive classroom climate (Oberle, 2018; Zur‐ briggen, Hofmann, et al., 2023). Factors that negatively affect students’ sub‐ jective well-being include peer bullying, (strong) competition among peers (Arslan et al., 2021; Hoferichter & Raufelder, 2017), conflicts with teachers, Well-being and learning 85 and pressure from teachers to achieve (Kiuru et al., 2020; Stang-Rabrig et al., 2023). Regarding teachers’ instructional practices, recent studies have shown that differentiated instruction is positively related to students’ subjective well-being (Pozas et al., 2021) and that good instructional quality has a positive impact on students’ subjective well-being (Obermeier et al., 2022). 4. Inclusive education and students’ subjective well-being Most of the aforementioned factors positively affecting students’ subjective well-being and learning correspond to crucial characteristics for the successful implementation of inclusive education. Inclusive education not only means ac‐ cess to general education but also providing high-quality teaching and learning environments that maximise the academic and social-emotional development of all students (United Nations [UN], 2006, Art. 26). Differentiated instruc‐ tion is seen as a vehicle to achieve inclusive education (Pozas et al., 2021) by implementing, for instance, adequately adapted teaching practices tailored to students’ needs (Gheyssens et al., 2023). As already mentioned, students’ subjective well-being is considered an im‐ portant indication of the quality of inclusive education. Simultaneously, it also serves as a useful indicator for adaptive teaching, as it affects a teacher’s deci‐ sions on how to design classroom interventions and instructions (Praetorius et al., 2015). Thus, accurate teacher judgement of students’ subjective well-being is an important condition for adaptive teaching, which in turn promotes each student’s learning and academic development. In the following, exemplary findings of our own studies on the topic of in‐ clusive education and subjective well-being are briefly presented. 4.1 Students’ emotional experience and adaptive teaching The first research question relates to the affective component of subjective well-being as a state: How do students experience different classroom situa‐ tions of adaptive teaching in inclusive education? To address this question, we conducted an experience sampling study in 40 classrooms of Grades 5 and 6 in Switzerland (primary education) with about 720 students and a total of over 8000 occasions (Zurbriggen & Venetz, 2018). The results of our multi‐ level multigroup analyses showed that the included characteristics of adaptive teaching had a similar positive effect on students’ emotional experience. When students had the option to choose between tasks, they were generally more positively activated (e.g., more energetic) and less negatively activated (e.g., 86 Carmen Zurbriggen less stressed) than when they could not choose between tasks. Similarly, the students were more positively activated and less negatively activated when they were able to work with their peers and did not have to work alone. Furthermore, students’ emotional experience was best when the perceived difficulty level of a task was slightly higher than average. However, one differential effect between student groups was observed: The group of students with above-average aca‐ demic achievement and well-adjusted social behaviour only reached maximum positive activation for tasks that they experienced as significantly more difficult than average. 4.2 Teacher judgement accuracy of students’ subjective well-being in inclusive education Second, we investigated teachers’ judgement accuracy of students’ subjective well-being as a trait and whether the (in-)accuracy in teacher reports (i.e., speci‐ ficity) could be explained by student and teacher characteristics. To address these questions, we drew from the self-reports of about 2600 students in Grade 6 and the corresponding ratings of about 430 homeroom teachers and applied a multiple-indicator correlated trait-correlated method minus one model with explanatory variables (Zurbriggen, Nusser, et al., 2023). Following the concep‐ tion by Hascher (2010), we focused on three components: emotional well-being in school, social inclusion in class, and academic self-concept. To investigate factors explaining the specificity in teacher reports of students’ subjective wellbeing, we accounted for selected characteristics relevant in the context of inclu‐ sive education and adaptive teaching. Our findings showed that teachers’ judgement accuracy of students’ subjec‐ tive well-being was only low to moderate. 12 % of the variance in teacher reports of students’ emotional well-being was shared with the students’ self-reports and 18 % with regard to social inclusion, while for the academic self-concept the consistency was a little higher at 33 % . In terms of student characteristics, the status of special educational needs (SEN), gender, and academic achievement were significantly related to the specificity in teacher reports of all three aspects of students’ subjective wellbeing. The effects were most pronounced for academic achievement, partic‐ ularly on the academic self-concept. German as a primary vs. secondary lan‐ guage could only explain the specificity in teacher reports of the academic selfconcept. The teacher characteristics included could explain the specificity in teacher reports only to a small extent. While teaching experience was nega‐ tively, but only very weakly, associated with the specificity in teacher reports of the students’ academic self-concept, teachers’ self-efficacy, their attitudes Well-being and learning 87 towards inclusion, and their responsibility for every student were positively associated with the specificity in teacher reports of students’ emotional wellbeing and their social inclusion in class. 4.3 Development of students’ subjective well-being in lower secondary education The third and last research question relates to the development of students’ subjective well-being in inclusive classrooms during lower secondary educa‐ tion and whether students’ academic achievement can predict changes in their subjective well-being. To this end, we draw on data from the longitudinal project Inclusive Educa‐ tion in Lower Secondary Schools in Germany (INSIDE). The sample consisted of about 4600 students from lower secondary education who participated in Grades 6, 7, and 9. We specified a latent neighbour change model with predictors for change in subjective well-being. As expected, the findings showed that students’ emotional well-being and their social inclusion slightly decreased from Grade 6 to Grade 9. The academic self-concept remained relatively stable. Furthermore, peer-related classroom climate and student-teacher relationship could predict change in students’ subjective well-being. 5. Conclusion and future directions This leads back to the initial question of this chapter: whether there is a tradeoff between well-being and learning. Rather than being incompatible goals, re‐ search suggests that their relationship is not straightforward and that several gaps need to be addressed (Clarke, 2020). To reach a more solid conclusion about the assumed reciprocal relationship between students’ subjective wellbeing and academic achievement or other learning outcomes, more longitudi‐ nal studies are required (Bücker et al., 2018). Future studies would benefit from including potential moderating or mediat‐ ing variables at the individual, classroom, and school levels, such as personality, instructional strategies, or school climate (Steinmayr et al., 2018). For instance, it could be worthwhile to investigate the role of students’ engagement in this regard, as students’ subjective well-being seems to be more strongly associated with academic engagement than with academic achievement. Our findings indicate that adaptive teaching has a positive impact on stu‐ dents’ emotional experience (Zurbriggen & Venetz, 2018). The question re‐ mains whether adaptive teaching has mediumand long-term effects on stu‐ dents’ subjective well-being as well as on successful learning. Additional re‐ 88 Carmen Zurbriggen search differentiating between components and time frames of subjective wellbeing could provide important insights into specific effects and underlying pro‐ cesses. In light of the relatively low accuracy of teacher judgement regarding stu‐ dents’ subjective well-being (Zurbriggen, Nusser et al., 2023), there is a risk of unjustly accusing teachers or placing additional pressure on these already heavily burdened professionals. Given the complexity and difficulty of esti‐ mating subjective or internal constructs, the discrepancies between the selfreports and teacher ratings of students’ subjective well-being could be just used as diagnostic information. Being aware of student’s own view and possible bias may reduce the expectancy effects of underor overestimation, for instance, of students with SEN or low achievement. Interventions regarding inclusion have the potential to change teachers’ beliefs, particularly if they provide the opportunity to gain experience with inclusive practices (Dignath et al., 2022). As for further practical implications, creating a supportive social classroom climate can enhance students’ subjective well-being, but also positively affect learning processes and achievement. This could be undertaken by incorporat‐ ing elements and strategies of ‘positive education’ (Seligman et al., 2009), or by implementing evidence-based, systemic social and emotional learning (RimmKaufman et al., 2023). In this vein, teachers should be supported in their en‐ deavours in fostering a rich learning environment where all students can thrive academically and enhance their subjective well-being. References . Amholt, T. T., Dammeyer, J., Carter, R., & Niclasen, J. (2020). Psychological well-being and academic achievement among school-aged children: A systematic review. Child Indicator Research 13, 1523–1548. https://doi.org/10.1007/s12187-020-09725-9 . Arslan, G., Allen, K. A., & Tanhan, A. (2021). School bullying, mental health, and well‐ being in adolescents: Mediating impact of positive psychological orientations. Child Indicator Research, 14, 1007–1026. https://doi.org/10.1007/s12187-020-09780-2 . Bücker, S., Nuraydin, S., Simonsmeier, B. A., Schneider, M, & Luhmann, M. (2018). Sub‐ jective well-being and academic achievement: A meta-analysis. Journal of Research in Personality, 74, 83–94. https://doi.org/10.1016/j.jrp.2018.02.007 . Bücker, S., Luhmann, M., Haehner, P., Bühler, J. L., Dapp, L. C., Luciano, E. C., & Orth, U. (2023). The development of subjective well-being across the life span: A metaanalytic review of longitudinal studies. Psychological Bulletin, 149(7–8), 418–446. https://doi.org/10.1037/bul0000401 . Casas, F., & González-Carrasco, M. (2019). Subjective well-being decreasing with age: New research on children over 8. Child Development, 90(2), 375–394. https://doi. org/10.1111/cdev.13133 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 101 teachers were more likely to attribute success in math to innate ability com‐ pared to German language arts. Furthermore, the stronger a teacher held a belief in the necessity of innate ability for math success, the lower the intrinsic motivation of their low-achieving students. These results highlight that teach‐ ers’ perceptions of math as requiring innate ability may hinder the creation of a classroom environment that promotes intrinsic motivation for all students and might be especially detrimental for low-achieving students. These are just a few examples of factors that might influence expectancyvalue variables. Beyond interventions and socializers’ beliefs, numerous other factors impact these variables, including cultural norms, classroom character‐ istics, teaching styles, teacher attributes, environmental factors, and more (see Baumert et al., 2002; Eccles & Wigfield, 2020; Kunter et al., 2013). Recent re‐ search has focused on students’ perceptions of socializers’ beliefs (e.g., Reschke et al., 2023) and classroom characteristics (Wirthwein et al., 2021). Under‐ standing the interplay between objective characteristics, students’ perceptions of them, the factors influencing those perceptions, and their impact on key motivational constructs such as expectancy-value variables will significantly enhance our understanding of motivation. 4. Conclusion on the importance of motivation Among the various motivational variables, expectations and values stand out as significant predictors of interindividual differences in career aspirations, aca‐ demic achievement, and its progression over time (Nishen et al., 2024; Stein‐ mayr & Spinath, 2009, 2010; Steinmayr et al., 2018, 2019). Thus, motivation holds considerable potential to enhance achievement-related outcomes across STEM fields and other domains. Importantly, it can be influenced by external factors beyond the individual, such as teachers’ beliefs about ability, which can be modified to a certain extent. However, the role of motivation in academic achievement should not be overstated, as academic achievement is influenced by numerous factors as outlined by several models explaining interindividual differences in academic achievement (e.g., Eccles & Wigfield, 2020; Baumert et al., 2002, p. 16). Some of these factors are very hard to change and are thus relatively stable, such as social and educational family background, while oth‐ ers are more flexible, including the quality of teaching, for example due to a teacher change. Additionally, some factors interact with motivation, such as aptitude (Bergold & Steinmayr, 2018), while others are unrelated. Academic achievement is a highly complex phenomenon that cannot be fully explained by a single variable or even a set of related variables like motivation. This complex‐ ity underscores the need for a multidimensional approach to understanding 102 Ricarda Steinmayr and fostering academic achievement which should consider motivation and its interplay with further achievement-related variables. Furthermore, quite a lot of open questions remain concerning the role of motivation for academic achievement as motivation is a complex construct influenced by a variety of factors spanning individual, contextual, cultural, and biological domains. Recent research highlights the reciprocal effects be‐ tween teacher characteristics and student motivation, emphasizing the dynamic interplay between educators’ attitudes, behaviors, and students’ motivational development (Kriegbaum et al., 2019). These findings underscore the impor‐ tance of teacher-student interactions in shaping long-term educational out‐ comes. The effectiveness of motivational strategies and interventions has also garnered considerable attention, particularly regarding their sustainability over time. Minimal interventions, which require relatively low investment of time and resources, have been shown to yield significant benefits in certain contexts (Harackiewicz et al., 2023). However, understanding their long-term impact on student motivation and achievement is critical for informing evidence-based practices in education. Cultural differences further complicate the landscape of motivation. Cultural norms, values, and beliefs can shape motivational pro‐ cesses, influencing how individuals perceive success, effort, and failure (e.g., Chirkov et al., 2003; Heine et al., 2001). These variations highlight the neces‐ sity of culturally responsive motivational strategies to support diverse student populations which has rarely been investigated. On a more fundamental level, biological and neurophysiological foundations of motivation provide insights into the underlying mechanisms driving motivational behaviors. Advances in neuroscience have revealed how brain structures, neurotransmitter systems, and hormonal processes interact to influence motivation and goal-directed ac‐ tions (Di Domenico & Ryan, 2017; Morris et al., 2022). However, there is still a need for a deeper understanding of how these biological and neurophysio‐ logical factors combine to produce unique motivational patterns, taking into account personal, environmental, and cultural influences. While significant progress has been made in identifying general mechanisms, much remains to be explored regarding their nuanced interplay and real-world applications. An‐ other factor that needs more research is emotion. Motivation is also intricately linked with emotions. Emotional states can significantly enhance or undermine motivational processes, shaping individuals’ goals (Chamani et al., 2023; Jär‐ venoja et al., 2018), and performance in various activities. However, the pro‐ cess underlying these relations are not fully understood. The interplay between motivation and emotions is thus a critical area for further investigation. Lastly, sources of individual differences in motivation extend beyond frameworks like the situated expectancy-value model (Eccles & Wigfield, 2020). Factors such as Learning and motivation 103 personality traits, prior experiences, and social influences contribute to these differences, underscoring the need for a more integrative understanding of the factors shaping motivation. By exploring these diverse dimensions, researchers and practitioners can develop more holistic approaches to fostering motivation in educational and other settings. Given its importance for academic achieve‐ ment, the endeavor is worthwhile. References . Ames, C. (1992). Classrooms: Goals structures, and student motivation. Journal of Edu‐ cational Psychology, 84, 261–271. https://doi.org/10.1037/0022-0663.84.3.261 . Baumert, J., Artelt, C., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., Schneider, W., Tillmann, K.-J., & Weiß, M. (2002). PISA 2000 – Die Länder der Bundesrepub‐ lik Deutschland im Vergleich. Leske + Budrich. https://doi.org/10.1007/978-3-66311042-2 . Bergold, S., & Steinmayr, R. (2016). The relation over time between achievement motivation and intelligence in young elementary school children: A latent crosslagged analysis. Contemporary Educational Psychology, 46, 228–240. https://doi.org/ 10.1016/j.cedpsych.2016.06.005 . Bergold, S., & Steinmayr, R. (2018). Personality and intelligence interact in the predic‐ tion of academic achievement. Journal of Intelligence, 6(2), 27. https://doi.org/10. 3390/jintelligence6020027 . Bergold, S., & Steinmayr, R. (2023). Teacher judgments predict developments in adoles‐ cents’ school performance, motivation, and life satisfaction. Journal of Educational Psychology, 115(4), 642–664. https://doi.org/10.1037/edu0000786 . Bjørnebekk, G., Diseth, Å., & Ulriksen, R. (2013). Achievement motives, self-efficacy, achievement goals, and academic achievement at multiple stages of education: A lon‐ gitudinal analysis. Psychological Reports, 112(3), 771–787. https://doi.org/10.2466/ 14.09.pr0.112.3.771-787 . Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Intrinsic motivation and extrinsic incentives jointly predict performance: A 40-year meta-analysis. Psychological Bul‐ letin, 140(4), 980–1008. https://doi.org/10.1037/a0035661 . Chamani, S., Razi, A., & Xodabande, I. (2023). Motivational and emotional states in self-directed language learning: A longitudinal study. Discover Education, 2(1), 23. https://doi.org/10.1007/s44217-023-00048-9 . Chirkov, V., Ryan, R. M., Kim, Y., & Kaplan, U. (2003). Differentiating autonomy from individualism and independence: A self-determination theory perspective on inter‐ nalization of cultural orientations and well-being. Journal of Personality and Social Psychology, 84(1), 97–110. https://doi.org/10.1037/0022-3514.84.1.97 . Ciani, K. D., Middleton, M. J., Summers, J. J., & Sheldon, K. M. (2010). Buffering against performance classroom goal structures: The importance of autonomy support and 104 Ricarda Steinmayr classroom community. Contemporary Educational Psychology, 35(1), 88–99. https:// doi.org/10.1016/j.cedpsych.2009.11.001 . Costa, P. T., & McCrae, R. R. (1995). Domains and facets: Hierarchical personality as‐ sessment using the revised NEO personality inventory. Journal of Personality Assess‐ ment, 64(1), 21–50. https://doi.org/10.1207/s15327752jpa6401_2 . Di Domenico, S. I., & Ryan, R. M. (2017). The emerging neuroscience of intrinsic mo‐ tivation: A new frontier in self-determination research. Frontiers in Human Neuro‐ science, 11, 145. https://doi.org/10.3389/fnhum.2017.00145 . Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Persever‐ ance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. https://doi.org/10.1037/0022-3514.92.6.1087 . Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and per‐ sonality. Psychological Review, 95(2), 256–273. https://doi.org/10.1037/0033-295X. 95.2.256 . Dweck, C.S., & Yeager, D.S. (2019). Mindsets: A View From Two Eras. Perspectives on Psychological Science, 14, 481–496. http://doi.org/10.1177/1745691618804166 . Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated ex‐ pectancy-value theory: A developmental, social cognitive, and sociocultural perspec‐ tive on motivation. Contemporary Educational Psychology, 61. https://doi.org/10. 1016/j.cedpsych.2020.101859 . Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72(1), 218– 232. https://doi.org/10.1037/0022-3514.72.1.218 . Gagné, F., & St Père, F. (2002). When IQ is controlled, does motivation still pre‐ dict achievement? Intelligence, 30(1), 71–100. https://doi.org/10.1016/S01602896(01)00068-X . Harackiewicz, J. M., Hecht, C. A., Asher, M. W., Beymer, P. N., Lamont, L. B., Wheeler, N. S., Else-Quest, N. M., Priniski, S. J., Smith, J. L., Hyde, J. S., & Thoman, D. B. (2023). A prosocial value intervention in gateway STEM courses. Journal of Personal‐ ity and Social Psychology, 125(6), 1265–1307. https://doi.org/10.1037/pspa0000356 . Heckhausen, H., & Heckhausen, J. (2018). Motivation und Handeln: Einführung und Überblick. Springer-Verlag. https://doi.org/10.1007/978-3-662-53927-9 . Heine, S. J., Lehman, D. R., Ide, E., Leung, C., Kitayama, S., Takata, T., & Matsumoto, H. (2001). Divergent consequences of success and failure in Japan and North America: An investigation of self-improving motivations and malleable selves. Journal of Per‐ sonality and Social Psychology, 81(4), 599–615. https://psycnet.apa.org/doi/10.1037/ 0022-3514.81.4.599 . Heyder, A., Weidinger, A. F., Cimpian, A., & Steinmayr, R. (2020). Teachers’ belief that math requires innate ability predicts lower intrinsic motivation among lowachieving students. Learning and Instruction, 65, 101220. https://doi.org/10.1016/ j.learninstruc.2019.101220