Learning and performance do not have to come at the expense of socio-emotional development

Stern, Elsbeth Learning and performance do not have to come at the expense of socio-emotional development Becker, Michael [Hrsg.]; Dignath, Charlotte [Hrsg.]; Bihler, Lilly-Marlen [Hrsg.]; Gaspard, Hanna [Hrsg.]; McElvany, Nele [Hrsg.]: Socio-emotional and cognitive development in learning. Educational goals in competition?! Münster ; New York : Waxmann 2026, S. 35-49. - (Dortmunder Symposium der Empirischen Bildungsforschung; 9) Quellenangabe/ Reference: Stern, Elsbeth: Learning and performance do not have to come at the expense of socio-emotional development - In: Becker, Michael [Hrsg.]; Dignath, Charlotte [Hrsg.]; Bihler, Lilly-Marlen [Hrsg.]; Gaspard, Hanna [Hrsg.]; McElvany, Nele [Hrsg.]: Socio-emotional and cognitive development in learning. Educational goals in competition?! Münster ; New York : Waxmann 2026, S. 35-49 - URN: urn:nbn:de:0111-pedocs-345054 - DOI: 10.25656/01:34505 https://nbn-resolving.org/urn:nbn:de:0111-pedocs-345054 https://doi.org/10.25656/01:34505 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 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 41 terest is also in line with an attitude held by many teachers (Lazarides et al., 2023). What all these beliefs have in common is that they see the causes for a lack of motivation primarily in the learners, which is of course a relief for teachers. However, motivation research has a much more differentiated view, which considers the context in which learning takes place. While intrinsic mo‐ tivation is a desirable goal, it would be unrealistic to expect it in every subject at any time. Students have developed their own pattern of interests and time allocation, which is at least partly desirable because they have to pave the way for their career paths. Rather than expecting students to enter the class with intrinsic motivation, teachers have to be prepared for less enthusiastic learners. Learning opportunities can be designed in such a way that students engage with the content, even if they are not too enthusiastic about it. The Self-Determination Theory (SDT) from Deci and Ryan (2012), which emphasizes the importance of conditions supporting the individuals’ expe‐ rience of autonomy, competence, and relatedness, provides a framework for designing such learning environments. Learners’ autonomy is violated if they have no idea of the benefits from participating in the curriculum, either in practical terms or in terms of intellectual insights. By starting a learning unit with a question that cannot be answered initially but will be so at the end can contribute to the feeling of autonomy. Also, giving learners some freedom to choose among the elements of the learning unit (e.g., problems to work on, texts to read, experiments to do) can increase the sense of autonomy. Learning is hard work and can be exhausting. Experiencing progress is key to maintaining the motivation to focus on further progress. Constantly experiencing failure is frustrating and will likely make students give up. Tailoring practice problems to students’ achievement level allows them to make progress with some effort. Applying means of formative assessment can help to support mastery instead of achievement orientation in two ways. First, it allows teachers to identify stu‐ dents’ difficulties and to present tailored problems, and secondly experiencing that teachers are interested in students’ state of knowledge without judging it gives feeling of relatedness (Lichtenberger et al., 2024). 4. Managing diversity In the early 20th century, a pragmatic need for predicting the learning potential of individuals initiated the development of standardized tests. When compul‐ sory schooling had become common more than a century earlier, the tremen‐ dous differences in learning potential became obvious. The need for a qualified workforce led to an extension of higher education and required valid and re‐ liable forecasts of performance potential in as yet unknown areas. Reasoning 42 Elsbeth Stern abilities, as they were tested in intelligence tests, revealed stable individual dif‐ ferences. Intelligence is now understood as a polygenically inherited uniform personality trait with a high reaction norm, which follows the normal distribu‐ tion, and which has a strong impact on the use of learning opportunities, and which explains individual differences in numerous outcome measures (Stern, 2017). In the past decades, numerous intelligence tests have been developed and standardized, appropriate for different diagnostic purposes, among them in educational contexts. Moreover, tremendous progress has been made in explain the neuro-cognitive foundations of differences in intelligence, which also have implications for educational decisions. The progress made in basic as well as in applied psychology calls for the education system to be analyzed in the light of intelligence research. 4.1 Intelligence and educational choices Different from the Anglo-American world, in Central Europe standardized rea‐ soning tests are rarely ever used for educational decisions beyond admission to special education programs. In the US and the UK, standardized university admission tests that show high correlations with intelligence tests. The same was formerly true for the admission to the grammar school in the UK, which was regulated by the 11+ examination. In central Europe, admission to the university is still mostly regulated after elementary school by assignment to aca‐ demic track schools (Gymnasium), and the decision is mainly based on grades and parents’ request. Those who do not go to the Gymnasium (depending on countries and regions [50–80 % ]) go to regular schools supposed to prepare for vocational education. While intelligence-based tests are rarely ever directly used for educational track choices, the substantial correlations between school grades and test scores of r = .50 (Roth et al., 2015) raise the question, to what extent intelligence indirectly prevails. In recent decades, numerous studies have revealed a considerable impact of the family background on educational tra‐ jectories, which is often more important than cognitive competencies, as many studies show (Blossfeld et al., 2016). Such trends are neither compatible with the values of a meritocratic society, nor do they raise hopes for economic stability and innovation. Considering the stable differences in cognitive abilities in the overall school system as well as in concrete classroom practice is a permanent challenge in education that has not yet met with much success. Meeting the different needs of a heterogeneous group of learners is inevitably accompanied by underand overload for many learners. Ability-grouping on the other hand goes along with misallocation, as even the best tests are far from perfect in terms of validity and reliability. Giving everyone the opportunity to 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. . 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