Beyond Self-Regulated Learning: Integrating Approaches to Self-Regulation in Education

Self-regulated learning (SRL) has become one of the most important theoretical concepts in educational research. In light of contemporary educational challenges, including the widespread use of information technology in educational settings, the growing focus on enabling students to become lifelong learners, and the increased emphasis on learner-controlled learning activities, SRL further shows a significant practical importance. The ability to effectively regulate learning processes is a key skill for learners to meet the aforementioned challenges. Typically, SRL is referred to as the regulation and control of cognitive, metacognitive, motivational, as well as affective states and processes in service of learning goals. Following this definition, a broad body of literature investigating SRL from different theoretical backgrounds and perspectives has shown that SRL is key factor for students’ academic success throughout all stages of education. However, the diversity in approaches to investigate SRL has also led to lack of clarity what SRL is and how it can be most effectively fostered. This issues becomes even more apparent when SRL is investigated in the context of other, more general research traditions on self-regulation (SR). The present dissertation addressed this research issue by integrating four areas of research on (SR) in education. These were derived from the mechanisms through which self- regulatory variables affect learning and include learning activities (e.g., cognitive and metacognitive strategies), driving forces (e.g., motivation and affect), personal dispositions (e.g., personality), and limited resources (e.g., working memory and executive functions). Specifically, based on research that has strongly linked each of these areas of research to learning and academic achievement, an integrative framework that situates SRL as part of SR in education has been proposed. To test this framework, the present dissertation tested the predictive value of key constructs representing all areas of proposed framework across different contexts (e.g., learning in school and laboratory learning task). Through this approach, this dissertation is the first study that empirically integrated the aforementioned research traditions on self- regulation in education. Study I aimed at identifying the best predictors of learning in school and laboratory learning tasks from a comprehensive set of self-regulatory constructs that reflect the four areas of research on self-regulation proposed in the framework (i.e., learning activities, driving forces, personal dispositions, and limited resources). Specifically, robust machine learning predictions were used to predict performance in school and laboratory learning task across five academic domains (i.e., math, physics, biology, art, and history). Results showed that predictors from all areas of the proposed framework are required to optimally predict learning in both settings. However, the specific variables that optimally predicted learning in school and laboratory learning tasks varied. While measures of driving forces (i.e., motivation) and limited resources (i.e., working memory capacity) predicted learning in both settings, predictors representing learning activities (e.g., effort-related vs rehearsal strategies) and personality (e.g., openness) only showed predictive value for one of the outcomes. Study II investigated if and how self-regulatory requirements in a computer- based learning task differed depending on the way participants interacted with the learning environment. In detail, participants used either mouse-based or touch-based interaction to work with the learning materials. Robust machine learning models predicting learning outcomes in both conditions were developed. Specifically, these models used measures that represent the four core areas of the proposed framework similar to Study I. Results showed that self-regulatory requirements were higher when learning with tablets. Specifically, beyond the predictive value of prior knowledge, learning on tablet was determined by critical evaluation (learning activity), motivational cost (driving force), openness (personal disposition), and switching (limited resource). Differences in performance using mouse-based interactions on the other hand were only related to control measures (reading comprehension and prior knowledge) but not related to self-regulatory constructs. Study III extended the scope of the first two studies to a detailed, process-oriented investigation of one key area of the proposed framework. In this study the emotional experience of participants (driving force) and its temporal unfolding throughout a learning activity was related to learning. Results showed that a group of students with primarily negative emotional experiences learned the least. Moreover, these students showed an increase in negative emotionality during learning that was predictive of lower learning outcomes. Lastly, additional analyses demonstrated that these emotional processes are related to stable personal dispositions (i.e., trait emotion regulation and neuroticism). Overall, across all three studies this dissertation has shown that SR shares a common underlying structure across contexts. However, the specific SR processes required to achieve optimal learning outcomes differ depending on the learning task, context and environment. Through these findings, this dissertation provides a theoretically derived and empirically supported theoretical framework, that situates self-regulated learning within the larger context of self-regulation in education. The findings of the studies are discussed in light of the proposed framework and the added value of a broader conceptualization of SR in education. Key steps for future research programs to extend upon this framework and integrate research traditions on self- regulation in education are derived.Dissertation ist gesperrt bis 21.05.2023 !Selbstreguliertes Lernen (SRL) ist eines der wichtigsten theoretischen Konzepte der Bildungsforschung. In Anbetracht aktueller Herausforderungen im Bildungsbereich, wie beispielweise den weit verbreiteten Einsatz von Informationstechnologie in Bildungskontexten, den größer werdenden Fokus Lerner zum lebenslangen Lernen zu befähigen oder dem zunehmenden Schwerpunkt auf Lerner gesteuerte Unterrichtsformate, wird darüber hinaus die zunehmende praktische Relevanz von SRL deutlich. Die Fähigkeit, Lernprozesse effektiv zu regulieren, ist eine Schlüsselfähigkeit für Lernende, um die oben genannten Herausforderungen zu bewältigen. Typischerweise wird SRL als die Regulation und Kontrolle von kognitiven, metakognitiven, motivationalen sowie affektiven Facetten des Lernens zur Erreichung von Lernzielen definiert. Basierend auf dieser breiten Definition haben eine Vielzahl von Forschungsvorhaben SRL aus verschiedenen theoretischen Hintergründen und Perspektiven untersucht. Dabei wurde gezeigt, dass SRL ein zentraler Erfolgsfaktor zur Erreichung von Lernerfolgen in allen Phasen und Bereichen der Bildung eines Individuums ist. Die Vielfalt der Ansätze zur Untersuchung von SRL hat jedoch auch zu Unklarheiten darüber geführt, was SRL ist und wie es am effektivsten gefördert werden kann. Diese Problematik wird noch deutlicher, wenn SRL im Kontext anderer, allgemeinerer Forschungstraditionen zur Selbstregulation (SR) untersucht wird. Die vorliegende Dissertation befasst sich mit dieser Problemstellung. Zu Erreichung dieses Ziels wurden vier Forschungsbereiche zu verschiedenen Aspekten der SR identifiziert und integriert. Diese umfassen Lernaktivitäten (z.B. kognitive und metakognitive Strategien), treibende Kräfte (z.B. Motivation und Affekt), persönliche Dispositionen (z.B. Persönlichkeit) und begrenzte Ressourcen (z.B. Arbeitsgedächtnis und exekutive Funktionen). Auf der Grundlage von starker empirischer Evidenz, die jeden dieser Bereiche eng mit Lernen und akademischen Leistungen verknüpft hat, wurde so ein integratives Rahmenmodell entwickelt, das SRL als Teil von SR in der Bildungskontexten betrachtet. Um dieses Modell empirisch zu testen, wurde in der vorliegenden Dissertation der Vorhersagewert von zentralen, repräsentativen Konstrukten für jeden der Bereiche des Rahmenmodells in verschiedenen Kontexten (z.B. Lernen in der Schule und Lernaufgaben im Labor) getestet. Durch diesen Ansatz ist diese Dissertation die erste Studie, die die oben genannten Forschungstraditionen zur Selbstregulation im Bildungsbereich empirisch integriert. Studie I hatte zum Ziel, die besten Prädiktoren für das Lernen in der Schule und für Laborlernaufgaben aus einem umfassenden Satz von selbstregulatorischen Konstrukten zu identifizieren, die die vier im Rahmenmodel postulierten Forschungsbereiche zur Selbstregulation widerspiegeln (Lernaktivitäten, treibende Kräfte, persönliche Dispositionen und begrenzte Ressourcen). Konkret wurden robuste Modelle des maschinellen Lernens verwendet, um die Leistung in der Schule und in Laborlernaufgaben in fünf akademischen Domänen (Mathematik, Physik, Biologie, Kunst und Geschichte) vorherzusagen. Die Ergebnisse zeigten, dass Prädiktoren aus allen Bereichen des vorgeschlagenen Frameworks erforderlich sind, um das Lernen in beiden Settings optimal vorherzusagen. Allerdings unterschieden sich die spezifischen Variablen, die das Lernen in Schul- und Laborlernaufgaben optimal vorhersagten. Während Maße für treibende Kräfte (z.B. Motivation) und begrenzte Ressourcen (z.B. Arbeitsgedächtniskapazität) das Lernen in beiden Settings vorhersagten, zeigten Prädiktoren, die Lernaktivitäten (z.B. Anstrengungs- vs. Wiederholungsstrategien) und Persönlichkeit (z.B. Offenheit) repräsentieren, nur für eines der Lernmaße einen prädiktiven Wert. Studie II untersuchte, ob und wie sich die Anforderungen an die Selbstregulation bei einer computergestützten Lernaufgabe in Abhängigkeit von der Art der Interaktion der Teilnehmer mit der Lernumgebung unterscheiden. Im Detail nutzten die Teilnehmer entweder mausbasierte oder touchbasierte Interaktion, um mit den Lernmaterialien zu arbeiten. Robuste Modelle des maschinellen Lernens, wurden angewandt, um Lernergebnisse in beiden Bedingungen vorherzusagen. Dazu wurden, ähnlich wie in Studie I, Maße verwenden, die die vier Kernbereiche des vorgeschlagenen Rahmenmodells repräsentieren. Ergebnisse zeigten, dass die Selbstregulationserfordernisse beim Lernen mit Tablets höher waren. Insbesondere wurde das Lernen am Tablet über den Vorhersagewert des Vorwissens hinaus durch kritische Bewertung (Lernaktivität), motivationale Kosten (treibende Kraft), Offenheit (persönliche Disposition) und Task Switching (begrenzte Ressource) am besten vorhergesagt. Leistungsunterschiede bei mausbasierten Interaktionen hingen dagegen nur mit Kontrollmaßen (Leseverständnis und Vorwissen), nicht aber mit selbstregulatorischen Konstrukten zusammen. Studie III erweiterte das Vorgehen der ersten beiden Studien um eine detaillierte, prozessorientierte Untersuchung eines Schlüsselbereichs des vorgeschlagenen Rahmenmodells. In dieser Studie wurde das emotionale Erleben von Lernen (treibende Kraft) und dessen zeitliche Entfaltung während einer Lernaktivität mit Lernen in Beziehung gesetzt. Ergebnisse zeigten, dass eine Gruppe von Lernenden mit primär negativen emotionalen Erfahrungen am wenigsten lernte. Darüber hinaus zeigten diese Lernenden eine Zunahme negativer Emotionalität während des Lernens, die prädiktiv für geringere Lernerfolge war. Zuletzt zeigten weiterführende Analysen, dass diese emotionalen Prozesse möglicherweise von stabilen persönlichen Dispositionen (Trait Emotionsregulation und Neurotizismus) verursacht werden. Über alle Studien hinweg hat die vorliegende Dissertation gezeigt, das SR eine zugrundelende Struktur hat, die unabhängig von Kontext ist. Die spezifischen selbstregulatorischen Prozesse, die nötig sind, um optimale Lernergebnisse zu erzielen variieren jedoch nach Rahmenbedingungen (z.B. der Lernaufgabe und - umgebung). Durch diese Studien demonstriert diese Dissertation einen theoretisch abgeleitetes und empirisch gestütztes Rahmenmodell, welches selbstreguliertes Lernen in den größeren Kontext der Selbstregulation in Bildungskontexten setzt. Weitere Schritte für zukünftige Forschungsvorhaben zur Integration von Selbstregulation in Bildungskontexten werden im Kontext des vorgeschlagenen Rahmenmodells hergeleitet und diskutiert

The understanding of the agriculturally shaped environment - from the theoretical construct to an applied indicator of sustainable development

Sustainable development of the resource ‘land’ is increasingly being discussed with a focus on rural areas. Understanding is key to solving land use conflicts. It can lead to joint solutions and can thus enable sustainable development at a practical level. It goes beyond environmental consciousness, which is related to general issues, and instead aims to address concrete challenges in the context of sustainable development at an applicable level. ‘Understanding’ with regard to land use conflicts has not yet been defined in the literature. Based on this motivation, it is the aim of this study to create the construct of ‘understanding’ conceptually, to validate it empirically with structural equation modelling, and to demonstrate that understanding might be an important prerequisite for sustainable development. In this case, the focus is not on a general kind of understanding, but rather on specific aspects of understanding in relation to the agriculturally shaped environment in rural areas. The empirical data for the paper were collected by means of a large-scale population survey in Western Pomerania, Germany, a rural peripheral region characterized by typical land-use conflicts in predominantly rural areas. A tripartite division of the construct into cognitive, emotional, and opinion levels was derived theoretically. The data collected showed that this construct is supported empirically and that it can be applied as an SDG indicator. Thus, the refined construct of understanding the agriculturally shaped environment can make a substantial contribution towards closing the knowledge/attitude-behavior gap

Adaptation of an IOR task for mobile touch devices to classify MCI and AD, and to investigate impairments in IOR

Adaptation of an IOR task to tablets for classification of MCI and AD, and to investigate impairments in IO

Measuring Cognitive Load Using In-Game Metrics of a Serious Simulation Game

Serious games have become an important tool to train individuals in a range of different skills. Importantly, serious games or gamified scenarios allow for simulating realistic time-critical situations to train and also assess individual performance. In this context, determining the user’s cognitive load during (game-based) training seems crucial for predicting performance and potential adaptation of the training environment to improve training effectiveness. Therefore, it is important to identify in-game metrics sensitive to users’ cognitive load. According to Barrouillets’ time-based resource-sharing model, particularly relevant for measuring cognitive load in time-critical situations, cognitive load does not depend solely on the complexity of actions but also on temporal aspects of a given task. In this study, we applied this idea to the context of a serious game by proposing in-game metrics for workload prediction that reflect a relation between the time during which participants’ attention is captured and the total time available for the task at hand. We used an emergency simulation serious game requiring management of time-critical situations. Forty-seven participants completed the emergency simulation and rated their workload using the NASA-TLX questionnaire. Results indicated that the proposed in-game metrics yielded significant associations both with subjective workload measures as well as with gaming performance. Moreover, we observed that a prediction model based solely on data from the first minutes of the gameplay predicted overall gaming performance with a classification accuracy significantly above chance level and not significantly different from a model based on subjective workload ratings. These results imply that in-game metrics may qualify for a real-time adaptation of a game-based learning environment

Modellierung von Handlungswissen aus fragmentiertem und heterogenem Rohdatenmaterial durch inkrementelle Verfeinerung in einem Regelbanksystem

(VLID)274823

Measuring cognitive load using in-game metrics of a serious simulation game

Serious games have become an important tool to train individuals in a range of different skills. Importantly, serious games or gamified scenarios allow for simulating realistic time-critical situations to train and also assess individual performance. In this context, determining the user’s cognitive load during (game-based) training seems crucial for predicting performance and potential adaptation of the training environment to improve training effectiveness. Therefore, it is important to identify in-game metrics sensitive to users’ cognitive load. According to Barrouillets’ time-based resource-sharing model, particularly relevant for measuring cognitive load in time-critical situations, cognitive load does not depend solely on the complexity of actions but also on temporal aspects of a given task. In this study, we applied this idea to the context of a serious game by proposing in-game metrics for workload prediction that reflect a relation between the time during which participants’ attention is captured and the total time available for the task at hand. We used an emergency simulation serious game requiring management of time-critical situations. Forty-seven participants completed the emergency simulation and rated their workload using the NASA-TLX questionnaire. Results indicated that the proposed in-game metrics yielded significant associations both with subjective workload measures as well as with gaming performance. Moreover, we observed that a prediction model based solely on data from the first minutes of the gameplay predicted overall gaming performance with a classification accuracy significantly above chance level and not significantly different from a model based on subjective workload ratings. These results imply that in-game metrics may qualify for a real-time adaptation of a game-based learning environment

Taking Another Look at Intelligence and Personality: An Eye-Tracking Approach

Intelligence and personality are both key drivers of learning. This study extends prior research on intelligence and personality by adopting a behavioral-process-related eye-tracking approach. We tested 182 adults on fluid intelligence and the Big Five personality traits. Eye-tracking information (gaze patterns) was recorded while participants completed the intelligence test. Machine learning models showed that personality explained 3.18% of the variance in intelligence test scores, with Openness and, surprisingly, Agreeableness most meaningfully contributing to the prediction. Facet-level measures of personality explained a larger amount of variance (7.67%) in intelligence test scores than the trait-level measures, with the largest coefficients obtained for Ideas and Values (Openness) and Compliance and Trust (Agreeableness). Gaze patterns explained a substantial amount of variance in intelligence test performance (35.91%). Gaze patterns were unrelated to the Big Five personality traits, but some of the facets (especially Self-Consciousness from Neuroticism and Assertiveness from Extraversion) were related to gaze. Gaze patterns reflected the test-solving strategies described in the literature (constructive matching, response elimination) to some extent. A combined feature vector consisting of gaze-based predictions and personality traits explained 37.50% of the variance in intelligence test performance, with significant unique contributions from both personality and gaze patterns. A model that included personality facets and gaze explained 38.02% of the variance in intelligence test performance. Although behavioral data thus clearly outperformed “traditional” psychological measures (Big Five personality) in predicting intelligence test performance, our results also underscore the independent contributions of personality and gaze patterns in predicting intelligence test performance