Knowing me, knowing you Interdisciplinary approaches to metacognition, social cognition and motivation in higher education

Metacognition, the ability to think about and regulate one’s own cognitive processes has been found to play an important role in academic achievement. The concept has been studied in both cognitive neuroscience and educational sciences research, under different paradigms. The aim of this dissertation is to investigate metacognition in higher education by combining insights, theories and methodologies from both disciplines. In particular, the following questions are investigated: How does metacognition research in neuroscience and educational sciences relate to one another? Given the role of motivation in self-regulated learning theory, how can learning analytics dashboards be designed to motivate students in a way that fosters self-regulated learning and academic success?
In chapter 1 the literature on metacognition in cognitive neuroscience and educational sciences is researched. The chapter focuses on the theoretical and methodological underpinnings of metacognition in both disciplines, main findings and state of the art in metacognitive training research. The review lays down a conceptual mapping of metacognition in the two fields and identifies a common ground to facilitate interdisciplinary research, as well as underexplored dimensions of metacognition that could beneficiate from it.
Chapter 2 investigates neural correlates of metacognition in academic learning, as well as the relationship between metacognition in academic learning and domain-specific measures of metacognitive judgements derived from lab tasks. Machine learning techniques alongside measures of grey matter volume in the brain were used for this research. The results indicate some overlap between metacognitive abilities in tasks and academic learning.
Chapters 3 and 4 investigate social comparison as an integral component of learning analytics dashboards to foster motivation in students and support metacognitive, self-regulated learning. In chapter 3, the effects ‘ideal cases’ of social comparison using assignment grades are researched in two experiments. The results indicate positive effects on extrinsic motivation and academic achievement. However, no effects on self-regulated learning were observed.
The dashboard in chapter 4 expands the comparisons available to learning activities, as well as grades. Social comparison with peers is implemented without selecting for desirable comparisons. Instead, automatic textual feedback is generated based on the situation to emphasise comparisons with peers that were hypothesised to be more beneficial and make potentially negative ones less salient. Overall, results suggests that the dashboard intervention promoted learners’ motivation and metacognition and led to more academic success.
Taken together, the studies in this dissertation provide foundations for interdisciplinary research on metacognition, learning, social cognition and motivation. Furthermore, they contribute to a better understanding of the neural basis of metacognition in academic learning and provides insights on how learning analytics dashboards can help students self-regulate their learning by motivating them.