Examining the Interplay of Emotion, Cognition, and Self-regulated Learning with Two Computer-mediated Learning Environments
Bibliographic record
Abstract
This dissertation examines the real-time interactions of emotion and cognition across three stages of scientific problem-solving and identifies the intrapersonal and contextual factors contributing to students' optimal problem-solving pathways in two computer-mediated learning environments, referred to as Crystal Island and PeppeR. Analyses of multiple data sources (i.e., facial behaviour data, computer logfile, and survey data) not only showed that the pattern change of student affective states over time is the function of the interplay of emotion and task characteristics but also emphasized the importance of self-regulatory learning behaviour in mitigating a range of emotional intensity, regardless of the content and/or the cognitive demands of the learning tasks. These findings are highly valuable as they offer insights into student individual characteristics and learning behaviour in complex learning processes, which are crucial parameters for modelling students’ learning and engagement outcomes in technology-rich learning environments. Therefore, one implication of this study is to inform the design of computer-based learning environments that may benefit from individualized attention to students’ affective-and-cognitive states and optimize engagement in learning with computer-mediated learning environments. This research is premised on the notion that understanding the synchronic nature of emotional, cognitive, and self-regulatory elements of complex learning is critical to promote an optimized learning experience. Previous and emerging research, for example, has revealed the predictive value of emotion in learning and achievement, but research on emotion and learning continue to be under-researched. The study of emotion and learning with advanced learning technology is particularly sparse due to the dynamic, rapid-changing, and multifaceted nature of emotional processes and the evolving complexity of learners’ interaction with the learning technology. The dissertation aims to address this knowledge gap and examine the dynamic nature of emotion across multiple task contexts and attempts to identify the critical emotional, motivational, and behavioural determinants of student optimal learning trajectories.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".