The Role of Epistemic Beliefs in Students’ Self-Regulated Learning With Computer-Based Learning Environments: Conceptual and Methodological Issues
Why this work is in the frame
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Bibliographic record
Abstract
Users benefit most from computer-based learning environments (CBLEs) when they are adept at self-regulated learning (SRL). Learner characteristics, such as epistemic beliefs, influence SRL processing. Therefore, research into learning with CBLEs must account for interactions between epistemic beliefs and SRL. In this article we integrate epistemic belief frameworks and models of SRL, and we argue that both phenomena should be modeled as a dynamic series of events. Such modeling allows for an examination of how various epistemic beliefs may be activated and deactivated through the process of self-regulation. We also show how CBLEs can be used to measure epistemic beliefs in novel ways and study how epistemic beliefs and SRL interact. Finally, we identify areas for future research and educational implications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it