Exploring counter-theoretical instances of graduate learners’ self-regulatory processes when using an online repository
Bibliographic record
Abstract
Academic self-regulation theories have proposed that learning involves a complex set of cognitive and metacognitive mechanisms that are enacted in phases. These phases include task understanding, strategy adoption, monitoring, and reflection. Whereas classical approaches to self-regulation contend that these phases work together to influence academic performance, the empirical research reported herein reveals that, for essay writing in an online learning environment, improved self-regulation is not necessarily associated with improved learning outcomes. We begin by reviewing frameworks for academic self-regulation, specifically in the context of learners’ experiences in online repositories equipped with Topic Maps (ISO 13250) indexes. We then offer explanations for counter-theoretical interactions found between task understanding (a frontline phase of self-regulation) and academic performance in 38 graduate learners who used Topic Maps to tackle ill-structured essay tasks. Our investigation sheds light not only on how learners’ perceptions of feedback facilitate task understanding, but also on the complex relationship between task understanding and monitoring proficiencies.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".