Time-driven modeling of student self-regulated learning in network-based tutors
Bibliographic record
Abstract
This study applies a time-driven approach to model self-regulated learning (SRL) on the basis of elapsed time metrics in the context of open-ended learning environments (OELEs), specifically, network-based tutors. In doing so, we examine how students allocated attentional resources to distinct phases of SRL as a measure of depth of information processing. Student teachers (N=68) were assigned to two different versions of nBrowser: a static version where the network did not converge on the basis of student interactions and a dynamic version where the network was continually updated by the system. Students designed a lesson plan and completed pre- and post-test self-report measures of knowledge gains. In both the experimental conditions, the results show four distinct SRL profiles that are relatively consistent and can be detected on the basis of behavioral patterns logged by the system across behaviors, namely, planning, requesting hints, studying examples, and monitoring. Although students who allocated more attentional resources to studying examples performed more poorly, their efforts to engage in planning, requesting hints, and monitoring were found to predict knowledge gains and design skills. Furthermore, students assigned to the dynamic version of the system outperformed those assigned to the static version in pedagogical knowledge gains.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".