Detection of Goal Setting and Planning in Self-regulated Learning Using Machine Learning and Think-aloud Protocols
Bibliographic record
Abstract
In this study, we used machine learning models to detect the goal setting and planning activities in self-regulated learning (SRL) based on the linguistic features of think-aloud transcripts. Specifically, we trained six types of machine learning models (i.e., decision tree, Gradient boosted decision tree, random forest, logistic regression, support vector machine, and neural network) on 2,792 think-aloud segments of medical students, who were asked to think out loud as they diagnosed virtual patients in a computer-simulated environment. The results suggested that machine learning models, especially Gradient boosted decision tree and neural network, could make accurate predictions. This study shows the possibility of using machine learning to free researchers from the labor-intensive work of coding think-aloud transcripts. This study also informs practitioners about automatically detecting students' SRL activities in real-time as they think aloud in learning, making the provision of timely feedback possible.
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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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.001 |
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".