Detection of Goal Setting and Planning in Self-regulated Learning Using Machine Learning and Think-aloud Protocols
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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