Cued Retrospective Reporting: Measuring Self-Regulated Learning
Bibliographic record
Abstract
Self-regulated learning (SRL) skills are especially important in professions where the half-life of initial training and education is short and where knowledge and skill obsolescence is quick. An example of just such a profession can be found in the constantly evolving field of Air Traffic Control (ATC), and thus SRL skills are essential for ATC trainees. If Air Traffic Controllers are to become and remain competent we must gain insight into how ATC trainees regulate their learning during task performance, how the acquisition of SRL skills can be stimulated, and thus how this can be measured. This study examines the use of cued retrospective reporting to measure learners’ SRL activities during the execution of complex ATC tasks. Results show that cued retrospective reporting is a workable method for measuring an extensive collection of regulation activities of ATC trainees. Further, relation between ATC task performances and SRL activities and other relating learner characteristics (i.e. Self-directed learning skills and the learners self-efficacy beliefs) were found.
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".