Metacognitive Strategies and Test Performance: An Experience Sampling Analysis of Students' Learning Behavior
Bibliographic record
Abstract
The aim of the present study was to explore students’ learning-related cognitions prior to an in-class achievement test, with a focus on metacognitive strategy use. A sample of 70 students in grade 11 (58.6% female, M age = 17.09 years) completed a series of structured, state-based measures over a two-week period via the experience sampling method until the day before a class test. Results illustrated students’ self-regulatory ability to preserve their motivational and cognitive resources, with test-related cognitions evidenced significantly more often in learning-related as opposed leisure settings. Metacognitive strategy use was also found to significantly increase as the test date approached underscoring the goal-oriented nature of situated learning behaviors. Higher intercepts and increases in frequency of test-related cognitions over time positively corresponded to test performance. Of the three metacognitive strategies assessed, monitoring was found to positively correspond with test performance. Implications for future practice as well as implications for future research employing the experience sampling method are discussed.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".