Pilot Study on the Relationship of Test Anxiety to Utilizing Self-Testing in Self-Regulated Learning
Bibliographic record
Abstract
Whether or not test-anxious students leverage the power of testing as potent learning tool is unclear. In a pilot study we investigated the relation of test anxiety to the utilization of testing activities and academic performance in self-regulated learning. We hypothesized that increased cognitive test anxiety would relate to less self-reported use of self-testing in favor of repetition strategies, and thus relate in turn to lower self-reported exam grades. To examine this idea, we created a scale contrasting self-testing and repetition strategies, which showed sufficient preliminary reliability and validity. The findings support our notion with respect to the cognitive interference component of test anxiety: More interference was associated with less self-testing, and the link of interference with exam grades was fully mediated by the reported degree of self-testing. Although our findings are preliminary and limited in generalizability due to small sample size and lack of factor analysis of the created scale, the results hint at one potential reason why test-anxious students may underperform. Consequently, educators might motivate their test-anxious students to rely more on effective study approaches.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".