The role of discrepancy between self-reported and response time-based questionnaire-taking efforts on Canadian students’ PISA 2022 test achievements
Bibliographic record
Abstract
The increasing use of low-stakes international assessments highlights the importance of test-taking efforts. Previous studies have used self-reported and response time-based measures to examine this effort. Although differences between these measures have been suggested, their association with performances and potential gender gaps remains underexplored. This study addresses this gap by using PISA 2022 data from 20,560 Canadian students (50% female) with polynomial regression and response surface analysis. Findings revealed that the congruence and discrepancy between self-reported effort (SRE) and response behaviour effort (RBE) significantly affect test achievements. Students showing high levels in both efforts achieved the highest scores, with congruence outperforming discrepancy. When discrepancies exist, students with higher RBE outperformed those with higher SRE. Gender differences emerged, with RBE having a significantly stronger impact on males’ math and SRE having a slightly greater effect on females’ reading. These findings offer new insights into effort and gender gaps in educational assessments.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".