Impact of Student Generated Exam Aids on Academic Performance, Stress and Learning Approach
Bibliographic record
Abstract
The permitted use of exam aids has the capacity to decrease examination stress and positively impact student learning and academic performance. The purpose of this study was to develop an exam aid assessment rubric to evaluate the inclusion of critical organization and course content integrative elements included on single page student-generated exam aids. The exam aids were used as both a learning and examination preparation tool that were also used as a supportive aid during the final exam of a fourth-year nutrition and disease pathophysiology course. Student-generated exam aids (n=167) were assessed according to two rubrics for exam aid assessment scores, namely the exam aid general score and exam aid content integration score developed by the research team. Subsequently, the relationship between students’ exam aid assessment scores and academic performance (e.g., final exam grade, final grade in the course), learning approach (namely deep and surface approaches, motives, and strategies) and perceived stress scale (PSS) scores were determined using online survey data collected at the end of the academic semester prior to the final exam. Higher exam aid assessment scores were positively correlated with higher academic performance outcomes, including both final exam grade and overall final grade in the course (PPPP<0.05). This demonstrates the negative impact of stress on academic performance and engagement in examination preparation activities and learning approach. Collectively, this study demonstrates the positive impact of student-generated exam aids but highlights that their potential benefits to support student learning and academic performance is limited in students still experiencing higher stress levels and/or surface learning 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 | 0.002 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".