Effects of solving contextualized physics problems among men and women: A psychophysiological approach
Bibliographic record
Abstract
INTRODUCTION: This study investigates the psychophysiological effects of contextualized physics problems during the problem's solving stage, expanding upon earlier findings (Bouhdana et al., 2023) on the problem's reading stage. METHODS: Participants (university students, both men and women) solved problems presented in three contexts - no context, technical, and humanistic - while measures of cognitive (pupillometry, EEG) and affective (electrodermal activity, valence) situational interest were collected. RESULTS: Key findings revealed that context significantly influenced pupillometry (p = 0.035) and the context*gender interaction significantly affected valence (p = 0.037), though post-hoc comparisons were not significant. Men exhibited higher emotional valence when solving decontextualized problems compared to humanistic problems, aligning with trends observed during the reading stage, though significance was not maintained in the solving stage (p = 0.096). Notably, cognitive situational interest, as indicated by pupillometry, increased during the solving stage, suggesting a shift from affective to cognitive engagement. However, cognitive situational interest during the solving stage did not predict accuracy, contrasting with our prior findings where affective situational interest (electrodermal activity) during reading significantly predicted accuracy. DISCUSSION: This shift may stem from the higher cognitive load associated with problem-solving tasks. These findings suggest that cognitive situational interest during the solving stage is a reflection of cognitive load rather than a predictor of success, while underscoring the importance of fostering affective situational interest during the reading stage to enhance performance and engagement. Future research should explore longitudinal effects, cultural influences on situational interest, and inclusive curriculum design. CONCLUSION: Our results highlight the need for balanced and context-sensitive approaches to curriculum development to optimize student motivation, performance, and long-term interest in physics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".