Comparing physical analogue and traditional videos for learning and emotional engagement
Bibliographic record
Abstract
The lack of diversity in science, technology, engineering, and mathematics (STEM) fields is a vital issue that carries into the workforce. This study aims to enhance underrepresented students’ learning experience and emotional engagement by replacing mathematical abstractions with physical analogues. In the context of a post-secondary STEM course, we developed four video modules to compare the effects of physical analogues against a more traditional framing and assessed both the impact on learning and students’ emotional engagement with the material. Results suggest that the physical analogue videos significantly increased students’ emotional engagement, with more impact for women and non-native English speakers than for men and native speakers. Furthermore, students from other disciplines who were taking the course as part of their minor program found the physical analogue videos to be more helpful than the traditional videos in terms of understanding the course content. Students also suggested that compared to the traditional videos, the physical analogue versions helped them build mental models through visualization and analogies. However, we observed no statistical evidence that student performance on quizzes varied between the two treatments.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".