Media Player Tool Use, Satisfaction with Online Lectures and Examination Performance
Bibliographic record
Abstract
Media players allow students to pause lectures and to replay portions at will, two capabilities with potentially important pedagogical value that are not available in face-to-face lectures. The first study showed that many students use and value these media player features when watching online introductory psychology lectures. The second study showed that use of the features was correlated with superior exam performance, a learning outcome that was at least partially mediated by increased satisfaction with the learning approach. Together, the findings demonstrate that students who used the features made available by the media-players were more satisfied with the course, and performed better in it. Résumé Les lecteurs multimédias permettent aux étudiants d’interrompre les enregistrements de cours en salle et d’en rejouer des parties à volonté, deux fonctions ayant une valeur pédagogique potentiellement importante qui ne sont pas disponibles dans les cours en salle. La première étude montre que plusieurs étudiants d’un cours d’introduction à la psychologie utilisent et apprécient ces fonctions des lecteurs multimédias. La deuxième étude montre qu’il existe une corrélation entre l’utilisation de ces fonctions et une meilleure performance aux examens, un résultat de l’apprentissage qui serait partiellement expliqué par l’augmentation de la satisfaction par rapport à l’approche d’apprentissage. Ensemble, les résultats indiquent que les étudiants qui utilisent ces possibilités inhérentes aux lecteurs multimédias sont plus satisfaits de leur cours et obtiennent de meilleures notes.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".