Remote Learning & Online Fatigue: Exploring the Live and Pre-Recorded Tutorials Impacts on Teaching Effectiveness and Students' Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The presented work aimed to investigate pre-recorded tutorials' impact on online fatigue and students' performance. An anonymous survey was conducted to explore students' note-taking habits, perceptions, and preferences for live or pre-recorded tutorials. Interestingly, results suggest an even split between preferences for live and pre-recorded tutorials and a diverse range of preferences and potential coping strategies for remote learning. Both formats offer benefits. By incorporating the valued aspects of each format, such as real-time question periods in live sessions and the ability to replay content in pre-recorded tutorials, educators can create a more engaging and effective learning environment. Furthermore, understanding students' note-taking habits and perceptions can help educators support students in developing strategies to maintain attention and engagement during remote learning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it