What Drives Student Engagement and Learning in Video Lectures? An Investigation of Instructor Visibility, Playback Speed, and Student Preferences
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
ABSTRACT COVID‐19 greatly increased the online delivery of higher education. But one limitation of online learning is that students often struggle to stay engaged while watching online lectures. We examined whether including an instructor's face in lecture videos (instructor visibility) enhances student engagement or learning. In two preregistered experiments, we found that instructor visibility in lecture videos did not affect either engagement or learning overall. However, participants reported higher engagement when they watched a video that aligned with their preference for instructor visibility. For example, participants who favored videos with the instructor visible reported greater engagement with such videos compared to those without the instructor, and vice versa. Additionally, we examined the effects of playback speed on engagement and learning. Our results suggest that speeded playing did not impact engagement but resulted in better learning efficiency. Lastly, using GPT, we explored participants' open‐ended responses to understand their preference for video lectures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it