Examining Increasing Playback Speed in Recorded Lectures on Memory, Attention, and Experience
Bibliographic record
Abstract
Recorded lectures represent a popular means of delivering educational content. These lectures afford increasing the playback speed which could be used to reduce time demands and increase the likelihood that a lecture is consumed. In two experiments (N = 320), we examined the impact of increasing the playback speed of lectures across a range of speeds on memory for the lecture material, mind wandering, and the learner’s experience of the lecture. For speeds up until 2x, findings revealed no significant differences in memory for the material, mind wandering, and learner’s subjective experience of the lecture, with the exception that “enjoyment of speed” decreased as speed increased. Beyond a speed of 2x, however, significant impairments in memory for the lecture material and decreases in liking toward both the video lecture and the speed were observed. Moreover, the increase in mind wandering with time on task often observed in recorded lectures was not modulated by lecture playback speed. These results reinforce extant results in the literature on the effects of increasing playback speed on memory for lecture material and add new insights in terms of this strategy’s influence on mind wandering and learner’s subjective experience of the lecture.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".