How Learners Engage with In-Context Retrieval Exercises in Online Informational Videos
Bibliographic record
Abstract
Learners increasingly refer to online videos for learning new technical concepts, but often overlook or forget key details. We investigated how retrieval practice, a learning strategy commonly used in education, could be designed to reinforce key concepts in online videos. We began with a formative study to understand users' perceptions of cued and free-recall retrieval techniques. We next designed a new in-context flashcard-based technique that provides expert-curated retrieval exercises in context of a video's playback. We evaluated this technique with 14 learners and investigated how learners engage with flashcards that are prompted automatically at predefined intervals or flashcards that appear on-demand. Our results overall showed that learners perceived automatically prompted flashcards to be less effortful and made the learners feel more confident about grasping key concepts in the video. However, learners found that on-demand flashcards gave them more control over their learning and allowed them to personalize their review of content. We discuss the implications of these findings for designing hybrid automatic and on-demand in-context retrieval exercises for online videos.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".