The Use of Lecture Recordings as Study Aids in a Professional Degree Program
Bibliographic record
Abstract
Lecture recording is now common in many educational institutions, leading to discussion about how best to support student learning. In this mixed methods study, we used a survey ( n = 46 participants), think-aloud methodologies in observed study sessions ( n = 8 participants) and recording analytics ( n = 637 recordings) to characterize how veterinary students utilize recordings during their studies. Only 48% of survey respondents considered they were more likely to use recordings as exams approached, but 78% considered they used recordings more when the topic was difficult. In the observed study sessions, students characterized their use of recordings as helping them to control their learning environment, allowing them to pause and rewind challenging topics, and as a jumping off point for future study, allowing them to structure the seeking out of additional information. In a linear model describing the recording analytics, students who had entered higher education directly from high school were more likely to watch more of a lecture than graduate entry students. In addition, the most visited lectures were also the ones with more view time ( F(5, 631) = 129.5, R2 = 0.50, p < .001). Overall, this study suggests that veterinary students were selective about their use of recordings in their study strategies, often using them to make up for deficits in their knowledge and understanding, or to supplement their experience at veterinary school. We discuss the consequences and implications for student study skills support.
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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".