Bibliographic record
Abstract
Lecture capture (LC)-a recording of the live lecture provided as a supplementary resource-is accepted as a standard provision in UK higher education. Previous research has shown it to be very popular with students, although there have been conflicting findings in terms of its impact on attendance and attainment, and suggestions that student engagement with this resource varies depending on their own preferences and approaches. The aim of the present study was to determine the impact of LC on students in a wider sense, encompassing pedagogic and pastoral aspects of student development. This mixed-methods study analyzed focus group and questionnaire data from first- and second-year veterinary students at one UK university. Results demonstrated the student belief that LC is important for learning and well-being but highlighted the facilitation of passive and surface learning that this resource offers. More worryingly, this study identified a group of students for whom this resource may be particularly unhelpful. This group, relied excessively upon LC for learning, felt overwhelmed by their workload despite working fewer hours, and subsequently achieved poorer exam results. A key theme in this negative relationship appeared to be low self-efficacy. The findings enable educators to consider how resources are provided and to encourage implementing mechanisms to help students make better choices, and take control of their learning.
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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".