Characterization of Catch-Up Behavior: Accession of Lecture Capture Videos Following Student Absenteeism
Bibliographic record
Abstract
The use of lecture capture in higher education is becoming increasingly widespread, with many instructors now providing digital videos of lecture content that can be used by students as learning resources in a variety of ways, including to catch up on material after a class absence. Despite accumulating research regarding the relationship between lecture capture and attendance, the nature of catch-up behavior following an absence has not been well characterized. This study measured attendance in relation to lecture video accesses to determine whether students catch up after missing a class, and if so, within what timeframe. Overall, it was found that 48% of absences were not associated with a corresponding lecture video access, and that when absences were caught up, the length of time taken to access the video was highly variable, with the time to the next exam being the likely determinant of when the video was viewed. Time taken to access a video was directly associated with deep learning approach score (as measured by the R-SPQ-2F). Males took significantly longer to view a corresponding lecture video after an absence than females, and missed significantly more classes than females. This study confirms that students use lecture capture variably, and that characteristics such as gender and learning approach influence lecture capture behavior including catch-up following an absence, a finding that is not unexpected given the diversity of students in higher education.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".