Student usage of higher production value multi-camera lecture recordings in a first-year engineering chemistry class
Bibliographic record
Abstract
This study explored student preference andusage patterns with multi-camera (5 cameras providing 5unique angles) recordings of in-person lectures comparedto standard single camera recordings employing a staticwide shot of all chalkboards. The participants were firstyearundergraduate engineering students at the Universityof Toronto. Hard copy anonymous survey responses andanalytical data collected from unlisted YouTube videoswere used to gather feedback and data. To assesspreference for multi- vs. single camera production, 6lectures were uploaded simultaneously in both productionformats. Data collected from YouTube Analytics showed ahigher preference for multi- vs. single camera production,as the multi-camera versions had on average 4.1 times thenumber of views compared to the single-camera videos.When compared to the multi-camera productions, thesingle-camera videos had a 34% greater average decreasein audience retention (from the first 30 seconds to the last30 seconds). From student survey responses, 64% ofstudents felt that the multi-camera recordings were moreengaging compared to the single camera production. Thefindings of this study demonstrate that students perceivevideos of high-production multi-camera as being valuableand more engaging to watch compared to the standardwide shot single camera lecture captures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".