Student perceptions and reported use of video recorded lectures in engineering courses
Bibliographic record
Abstract
Between 2008 and 2010 an introductory circuit analysis course for second year engineering students had its lectures recorded (2008 was audio only, other years were by video) and the recordings were made available to registered students as a supplemental resource. Attendance to lectures was still required. In 2011 an introductory programming course was recorded in a similar way. In each of these offerings the students were anonymously surveyed at the end of the course using an online survey tool with most questions using a five point Likert category scale. The survey looked at the perceived usefulness of the recordings, the approach to watching and the impact on attendance. The responses showed strong support to having video lecture capture and the reported use of the videos was to watch selected material. There was a difference between the courses on the impact on attendance, with the circuit analysis course indicated little impact on attendance, whereas the responses from the other course indicates more missed lectures due to the availability of recordings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".