ON THE RECORD: STUDENT MOTIVATIONS FOR RECORDING LECTURES AND IMPLICATIONS FOR LEARNING
Bibliographic record
Abstract
Prior to the pandemic, a second-year engineering course was delivered using a blended synchronous format. Students were surveyed on many aspects of their experience with this format including their use of recorded lectures. Participants reported both recording and watching behaviour: 30% of students watched recorded lectures with students watching or recording at least half of the lectures throughout the semester. From the results, recording of the lectures offers an increase in the final grade of, on average, 9.5% (p=0.0071) for both lowattending and high attending students. While attending most synchronous lectures tends to yield overall better performance (on average, 14.4%, p=0.0001), low attending students can overcome part of that gap by reviewing recorded lectures. Motivations for recording were associated with scheduling conflicts that prevented participants from attending the live lecture and participants wanting to review the material afterwards. Generally, students chose not to record the lectures because of a perceived barrier to doing so or a perception that their existing lecture notes were sufficient. Post pandemic, it may be beneficial to incorporatelecture recording with face-to-face lectures to allow students the additional benefit of reviewing lecture material and increasing student access to lecture content.
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 | 0.007 | 0.053 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".