Engineering Student Engagement in Large First-Year Lectures: A Joint Student-Faculty Study at a Large Canadian University
Bibliographic record
Abstract
In-person lectures are a common mode of course delivery at many schools. This study examines factors related to lecture attendance and student engagement within core first-year math, science, and engineering courses in engineering at a large, research-intensive Canadian university. Through an optional and anonymous survey created and distributed by students, a dataset of 229 responses was generated and subsequently co-analyzed by students and faculty. The survey revealed statistically significant relationships of lecture attendance and engagement with factors such as the specific course, motivations for attending (e.g., practice problems or team quizzes), lecture pacing, and the perceived quality of presentation materials. In particular, the motivations for attending had a substantial impact on student-reported engagement (with effect sizes ranging from 0.7 to 1.3). This project also represents an encouraging next step in student-faculty collaboration in the program review process.
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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 | 0.000 |
| 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 | 0.001 |
| 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".