Tutorial Attendance and Student Performance PACS: 01.40 Education 1. The Study
Bibliographic record
Abstract
Abstract: I present data on the correlation between student attendance at optional tutorials and performance as measured by the final grade in the course. Two courses were studied: a large course in Physics for the Life Sciences and a somewhat smaller liberal arts course in Physics without mathematics. For both courses, students who attended all or most tutorials received a mean final mark in the course just over a full letter grade higher than students who attended none or very few tutorials. I discuss the difficulties in untangling cause and effect in the correlation of these two factors. At the University of Toronto undergraduate courses typically have two or three hours of classes a week and a weekly one-hour tutorial; tutorials are known as recitation sections at some other institutions. Here I present data on the correlation between attendance at tutorials and the final mark that students received in their course. I studied two courses: 1. A full-year first year Physics course for students in the Life Sciences. 1 The course has nearly 1,000 students, most of whom are intending to apply to a professional faculty such as Medicine. The course is calculus based.
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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 | 0.016 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".