How can we make a large class feel like a small one?
Bibliographic record
Abstract
Large classes have become standard at North American universities. Lecture halls with capacities for several hundred students were built to accommodate Ontario’s double-cohort, yet enrollments did not decline after they graduated. Administrators tout the cost-saving efficiencies of large classes however instructors find it challenging to engage students in higher order learning. In this study our main goal is to answer the question “Can we make a large class feel like a small one?” To answer this, we surveyed students and faculty in the College of Biological Science at the University of Guelph and performed a principal component analysis (PCA) on data about course structure and student outcomes. The survey showed that students and faculty agree that small classes generally have fewer than 40 students, and that the high end of a medium-sized class is approximately 200 students. There was disagreement, however on the maximum size of a large class, with instructors reporting higher overall class sizes compared to students (1000 versus 600 students). Results of our preliminary principal components analysis distinguished large classes from small classes by higher failure rates, apparent lower instructor availability, and a decreased number and variety of assessments. A few courses did not fit this classification, indicating that there are large classes that may mimic small classes. In a period of continually decreasing resources, including time, determining what can make a large class feel like a small class will give the instructor the best tools to make a difference in the large classroom.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".