Does Size Really Matter? A Comparison of Class Size and Student Outcomes in Introductory Physiology Courses
Bibliographic record
Abstract
Traditionally, it's been thought that small class size resulted in better student outcomes. As teaching styles change and inquiry‐based learning becomes more common, this may not necessarily be the case. This study examined student outcomes for introductory physiology courses of different sizes at two different institutions over a three year period. One course averaged 30 students per year while the other course averaged 200 students per year. Both courses were taught by the same instructor, using the same teaching materials, lecture style, and inquiry‐based techniques. The same hands‐on lab exercises were taught and the same lab report questions were assigned. Tutorials involved teaching the same case studies (by the instructor at the small institution and by teaching assistants under the direction of the instructor at the larger institution). Each course used the same exam questions and consisted of multiple choice and short answer formats. With class size being the only significant variable, it was possible to test if class size was a key factor in student success. Students in both courses (and in all years) were given a pre‐test to assess the student knowledge base at the beginning of the course. In all cases, the pre‐test showed similar levels of starting knowledge between the different‐sized courses (31.1±1.8% for the smaller course vs. 30.6±0.9% for the larger course). Student outcomes were compared between the classes by assessing exam grades (overall scores as well as individual question scores), comparing lab report grades, and by comparing final course grades. Assessment of all these parameters showed no significant difference between the two courses and this result was seen in each year of the study. Lab report grades, averaged over 5 lab reports, differed by less than 2% between the large and small courses (not a significant difference). Mean exam scores and final grade scores were also not significantly different for any of the years of the study. Course grades between the courses differed by less than 3±0.8%. These results would indicate that class size alone was not a significant factor in determining student outcomes. Students showed similar success in both large and small classes when all other factors were similar. Interactive lectures, as well as the use of case studies and other inquiry‐based learning methods may have a much greater influence on student learning and success than class size.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".