Small changes, big gains: A curriculum-wide study of teaching practices and student learning in undergraduate biology
Bibliographic record
Abstract
A growing body of evidence has shown that active learning has a considerable advantage over traditional lecture for student learning in undergraduate STEM classes, but there have been few large-scale studies to identify the specific types of activities that have the greatest impact on learning. We therefore undertook a large-scale, curriculum-wide study to investigate the effects of time spent on a variety of classroom activities on learning gains. We quantified classroom practices and related these to student learning, assessed using diagnostic tests written by over 3700 students, across 31 undergraduate biology classes at a research-intensive university in the Pacific Northwest. The most significant positive predictor of learning gains was the use of group work, supporting the findings of previous studies. Strikingly, we found that the addition of worksheets as an active learning tool for in-class group activities had the strongest impact on diagnostic test scores. This particular low-tech activity promotes student collaboration, develops problem solving skills, and can be used to inform the instructor about what students are struggling with, thus providing opportunities for valuable and timely feedback. Overall, our results indicate that group activities with low barriers to entry, such as worksheets, can result in significant learning gains in undergraduate science.
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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 | 0.004 | 0.020 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".