Is More Activity Always Better? A Department‐Wide Study of Relationships Between Classroom Practices and Student Performance
Bibliographic record
Abstract
Extensive research into science education has established that active learning practices positively impact student learning1. From these findings, we can now move forward to explore how to best design active classroom practices, and assess the most effective use of instructional time. To address these issues on a large scale within biology, we investigated the relationships between specific classroom practices and student learning in a biology department‐wide study at a large research university in the Pacific Northwest. For 31 different biology course sections ranging from freshman to senior year, and on topics from molecular biology and biochemistry to ecology and physiology, we collected classroom activity and diagnostic test data. Classroom observations captured the student and instructor behavior using the validated tool COPUS2; the diagnostic assessments were multiple‐choice pre‐ and post‐tests aligned to each course's content, consisting largely of validated questions from the concept inventory literature. Our observational data analysis showed a wide variety of instructor practices across the department, consistent with other large‐scale characterizations, which allow for clustering this multidimensional classroom data into broader instructional styles3,4. Notably, students in “Collaborative” classrooms significantly outperform students in “Peer Instruction” and “Lecture” styles, though there is no difference between the performance in the two latter styles. More finely‐grained, the amount of class time spent on clicker questions or worksheets are each significant and positive predictors of student performance. Interestingly, we see a negative relationship between unstructured group discussions (using neither clicker nor worksheets) and performance, highlighting the need for targeted approaches to engage students in productive active learning. Thus, building on current research that describes large‐scale teaching practices, we can now quantitatively link program‐wide COPUS observational data with student outcomes. The results of this work describe a platform by which others might evaluate learning and classroom practices, inform future directions for instructor development, and provide further insight into the types of classroom activities that have the greatest impact on student learning.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".