Effective variations of peer instruction: The effects of peer discussions, committing to an answer, and reaching a consensus
Bibliographic record
Abstract
Peer Instruction (PI) is a widely used student-centered pedagogy, but one that is used differently by different instructors. While all PI instructors survey their students with conceptual questions, some do not allow students to discuss with peers. We studied the effect of peer discussion by polling three groups of students (N = 86) twice on the same set of nine conceptual questions. The three groups differed in the tasks assigned between the first and second poll: the first group discussed, the second reflected in silence, and the third was distracted so they could neither reflect nor discuss. Comparing score changes between the first and second poll, we find minimal increases in the distraction condition (3%), sizable increases in the reflection condition (10%), and significantly larger increases in the peer discussion condition (21%). We also examined the effect of committing to an answer before peer discussion and reaching a consensus afterward. We compared a lecture-based control section to three variations of PI that differed in their requirement to commit to an answer or reach consensus (N = 108). We find that all PI groups achieve greater conceptual learning and traditional problem solving than lecture-based instruction. We find one difference between these groups: the absence of consensus building is related to a significant decrease in expert views and beliefs. Our findings can therefore be used to make two recommendations: always use peer discussions and consider asking students to reach a consensus before re-polling.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".