How Do Peers Impact Learning? An Experimental Investigation of Peer-to-Peer Teaching and Ability Tracking
Bibliographic record
Abstract
Classroom peers are believed to influence learning by teaching each other, and the efficacy of this teaching likely depends on classroom composition in terms of peers' ability.Unfortunately, little is known about peer-to-peer teaching because it is never observed in field studies.Furthermore, identifying how peer-to-peer teaching is affected by ability tracking-grouping students of similar ability-is complicated by the fact that tracking is typically accompanied by changes in curriculum and the instructional behavior of teachers.To fill this gap, we conduct a laboratory experiment in which subjects learn to solve logic problems and examine both the importance of peer-to-peer teaching and the interaction between peer-to-peer teaching and ability tracking.While peer-to-peer teaching improves learning among low-ability subjects, the positive effects are substantially offset by tracking.Tracking reduces the frequency of peer-to-peer teaching, suggesting that low-ability subjects suffer from the absence of high-ability peers to teach them.
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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 | 0.006 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".