ERIC ED560883: An Analysis of Peer-Submitted and Peer-Reviewed Answer Rationales, in an Asynchronous Peer Instruction Based Learning Environment
Bibliographic record
Abstract
This paper reports on an analyis of data from a novel "Peer Instruction" application, named DALITE. The Peer Instruction paradigm is well suited to take advantage of peer-input in web-based learning environments. DALITE implements an asynchronous instantiation of peer instruction: after submitting their answer to a multiple-choice question, students are asked to write a rationale for their choice. Then, they can compare their answer to other students' answers, and are asked to choose the best peer-submitted rationale among those displayed. We engaged in an analysis of student behaviour and learning outcomes in the DALITE learning environment. Specifically, we focus our investigation on the relationship between student proficiency, how students change their answers after reading each others' writings, and the peer-votes they earn in DALITE. Key results include: i) peer-votes earned is a significant predictor of success in the course; ii) there are no significant differences between strong and weak students in how often they switch from the correct answer to a wrong answer after consulting peer-rationales, or vice versa; iii) even though males outscore females in conceptual physics questions, females earn as many votes from their peers as males do for the content they produce when justifying their answer choices. [This work has been funded through the Programme d'Aide la Recherche sur l'Education et l'Apprentissage (PAREA), administered by the Ministere d'Education et Loisirs de Quebec.] [For complete proceedings, see ED560503.]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.227 | 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 teacher head, 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".