An Analysis of Peer-Submitted and Peer-Reviewed Answer Rationales, in an Asynchronous Peer Instruction Based Learning Environment.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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) peervotes earned is a significant predictors 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.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it