Investigating the Validity of Different Peer Groupings in the Assessment of English Writings
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
Peer assessment is an indispensable part in classroom assessment and it serves as a very useful way of promoting learning. However, different ways of peer grouping may influence the validity of peer assessment. This study analyzes the quality and quantity of feedback, adoption rate of feedback as well as scores of students’ original drafts and the revised versions. It finds that all ways of grouping can promote learning but the degree of validity varies among groups. Besides, accuracy and adoption rate are high in students’ feedback, which means peer feedback is effective to a great extent. Among all the ways of grouping, homogeneous grouping i.e. pairing students with the same or similar language proficiency level can archive more significant promotion in learning. In general, students hold a positive view towards the validity of peer assessment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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