Is Peer Feedback Helpful When Learning Literature Review Writing? A Study of Feedback Features and Quantity
Bibliographic record
Abstract
The literature review is an important part of an academic text. It is common for students to learn literature review writing through reading and practice. This study explored peer feedback as an assisting interactive social tool that aims to provide formative assessment of literature reviews written by master students. The student reviewers’ feedback characteristics were identified and coded, and their relationships with writing performance were examined. Six assessment criteria and writing categories relevant to the literature reviews genre were employed for the writing process as well as a peer feedback process. The feedback patterns were analysed according to four dimensions: describing status or problems versus prescribing directions, abundant input versus uncritical/empty comments, high versus low level, and constructive versus negative. No correlations were found between review patterns, students’ performance mark, and quantity of feedback received. Significant correlations were observed between specific review patterns and separate category scores. The dimensions constructive vs. negative and high vs. low level correlated with most category scores. The findings show that the students were able to provide useful and high-level comments to assist their peers’ writing. Overall, it was found that peer feedback quality and quantity do not define the performance mark, but benefit individual aspects of literature reviews writing.
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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 | 0.174 | 0.510 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".