Student satisfaction with use of an online peer feedback system
Bibliographic record
Abstract
We contribute to the growing evidence of the positive effect of use of online peer feedback tools on students’ teamwork skills development. We do so by exploring individual and contextual factors underlying satisfaction with using a peer feedback system alongside team projects. Employing path analytical framework and bootstrap methods, we analysed data from an international sample of 100 project teams in management studies. Drawing on procedural justice theory, we theorised and found support that students’ uncertainty avoidance orientation and virtuality in collaboration were positively related to their satisfaction with use of a peer feedback system. Such satisfaction in turn allowed them to be more effective team members. Our findings provide evidence for higher education institutions and instructors considering the adoption of online peer feedback systems alongside teamwork in their curricula. Specifically, peer feedback appears to be effective in the development of teamwork skills and students appreciate the opportunity to provide feedback to their peers in a structured and dedicated environment. Our findings are timely and of important practical significance as educational institutions increasingly rely on the use of computer-mediated technology during the COVID-19 pandemic.
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".