Supporting Creative Thinking Using Online Peer Assessment: Student Perceptions and Team Processes in Higher Education
Bibliographic record
Abstract
This study investigated postsecondary students’ team-level creative thinking using online peer assessment as a tool for electronic brainstorming. Student teams developed ideas for a public service announcement within the platform peerScholar. Following an explanatory sequential mixed method design, the researchers surveyed student perceptions of online peer assessment as a support for team-based creative thinking, then qualitatively analyzed textual interactions within teams that may have influenced those perceptions. Nine out of 10 teams gave a high score for online peer assessment supporting team-based creative thinking (M ≥ 3.8). However, scores varied across teams (ranging from M = 3.7 to 4.8), with strength in agreement of scoring within teams ranging from rWG = 0.67 to 0.96. Textual analysis revealed that students provided a range of Cognitive and Affective feedback. Affective feedback was often supportive, while Cognitive feedback tended to be direct but nonabrasive, usually involving suggestions or identifying issues with ideas. Teams that balanced Cognitive and Affective feedback tended to reach stronger agreement about online peer assessment supporting team-based creative thinking while maintaining high mean scores. These patterns indicate that integrating higher frequencies of both feedback may support team-based creativity by fostering a unified belief in the value of the collaborative work.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".