Designing Dialogic Peer Feedback in Collaborative Learning: The Role of Thinq Tank
Bibliographic record
Abstract
As technology continues to reshape education, the integration of peer feedback (PF) is increasingly recognized as a key element in promoting student collaboration and learning. This design study tracks the development and implementation of the Thinq Tank tool, which is aimed at facilitating dialogic PF within computer-supported collaborative-inquiry learning (CSCiL) environments. The research was conducted through three design iterations, comparing two key experimental conditions: one contrasting non-dialogic PF with dialogic PF, and another comparing dialogic PF with scripted dialogic PF featuring dynamic sentence-starters and defined roles. Utilizing a quasi-experimental design, we assessed the overall impact on scientific (collaborative) learning outcomes, students’ perceptions of PF, and the composition and elements of PF dialogues. The findings suggest that the integration of dialogic PF within a CSCiL lesson series helps to boost scientific collaborative learning outcomes and increases student receptivity to PF. The study demonstrates that integrating structured support mechanisms within PF tools promotes PF dialogues in which students critically evaluate each other’s work and collaboratively think about ways to improve quality. These insights illustrate that structured, technology-supported PF can significantly advance inquiry outcomes and foster deeper collaborative learning in secondary STEM education.
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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 | 0.037 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".