Fairness in Student Group Formation:Perspectives, Priorities, Compromises, Mechanisms, and Tooling
Bibliographic record
Abstract
Allocating students to groups is a critical yet under-researched challenge in computing education, with significant implications for fairness and student outcomes. Little is known about existing allocation approaches and their treatment of fairness, whilst practical realities faced by educators and students remain largely undocumented. Without rigorous attention to fairness, group formation risks amplifying bias and disadvantaging vulnerable students. This study provides the first holistic exploration of fairness in group formation within higher-education computing contexts through a systematic review of 262 papers, analysis of 18 allocation tools, interviews with 20 educators, and six student focus groups. Findings reveal a lack of evidence linking fairness definitions to groupwork characteristics, processes, and outcomes. To address this gap, we propose a framework for pedagogically informed group formation that embeds fairness, supporting educator decision-making and improving student experiences. We also establish definitions of fairness, groupwork characteristics, and processes to guide future research in computing 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 | 0.157 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| 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".