Diversity Considerations in Team Formation Design, Algorithm, and Measurement
Bibliographic record
Abstract
Building teams that foster equitable interaction provides the foundation for a positive collaborative learning experience.Existing literature shows that many context-specific algorithms exist to help instructors form teams automatically in large classes, but the field lacks general guidelines for selecting a suitable algorithm in a given pedagogical context and lacks a general evaluation approach that allows for the methodological comparison of these algorithms.This paper presents a general-purpose team formation algorithm that considers diversity and inclusion in its design.We also describe an evaluation framework with diversity metrics to assess team compositions using synthetically generated student data and real class data.Our simulation and classroom experiments show that our algorithm performs competitively against three state-of-theart algorithms.We hope this work contributes to building a more equitable and collaborative learning environment for students. CCS Concepts• Human-centered computing → Collaborative and social computing design and evaluation methods; • General and reference → Metrics; • Social and professional topics → User characteristics.
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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 | 0.062 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".