Student Group Work in Widely Interdisciplinary Teams
Bibliographic record
Abstract
Group work is often used in university courses. This article examines group work in a widely interdisciplinary holography course that combines both art and science, for students from the arts, humanities, social sciences, and sciences. In these interdisciplinary teams, how much specialization of labor (dividing work according to students’ pre-existing abilities or personal interests) is acceptable? We present student survey responses regarding their attitudes toward interdisciplinary group work, and their practices in dividing the work, to determine how much specialization of labor is taking place within the interdisciplinary teams. The surveys indicate a mix of approaches among groups concerning the division of labor based on prior skills. In the presence of specialization of labor, students learned from their partners and displayed a positive attitude toward working with someone from a different discipline. We believe that the intriguing nature of the holography projects helped many students avoid dividing the work according to their prior skills, and helped them see the value of working in a widely interdisciplinary team.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".