Instructors’ Understanding of Teaching First-Year Engineering Teaming: Characterization and Redirection
Bibliographic record
Abstract
Teaming is increasingly important to teach well in undergraduate engineering education. Teams composed of both majority and minoritized students have an increased risk of majority members harassing minoritized members. Instructors of large classes have a difficult time identifying in which teams such harassment is taking place, and knowing what to do to interrupt it. This paper, part of a bigger project grounded in microaggression theory and selective incivility theory, specifically considers what instructors currently do, and indeed whether it is their job to address teammate harassment. We undertook a rough thematic analysis of interviews with instructors of a large first-year engineering course at a large American research-extensive majority-white university in the Midwest. We found instructors adopted an individual-centric model of teaming, intervened mainly in severe instances, and their interventions tended to be subtle. We offer an early version of an alternative model to structure forthcoming training sessions with instructors, graduate teaching assistants, and peer teachers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".