Training Teaching Assistants to be Coaches: A Sustainable Approach
Bibliographic record
Abstract
More is being asked of graduate teachingassistants (GTAs) as engineering education places anincreased emphasis on teamwork and design. GTAsdirectly influence undergraduate learning. How theyapproach their role can determine if that influence ispositive or negative. Exerting a positive influencerequires GTAs to encourage positive group dynamics,foster self-directed learning, and support individual andteam innovation. In other words, the role of a GTA iscloser to that of a coach than to a teacher or a mentor.Unfortunately, most GTAs lack experience and skill incoaching and facilitation. To help shift our GTAs’perspectives and help them to think, act, and viewthemselves as coaches, we have developed a coachingand facilitation training workshop. We have now offeredthis workshop for a second year. The second offeringincludes a number of important updates which weredriven by three motivations: 1) improved educationaleffectiveness, 2) sustainability, and 3) dissemination.Important changes include a new activity to introduce theuse of open-ended questioning and a workbook whichsupports the workshop activities and acts as a referencesource which participants can consult after the workshop.We have also created a facilitators’ guide so that theworkshop may be delivered by facilitators other than theauthors. All workshop materials have been released undera Creative Commons license to facilitate dissemination.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".