ENGINEERS TEACHING COMMUNICATION: EVALUATING THE IMPACT OF TA TRAINING ON GRADUATE STUDENT COMMUNICATION, TEACHING AND PROFESSIONAL DEVELOPMENT
Bibliographic record
Abstract
The Engineering Communication Programworks with engineering TAs in the Department ofMechanical and Industrial Engineering at the Universityof Toronto to deliver communication instruction in coredesign courses. Engineering TAs’ disciplinary expertiseaffords increased credibility with students, and we havehad consistent anecdotal evidence from TAs that teachingcommunication has made them better communicators.Currently, training involves a combination of instructionand mentorship, both from faculty and each other.Here, we investigate TAs’ increased confidence andskill in communication and teaching: what they finduseful, how the training has influenced theircommunication and teaching practice, and what morethey would like to explore in the future. An initial surveyand discussion found that confidence was shaped byexperience, course-specific training, instructor feedback,and peer learning. We hope to build on these findings infuture through a broader study of TAs in the Faculty andfurther development of our TA training programs
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".