Using senior undergraduate students as coaches in an introductory chemical engineering design course
Bibliographic record
Abstract
Senior undergraduate students were hired as Teaching Assistants (TAs) in CHBE 221, an introductory bioprocess design course offered in the Department of Chemical and Biological Engineering at University of British Columbia, and the roles were recast from traditional course support, to being coaches and mentors to a small number of teams working on term-spanning design projects. This was done as a means to provide more personalized support to students working on realistic design problems early in their training, leverage the benefits of peer learning strategies, and provide professional and leadership development opportunities to the TAs ahead of graduation. At the end of the course in which this was implemented, surveys were sent to students asking them to comment on their experience with this mentorship model, and the TAs of the course were interviewed to better understand theirs. This paper presents the results of these surveys and interviews, highlights the benefits and challenges of this approach, and provides recommendations for future implementations of this student support model
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".