Teaching Assistant in Residence: A Novel Peer Mentorship Program for Less Experienced Teaching Assistants
Bibliographic record
Abstract
Each semester approximately 80 graduate teaching assistants (TAs) support the delivery of the undergraduate computer science program at The University of Calgary. While these teaching assistants provide an essential service to the undergraduate program, in past years the department has invested little effort in ensuring that teaching assistants have the opportunity to develop the skills necessary to tackle these duties effectively. During the 2012-2013 academic year, a novel TA mentorship program was initiated. An experienced teaching assistant with a demonstrated record of excellence in teaching was hired to serve as the TA in Residence. This graduate student provided training and advice to new teaching assistants, including classroom visits where the TA in Residence observed TAs in action. TAs that participated in the program generally reported that the advice provided by the TA in Residence was helpful, and all of the TAs that responded to the survey question believed that it would be worthwhile to continue the mentorship program in the future. As a result, we continued the TA in Residence program in subsequent years. This poster provides an overview of the TA in Residence program, its benefits, and the challenges that the TAs in residence have faced and overcome. The revisions that we have made to the program since its inception are also described, which will allow other departments interested in developing a TA in Residence program to avoid some of the pitfalls that we initially encountered.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".