Managing TAs at Scale: Investigating the Experiences of Teaching Assistants in Introductory Computer Science
Bibliographic record
Abstract
Teaching assistants (TAs) are essential members of post-secondary instructional teams, who often have considerable student-facing time. The recently increasing demand for introductory computer-science (CS) courses has resulted in a corresponding increased demand for TAs. The existing TA literature has predominantly focused on investigating the role of training in the TA experience, particularly with respect to performance. This provides little insight into how TAs experience their management. Consequently, we investigate the role of management in the experiences of introductory-level CS TAs. We provide the structure for a formal, tiered management scheme employed in a high-enrolment introductory CS course. This scheme attempts to address the challenges associated with managing TAs at scale. As a case study, we compare the experience of TAs under this new management scheme with that of TAs under an informal, unstructured management style used in smaller introductory CS courses. Specifically, we used questionnaires to look at TAs' self-efficacy and understand their experiences. While we did not find a significant difference in TA self-efficacy between the two management styles, thematic analysis of the open-response data revealed a greater number of challenges reported by the TAs under the tiered management system. TAs characterized this tiered system as "organized" and frequently reported feeling overworked. Across both groups, TAs identified improved communication and additional training as factors that could improve their experience. Our findings suggest self-efficacy was not a sufficient measure to quantify TA experience. We propose framing TA experience based on their motivation and general well-being.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".