Perspectives of graduate student teaching assistants on value and meaning in their role
Bibliographic record
Abstract
There is increasing reliance on graduate student teaching assistants (GTAs) in undergraduate education, yet the impact of this role on graduate students is understudied. Previous research has focused on tangible outcomes such as skill development, rather than how GTAs value or find meaning in this role. Our study used a phenomenological approach rooted in self-determination theory to allow graduate students to describe their own experience of being GTAs and how they found value and created meaning through their role. We conducted interviews with five GTAs and used thematic analysis to describe their experiences. Generally, GTAs found the experience to be an important and positive aspect of their graduate program, intentionally using the experience to explore potential career paths and find belonging in the wider academic community. However, GTAs also identified several challenges, including pressure to exceed their contracted hours, and some GTAs saw these challenges as ethical dilemmas that were difficult to resolve. Overall, our study demonstrates the deep mindfulness that GTAs use when reflecting on their experiences and making choices within their role. Given the reflection that GTAs bring to their role, we recommend that those who train and work with GTAs actively support their professional development through centering GTA needs in order to enhance the experience of GTAs in the classroom.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".