“A Group of People to Lean On and Learn From”: Graduate Teaching Assistant Experiences in a Pedagogy-Focused Community of Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Graduate teaching assistants (GTAs) influence undergraduate STEM students’ learning and experience because they teach most lab sections across STEM disciplines. Despite GTAs’ central role in lab teaching, their training is often focused on policies and expectations, rather than teaching effectively. In this study, we took a community of practice (CoP) approach to learning and facilitated a semester-long, pedagogy-focused CoP to address the lack of pedagogical development and support for GTAs. Our purpose was to collect, describe, and develop our understanding of the experiences of GTAs participating in our CoP while teaching reformed undergraduate exercise physiology labs. CoP members completed an asynchronous, SoTL-informed micro-course focused on evidence-informed teaching practices and active student learning support, which was then debriefed at the first CoP meeting. Each subsequent weekly meeting featured 30 minutes of CoP members reflecting on, discussing, and helping each other improve their teaching practices. At the end of the semester, we conducted semi-structured interviews with GTAs to learn about their experiences in the CoP and teaching. We found reflecting on and talking about their teaching was a novel experience for GTAs. They also described implementing teaching practices that were new to them, exercising autonomy, developing confidence, approaching teaching philosophies, and their experiences with psychological safety. Our findings suggest facilitating weekly reflection on teaching in a CoP can provide GTAs with opportunities to develop their teaching practices in meaningful, practical, and (sometimes!) enjoyable ways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it