Investigating teaching assistants’ participation in a simulated meeting in a United States University English course
Bibliographic record
Abstract
Abstract In countries such as the United States and Canada, an increasing number of non-native English speaking graduate students work in the capacity of university teaching assistants. Over the past several decades, a number of communication challenges between non-native English speaking International Teaching Assistants (ITAs) and native English-speaking undergraduate students have emerged. Universities have responded by developing ITA education programs that aim to teach English for the specific purpose of university teaching. This paper examines how ITA education can provide ITAs opportunities for authentic practice and reflection of communicating in an instructional capacity. ITAs enrolled in an English course participated in a video-recorded clinical simulation – a form of situated practice in which individuals engage with an actor who is trained to interact with all participants in a consistent manner. ITAs met with an actor-portrayed undergraduate student who is visiting their teaching assistant to express concern about a group project. ITAs met in small groups following the simulated meeting to reflect on the shared experience. Qualitative analysis demonstrates that the simulated context elicited both challenges to comprehensibility and the use of strategies by students and ITAs to manage miscommunication, while working towards mutual understanding of the students’ concerns. Reflective discussions reveal how the ITAs evaluated approaches to communicating with a concerned student in an instructional context. Embedding clinical simulations in ITA education can provide opportunities for the situated practice of using language to communicate instructional decisions, and as a structured opportunity for supporting mutual understanding between ITAs and native English-speaking students.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".