Standing to Preach, Moving to Teach: What TAs Learned from Teaching in Flexible and Less-Flexible Spaces
Bibliographic record
Abstract
This paper examines the effect of the architectural layout of two classrooms (one flexible and one less-flexible) on Teaching Assistants’ (TAs) movement and interactions with students. Four TAs from a first-year undergraduate introductory course were chosen for the two studies. In study 1, the TAs taught the same lesson twice to two groups of students on the same day but in different classrooms, thereby controlling for content differences. Study 2 investigated the impacts that flexible and non-flexible spaces have on the same cohort of students, as the TAs continued to teach the same students but the students switched classrooms for the second half of the course, thereby controlling for differences in student participants. From the video analyses, there was a clear difference in how the TAs moved in the classroom and the interactions they had with students. Both TAs and students reported in surveys that there was a difference in their movement in the respective rooms that had an impact on their teaching and learning quality. This finding starts the conversation on how space can affect TAs, in order for TAs to consider how their movement is affected by classroom configurations, and how this change in movement can affect teaching strategies and impact their students’ learning.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".