Strategic Use of Role Playing in a Training Workshop for Chemistry Laboratory Teaching Assistants
Bibliographic record
Abstract
Many Canadian universities have created professional development programs for their teaching assistants (TA) but may be uncertain about how to bridge the gap between TAs’ knowledge of effective teaching strategies and TAs’ confident applications of these strategies. We present a technique used in a two-day training workshop to enhance graduate students skills in using effective teaching strategies: role playing. This paper outlines a framework that includes five key elements (Icebreaking, Shared Experiences, Modelling, Acting and Debriefing) to strategically design role playing activities in a training program. We describe each of the 5 elements and explain how they support training through role play exercises. Participant written feedback collected in 2010, 2011, 2012 and 2014 suggested that role playing was a useful and enjoyable technique. Pre and post workshop questionnaire data suggested that self-perceived competencies for specified tasks directly connected to a role play activity promoted greater positive differences between the pre and post groups compared to self-perceived competencies for specified tasks not directly connected to a role play activity. Based on these results, we assert that training programs which rely on strategic role playing activities will lead to a better overall TA experience of the training program and improvements in TAs’ self-perceptions of certain teaching competencies.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".