Creation of A Mixed-Reality Simulator Professional Development Module to Attend to the Unique Needs of International Teaching Assistants in Active Learning Classrooms
Bibliographic record
Abstract
International teaching assistants (ITAs) bring cultural perspectives and language diversity to university programs. However, ITAs can be underserved through traditional professional development initiatives. A mixed-reality teaching simulator module was created to attend to the unique needs of chemistry ITAs and prepare them for active learning instruction. To create the module, seven ITAs were interviewed about their professional development needs. ITAs most commonly wanted to practice asking and answering student questions, engaging disengaged or disrespectful students, and navigating language difficulties with students. The mixed-reality simulated teaching module provided opportunities for ITAs to rehearse one or more of the three focus areas. Three ITAs tested the simulator and reported that using the simulator for professional development with cultural and linguistic layers would be beneficial both to existing and new ITAs. These research findings set the foundation for generating a transformative set of training materials that institutions can use to reduce teaching anxiety and increase the effectiveness of ITAs in the classroom, thus enhancing student learning through more effective active learning instruction.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".