Simulation-assisted Internship Workshops – A Tool for Bridging between Academia and the Field in Teacher Training in Israel
Bibliographic record
Abstract
During their residency year and concurrent with their first year of work at a school, teacher residents must participate in an internship workshop to receive support, empowerment, and professional development. The purpose of the current study is to examine the efficacy of an internship workshop combining simulations for teacher residents, and whether combining simulations in the workshop constitutes a teacher training tool that bridges between academia and the field with regard to group cohesion and evaluation of personal functioning in the workshop. Another purpose is to explore the resident's sense of empowerment and mental preparation after participating in the workshop. Participants included 41 teacher residents who participated in 16 simulations throughout their residency year. The study utilized a mixed methodology: a quantitative method comprised of questionnaires on social cohesion in the workshop and social-personal functioning; and a qualitative method, where the participants were interviewed at the conclusion of the workshop. The research findings show a high score for social cohesion in the workshop. Despite the disagreements among the residents, they feel safe and accepted in the group. Regarding the aspect of social-personal functioning, the residents reported that the workshop had a considerable impact on their functioning at the school and that thanks to the workshop they reached a good understanding of their behavior as teachers and of their interpersonal conduct in the group. Regarding the measure of behavior in the workshop, they reported active participation and high sharing of difficulties. In the qualitative part, in the interviews the teachers noted the importance of two processes that took place in the cohesive and supportive group. The first was their mental preparation by dealing with difficulties, challenges, and dilemmas that arose in the workshop via the simulations, as well as receiving reflective feedback and practical tools for their work. The second was the importance of assistance with the bureaucratic process until receiving the teacher's license (such as completing forms, evaluation, teacher associations, and pay). The research findings illuminate the importance of integrating simulations in internship workshops for teacher residents, which strengthens the association between academia and actual work at the school.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".