Co-Teaching in the Academy-Class Program: From Theory to Practical Experience
Bibliographic record
Abstract
This study focuses on implementing co-teaching models in the practical experience of teacher training processes. It examines the experience models in terms of theory versus practice from the perspectives of students of education and training teachers (school and pre-school) who participated in the special “Academy-Class” program in the 2017 academic year at Ohalo College. 125 subjects participated in the study. The overriding goal of the research was to identify the dominant patterns in this unique practical experience in teachers training. The research questions sought to clarify the extent to which the six main co-teaching models described in the research literature are manifested in practical and educational terms in the Academy-Class program; offer a comparison between common teaching practices and the co-teaching models; and assess how common Synergetic Collaboration is as a co-teaching method relative to other low-level methods.Our findings show that the co-teaching models were more dominant than the traditional teaching models among all the sample groups. The greatest difference was found in the reports of the training teachers (0.79) at the school, while the smallest difference was found among students training to become teachers (0.13). We have seen that experiencing a clinical model of co-teaching involves shared work between a training teacher and of a student of education. There is a need to change training processes, as well as expanding the theoretical approaches that describe the wide range of shared co-teaching.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".