Co-Teaching in the “Academia Class”: Evaluation of Advantages and Frequency of Practices
Bibliographic record
Abstract
This article constitutes the continuation of a research process that investigated models and methods for co-teaching in the “Academia Class” program in the Ohalo Academic College (Nissim & Naifeld, 2018). The article focuses on the contribution of this program to all those who participated in it, identifying co-teaching practices and the connections between the sense of contribution and identification of those practices. The research relied on the collection and analysis of quantitative data. The research population included 125 respondents, 51 (40.8%) schoolteachers, 36 (28.8%) student-teachers studying general education, 18 (14.4%) kindergarten teachers and 20 (16.0%) student-teachers studying early childhood education. Three main research questions guided the investigation: 1) To what extent does each group of participants in the program estimate that co-teaching methods are advantageous for the teachers/kindergarten teachers, student teachers and pupils? 2) Which prevalent co-teaching practices are used in the Academia Class program? 3) Is there a correlation between the respondents’ attitudes concerning the advantages of co-teaching and the practices prevalent in the Academia Class program? The responses to these questions indicate the extent of success or lack of success of co-Teaching. The main finding indicates that the trainer teachers and the student teachers agreed that there were many advantages to co-teaching and that it contributed to school pupils and the kindergarten children. Thus, it seems that the Academia Class program has an influence beyond mere training processes, on the learning processes in the classes and school pupils. With regard to the advantages of the co-teaching for the school teachers and kindergarten teachers.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".