Collaboration, Mentoring and Co-Teaching in Teacher Education
Bibliographic record
Abstract
Collaboration, Mentoring and Co-Teaching in Teacher Education Collaboration at the university level is a fundamental element needed to enhance teaching (Cochran-Smith & Fries, 2005) and reflection is a critical component of teacher education (Dewey, 1933, 1938). A case study is presented of one senior university faculty member's experiences co-teaching with two doctoral students seeking to understand the impact of shared decision-making and authentic collaboration on individuals entering the academy. An analysis of the authors' shared experiences indicated that, through this mentoring, collaborative and mutually beneficial relationships were built. An analysis of the authors' experiences also indicated that these collaborative relationships were built upon several key factors, specifically (a) a strong sense of individual accountability and professionalism; (b) the mutual creation and demonstration of respect; (c) affirmation and overt participation in reciprocal growth and development; (d) attention to issues of power and abeyance. The findings of the study highlight the need for further exploration into the role of mentorship of junior faculty and the efficacy of co-teaching processes in the development of professional identities of junior faculty entering the academy.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".