“Anyone can be a mentor”: tracing teacher candidates’ understanding of their emerging mentoring practice and identity
Bibliographic record
Abstract
Purpose In this research paper, I create and test a model of peer mentoring at a Canadian faculty of education that created spaces for candidates to pursue topics of interest through inquiry in communities of practice (CoPs). The study offers insight into how teacher candidates’ conceptualization of mentoring changed as they participated in the model under study. Design/methodology/approach First-year teacher candidates (n = 18) participated in voluntary CoPs over eight months. In addition to audio recordings of the CoPs, the data draws from two rounds of interviews and notes from a researcher journal. The analysis examined candidates’ practices by looking at how they performed mentorship and how they recognized mentorship. Findings The findings demonstrate how candidates’ understanding of mentoring changed throughout the eight months. While candidates used criteria such as quantity of experience to identify potential mentors in the initial phase, continued participation in the CoPs eventually resulted in candidates identifying each other as mentors. Nevertheless, candidates still struggled to recognize themselves as mentors. Originality/value Although previous models of peer mentoring in initial teacher education have shown potential for teacher candidates to try out new roles and identities, the findings from this study are unique because they demonstrate how an informal mentoring practice can emerge between candidates at the same stage in their studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".