Can mentoring and reflection cause change in teaching practice? A professional development journey of a Canadian teacher educator
Bibliographic record
Abstract
This article explores elements of the professional development of a pre‐tenured teacher education professor. I am that professor and I trace my journey of growth, which was aided by peer mentoring. First, I present a brief discussion on literature associated with mentoring that I found pertinent, followed by how mentoring has emerged as I re‐designed a teacher education course to better meet the needs of pre‐service teachers. This course previously posed great difficulties for me in linking theory to practice. In this context, mentoring helped me improve my teaching practice through critical conversations with a mentor. It documents my struggles to improve my teaching at the university level. In narrating my journey I am not presenting a model of best practice but, rather, highlighting how mentoring allowed me to reflect on and improve my teaching practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.021 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| 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".