Building a sustainable structure to support the Adaptive Mentorship model in teacher education
Bibliographic record
Abstract
Purpose – The purpose of this paper is to develop a deeper understanding of how to implement a professional development training strategy for the Adaptive Mentorship (AM) model (Ralph and Walker, 2010a) and explore how cooperating teachers used the model, not only to assist pre-service teachers in their development, but also to reflect on their role as a mentor. Design/methodology/approach – This research design uses a collective case study approach. The researchers are positioned in the study as active agents, not only in the traditional way as administrating a questionnaire but as participant leaders. The questionnaire was designed to collect data on the frequency of use and effectiveness of the AM model. It was sent to cooperating teachers, for two years from two different cohorts (n=141,n=123). Findings – By the end of the second year 84 percent of the cooperating teachers said they “did or mostly did” understand the AM model after the seminar. Less than half of the cooperating teachers (42 percent) recommended that the AM model should be used at seminar. Of the rest, while 21 percent were not in favor of the AM model being used, 37 percent would consider using it at seminar. The findings in this study suggest that for many cooperating teachers the notion of reciprocal development had not yet permeated their consciousness. Originality/value – This study will guide future cooperating teacher professional development sessions to support cooperating teachers as they make the paradigmatic shift from supervisor to mentor. To the knowledge it is the only study that explores the professional development training necessary for implementing the AM model with an entire cohort of interns.
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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".