Re‐envisioning mentorship: pre‐service teachers and associate teachers as co‐learners
Bibliographic record
Abstract
Associate teachers have always been integral to pre‐service teacher education, providing learning experiences to support the development of pedagogical knowledge in various subject areas. However, the requirement by many national and provincial curricula that technology be integrated into teaching practice, calls for a re‐examination of the roles associate teachers play. This paper reports on a study of associate teachers’ perspectives about their roles in supporting pre‐service teachers as they integrate technology during the practicum. An invitation to participate in a set of pre‐ and post‐practicum interviews about supporting pre‐service teachers integrate technology was issued as part of a larger survey sent to 150 associate teachers. Content analysis of pre‐ and post‐interview data from four associate teachers and survey responses revealed that associate teachers’ roles varied across a continuum from mentor to co‐learner in relation to technology integration. These changing roles point to a need to re‐envision traditional notions of mentorship during the practicum.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".