The Changing Face of Mentorship for Preservice and Beginning Teachers
Bibliographic record
Abstract
In education, it has long been understood that there are many benefits to mentoring. Unfortunately, a common training program across Alberta does not exist for mentors to professionally acquire skills to excel at their role as mentor. The literature describes a multitude of benefits to mentorship and how both a mentor and a preservice teacher would benefit from the time and practice invested in mentoring. In this capstone project, I have introduced a professional development (PD) session for the inexperienced teacher mentor that will inform and allow growth and skill development to help prepare a mentor for the challenges of mentorship with new preservice teachers in Alberta. I describe a series of steps to follow prior to accepting a student teacher to affirm personal readiness to steps to follow once working with a student teacher. These steps follow Alberta Education’s philosophy of supporting the vision of an educated Alberta 2030: Engaged Thinkers and Ethical Citizens with an Entrepreneurial Spirit include communication, relationship building (Alberta Education, 2010). This PD session will dramatically increase the level of success of partnerships between mentor and mentee and increase the professionalism and retention of teachers in education to better prepare them for the classroom.
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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 | 0.043 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".