To Teach is to Learn Twice: The Power of a Blended Peer Mentoring Approach
Bibliographic record
Abstract
Two students at a Canadian university perceived there was a lack of opportunities for peer mentoring support in their teacher education program. They approached a faculty member to co-create and research a blended peer mentoring support program embedded in a first-year education course. This study documents the journey of these two students as co-inquirers in a Scholarship of Teaching and Learning (SoTL) project. Through online surveys and interviews, first-year teacher candidates and faculty involved in the blended peer mentoring program identified four key benefits: new perspectives and expansion of ideas, positive and encouraging reinforcement, supportive connection with second-year students, and probing questions to think more deeply. Conversely, three major challenges were uncovered with the use of digital technologies to support this blended approach to peer mentoring: lack of email notification from the institution’s learning management system (LMS) with regards to the peer mentors’ online contributions, the impersonal nature of online peer mentoring, and the limited number of peer mentors. The major recommendation from this study was to create a blended program assignment to provide all second-year teacher candidates with the opportunity to learn how to serve as peer mentors to students just entering the teacher education program.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".