Developing and Implementing a Blended Faculty Teaching Mentorship Program: A Canadian Pilot Project
Bibliographic record
Abstract
This article presents the results of the development and implementation of a mentorship program, now in its sixth year, designed to support the professional development of teaching skills for faculty and contract instructors. The program is unique in that it is a combination of a variety of other approaches such as formal and informal mentoring as well as intra-departmental and interdepartmental mentoring. This model incorporates the effective elements of a mentor program as identified in the literature, while eschewing the traditional model of one-to-one mentoring between senior and junior colleagues. The findings to date, which include more support for teaching after participating in the project as well as an increase in the amount of faculty accessing a mentor, indicate that the program is achieving its intended goals and should continue. The authors provide recommendations and suggestions for the inclusion of a teaching mentorship program at other institutions within (or outside of) Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.040 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".