Highly Relevant Mentoring (HRM) as a Faculty Development Model for Web-Based Instruction.
Bibliographic record
Abstract
This paper describes a faculty development model called the highly relevant mentoring (HRM) \nmodel; the model includes a framework as well as some practical strategies for meeting the \nprofessional development needs of faculty who teach web-based courses. The paper further \nemphasizes the need for faculty and administrative buy-in for HRM and examines relevant \ntheories that may be used to guide HRM in web-based teaching environments. \n \nOf note is that HRM was conceived by the instructional design staff who contributed to this \npaper before the concept of high impact mentoring appeared in the recent literature (2009). \nWhile the model is appropriate in various disciplines and professions, the examples and \nscenarios provided are drawn from a Canadian university’s experience of using HRM, in \nconjunction with a pedagogical approach called ICARE, in a variety of nursing courses and \nprograms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".