Highly Relevant Mentoring (HRM) as a Faculty Development Model for Web-Based Instruction / Highly Relevant Mentoring (HRM) (mentorat haute efficacité), un modèle de formation du corps professoral à l’enseignement en réseau
Bibliographic record
Abstract
This paper describes a faculty development model called the highly relevant mentoring (HRM) model; the model includes a framework as well as some practical strategies for meeting the professional development needs of faculty who teach web-based courses. The paper further emphasizes the need for faculty and administrative buy-in for HRM and examines relevant theories that may be used to guide HRM in web-based teaching environments. Of note is that HRM was conceived by the instructional design staff who contributed to this paper before the concept of high impact mentoring appeared in the recent literature (2009). While the model is appropriate in various disciplines and professions, the examples and scenarios provided are drawn from a Canadian university’s experience of using HRM, in conjunction with a pedagogical approach called ICARE, in a variety of nursing courses and programs. Cet article décrit un modèle de formation du personnel enseignant intitulé « highly relevant mentoring (HRM) » (mentorat haute efficacité); ce modèle comprend une structure et des stratégies pratiques visant à combler les besoins en formation du corps professoral d’une faculté offrant des cours en réseau. L’article souligne la nécessité d’un appui facultaire et administratif au HRM et étudie les théories pertinentes pouvant servir à guider le HRM dans des milieux d’enseignement en réseau. On notera que le HRM a été conçu par l’équipe de conception de matériel pédagogique qui a contribué à cet article avant l’apparition, dans les publications récentes (2009), du concept de « high impact mentoring » (mentorat à haut rendement). Bien que ce dernier modèle convienne à diverses disciplines et professions, les exemples et les scénarios fournis ici sont tirés de l’expérience d’utilisation du HRM dans une université canadienne, conjointement à une approche pédagogique appelée ICARE, dans une variété de cours et de programmes de sciences infirmières.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".