Online learning for faculty development: A review of the literature
Bibliographic record
Abstract
BACKGROUND: With the growing presence of computers and Internet technologies in personal and professional lives, it seems prudent to consider how online learning has been and could be harnessed to promote faculty development. AIMS: Discuss advantages and disadvantages of online faculty development, synthesize what is known from studies involving health professions faculty members, and identify next steps for practice and future research. METHOD: We searched MEDLINE for studies describing online instruction for developing teaching, leadership, and research skills among health professions faculty, and synthesized these in a narrative review. RESULTS: We found 20 articles describing online faculty development initiatives for health professionals, including seven quantitative comparative studies, four studies utilizing defined qualitative methods, and nine descriptive studies reporting anecdotal lessons learned. These programs addressed diverse topics including clinical teaching, educational assessment, business administration, financial planning, and research skills. Most studies enrolled geographically-distant learners located in different cities, provinces, or countries. Evidence suggests that online faculty development is at least comparable to traditional training, but learner engagement and participation is highly variable. It appears that success is more likely when the course addresses a relevant need, facilitates communication and social interaction, and provides time to complete course activities. CONCLUSIONS: Although we identified several practical recommendations for success, the evidence base for online faculty development is sparse and insubstantial. Future research should include rigorous, programmatic, qualitative and quantitative investigations to understand the principles that govern faculty member engagement and success.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".