Technology-Enhanced Faculty Development: Future Trends and Possibilities for Health Sciences Education
Bibliographic record
Abstract
Engagement is the focus of technology-enhanced faculty development. Unfortunately, faculty development (i.e. “teaching the teacher”) can sometimes fail to mirror the advances we have created for our students, leaving room for innovation, modernization, and change [ 1 ]. While research has shown learning outcomes can be somewhat equivocal for traditional approaches to teaching compared to technology-enhanced ones [ 2 , 3 , 4 ] sometimes favouring lower technology approaches [ 5 , 6 ], continued use of “the same boring methods” certainly limits the level of learner engagement. Practically speaking, unengaged faculty will not pursue learning objectives. Technology-enhanced faculty development supports various benefits, from access to content through diverse device options (e.g. mobile phones) to interaction with content via instant feedback options (e.g. interactive video, online quiz). Additionally, with distributed and online education models growing, innovative education technology can vastly improve access to education.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.026 | 0.025 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".