Faculty members’ perceptions of how faculty development initiatives could contribute to developing their adaptive expertise
Bibliographic record
Abstract
PURPOSE: Faculty development should equip faculty members to navigate emerging challenges by cultivating adaptive expertise. While prior studies highlight the potential for such development, little is known about educational strategies or design principles that support it. This study explored faculty members' perspectives on how faculty development initiatives could foster adaptive expertise. METHODS: principles on the development of adaptive expertise, which we synthesized from existing literature. RESULTS: Participants suggested that faculty development initiatives could support faculty members' adaptive expertise by (1) bridging educational theory and practice, (2) facilitating collaborative learning across diverse contexts, and (3) encouraging experimentation with teaching practices in safe settings. Central to these three findings was the value of reflection on 'the why' behind teaching practices. Additionally, certain enabling conditions, such as a safe culture, were deemed essential for effectively implementing these principles. CONCLUSION: Our research shows that under certain conditions, faculty development initiatives could promote adaptive expertise by bridging theory and practice, fostering diverse collaborations, and encouraging safe experimentation, all grounded in a focus on understanding 'the why' behind teaching practices.
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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 | 0.037 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".