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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".