Developing Faculty Developers: An Underexplored Realm in Professional Development
Bibliographic record
Abstract
ABSTRACT: As faculty developers enter the field and grow in their roles, how do they keep up with ongoing changes and ensure that their knowledge remains relevant and up-to-date? In contrast to most of the studies which focused on the needs of faculty members, we focus on the needs of those who fulfill the needs of others. We highlight the knowledge gap and lack of adaptation of the field to consider the issue of professional development of faculty developers more broadly by studying how they identify their knowledge gaps and what approaches they use to address those gaps. The discussion of this problem sheds light on the professional development of faculty developers and offers several implications for practice and research. Our own piece of the solution indicates that faculty developers follow a multimodal approach to the development of their knowledge, including formal and informal approaches to addressing perceived gaps. Within this multimodal approach, our results suggest that the professional growth and learning of faculty developers is best characterized as a social practice. Based on our research, it would seem worthwhile for those in the field to become more intentional about the professional development of faculty developers and harness aspects of social learning in that process to better reflect faculty developers' learning habits. We also recommend applying these aspects more broadly to, in turn, enhance the development of educational knowledge and educational practices for the faculty members these educators support.
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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 | 0.043 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| 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".