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Record W4379598681 · doi:10.1097/ceh.0000000000000516

Developing Faculty Developers: An Underexplored Realm in Professional Development

2023· article· en· W4379598681 on OpenAlexaff
Klodiana Kolomitro, Eleftherios Soleas, Yvonne Steinert

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsQueen's University
Fundersnot available
KeywordsProfessional developmentFaculty developmentRealmAdaptation (eye)Process (computing)Engineering ethicsKnowledge managementField (mathematics)Medical educationBest practicePsychologyPedagogyMedicineEngineeringComputer sciencePolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.240
GPT teacher head0.527
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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