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Record W4206393298 · doi:10.1002/aet2.10722

The Learning Loop: Conceptualizing Just‐in‐Time Faculty Development

2022· article· en· W4206393298 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAEM Education and Training · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicE-Learning and COVID-19
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLoop (graph theory)Mathematics educationPsychologyComputer scienceMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: As technology advances, the gap between learning and doing continues to close-especially for frontline academic faculty and clinician educators. For busy clinician faculty members, it can be difficult to find time to engage in skills and professional development. Competing interests between clinical care and various forms of academic work (e.g., research, administration, education) all create challenges for traditional group-based and/or didactic faculty development. METHODS: The authors engaged in a synthetic narrative review of literature from several unrelated fields: learning technologies, medical education/health professions education, general/higher education. The aim for this review was to synthesize this pre-existing literature to propose a new conceptual model. RESULTS: , to guide the development of online faculty development for just-in-time delivery. CONCLUSIONS: is a new conceptual framework that may be of use to those engaging in online, digital learning design. Faculty developers, especially in emergency medicine, can integrate leading concepts from the technology-enhanced learning field (e.g., microlearning, micro-credentialing, badging) to create new types of learning experiences for their end-users.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.103
GPT teacher head0.390
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