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Record W3195271869 · doi:10.1187/cbe.21-02-0037

Professional Development, Shifting Perspectives, and Instructional Change among Community College Anatomy and Physiology Instructors

2021· article· en· W3195271869 on OpenAlexaff
Audrey Rose Hyson, Branden Bonham, Suzanne Hood, Megan C. Deutschman, Laura C. Seithers, Kerry Hull, Murray Jensen

Bibliographic record

VenueCBE—Life Sciences Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsBishop's University
FundersNational Science Foundation
KeywordsConceptual changeProfessional developmentMedical educationPsychologyProfessional learning communityQualitative researchCommunity collegeActive learning (machine learning)Faculty developmentPedagogyMedicineSociologyComputer science

Abstract

fetched live from OpenAlex

This paper presents community college (CC) instructors' responses to the Community College Anatomy and Physiology Educational Research (CAPER) project, a professional development program focused on active learning and educational research. We engage with conceptual change theory to better understand why and how CC instructors shifted their perspectives toward active learning. Qualitative data indicate that the participating CC instructors experienced pedagogical discontentment, leading to increased positive beliefs about active learning and educational research. In addition, we find that CC instructors have continued their pursuit of pedagogical change and educational research through communities of practice, which provide positive learning environments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.137
GPT teacher head0.452
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2021
Admission routes1
Has abstractyes

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