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Record W4365514315 · doi:10.1080/1360144x.2022.2135005

Impact of a regional community of practice for academic developers engaged in institution-level support for SoTL

2023· article· en· W4365514315 on OpenAlexaff
Laura Lukes, Sophia Abbot, Dayna Henry, Melissa Summer Wells, Liesl Baum, Kim A. Case, Edward J. Brantmeier, Lindsay B. Wheeler

Bibliographic record

VenueThe International Journal for Academic Development · 2023
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsScholarship of Teaching and LearningLeverage (statistics)InstitutionCommunity of practiceHigher educationScholarshipSociologyIsolation (microbiology)Knowledge managementPedagogyManagementPolitical scienceTeaching methodComputer scienceSocial scienceTeaching and learning center

Abstract

fetched live from OpenAlex

Academic developers play a key role in advancing instructor engagement in the Scholarship of Teaching and Learning (SoTL) at their higher education institutions, but face structural and epistemological isolation. To leverage the knowledge and experience of developers leading SoTL efforts at their respective institutions, a group of academic developers co-created a regional community of practice (CoP) centered on developing evidence-based strategic plans and programming models to advance SoTL at their. We describe the development and outcomes of this regional CoP. Future directions for the use of such a regional CoP model to collaboratively develop cross-institutional offerings are also discussed.

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)0.049
metaresearch head score (Gemma)0.069
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.069
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0060.004
Scholarly communication0.0080.004
Open science0.0040.026
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.337
GPT teacher head0.539
Teacher spread0.202 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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