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Record W4388776859 · doi:10.18357/otessaj.2023.3.1.42

Sustaining Positive Change in the Teaching Scholars’ Online Community of Practice

2023· article· en· W4388776859 on OpenAlexaffvenue
Andrew Mardjetko, Michele Jacobsen, Beth Archer‐Kuhn, Cari Din, Darlene Donszelmann, Lorelli Nowell, Heather A. Jamniczky

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsScholarship of Teaching and LearningScholarshipCommunity of practiceLeverage (statistics)Value (mathematics)SociologyPedagogyCommunity engagementKey (lock)Online communityKnowledge managementEngineering ethicsPublic relationsPolitical scienceTeaching methodTeaching and learning centerEngineeringComputer science

Abstract

fetched live from OpenAlex

In this paper, we emphasize the value of an online community of practice (OCoP) for bringing together faculty from across disciplines to share and leverage their diverse expertise and perspectives. We examine the transition of an interdisciplinary community of practice through the pivot into an online environment for engagement, communication, and collaboration. Through this paper we describe our individual Scholarship of Teaching and Learning (SoTL) projects and how we have navigated these projects within the Teaching Scholars OCoP, as well as our reflections and key learnings that have resulted from this sustained collaboration. We contribute key learnings and online strategies which can inform and be tailored by other academics and institutions who are developing online communities of practice as an approach to sustaining educational leadership and change in SoTL research and practice in diverse and distributed contexts.

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)0.040
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.004
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 head0.105
GPT teacher head0.462
Teacher spread0.356 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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