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Record W4381684111 · doi:10.36021/jethe.v6i1.320

Modalities of Faculty Engagement with the Scholarship of Teaching and Learning

2023· article· en· W4381684111 on OpenAlexaffabout
Celeste Suart, Martha Cassidy-Neumiller, Kelsey Harvey

Bibliographic record

VenueJournal of Effective Teaching in Higher Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScholarship of Teaching and LearningModalitiesScholarshipMedical educationDisciplinePsychologyTeaching staffSociologyPedagogyTeaching methodTeaching and learning centerPolitical scienceMedicineSocial science

Abstract

fetched live from OpenAlex

This article reports on a mixed-methods study examining the ways in which faculty and staff engage with the Scholarship of Teaching and Learning (SoTL) at a medium-sized research-intensive university in southern Ontario, Canada. Survey data was collected from fifty-six faculty and staff respondents, along with eight faculty completing follow-up semi-structured interviews. We found respondents used multiple engagement modalities to stay informed on SoTL literature, carry out SoTL research, and disseminate their findings. Barriers to SoTL participation include lack of dedicated time, limited formal SoTL training, and inexperience with different disciplinary norms found in SoTL articles. Participants emphasized the importance of collaborative SoTL inquiry, highlighting in particular the benefits of partnering with students on scholarly projects. Additionally, participants underscored the importance of implementing evidence-based teaching strategies. Our findings mirror trends in the literature regarding SoTL engagement activities, barriers to participation, and faculty perceptions of SoTL. This study contributes novel insight into the ways faculty choose to engage with SoTL and common obstacles, as well as suggestions for how teaching and learning centers can use engagement data to better support faculty and staff SoTL scholars.

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)0.030
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.156
GPT teacher head0.476
Teacher spread0.320 · 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 designObservational
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

Citations2
Published2023
Admission routes2
Has abstractyes

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