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Record W3113707496 · doi:10.21432/cjlt27944

Institutional Perspectives on Faculty Development for Digital Education in Canada

2020· article· en· W3113707496 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsSimon Fraser UniversityRoyal Roads University
Fundersnot available
KeywordsHigher educationProfessional developmentFaculty developmentWork (physics)SociologyPublic relationsPedagogyMedical educationPolitical scienceEngineering

Abstract

fetched live from OpenAlex

As digital education at the post-secondary level continues to grow, robust professional development that prepares faculty to teach in online and blended settings is necessary. In this study we analyze open-ended comments from the Canadian Digital Learning Research Association’s annual survey of Canadian post-secondary institutions (2017-2019) to deepen our understanding of faculty training and support for digital education as articulated by higher education institutions. We find that 1) digital education orientation or on-boarding processes for faculty vary widely; 2) institutions employ an extensive array of professional development practices for digital education; 3) institutions report culture change, work security, and unclear expectations as challenges in providing digital education training and support; and 4) institutions articulate aspirations and hopes around professional development investments in order to build digital education capacity. These findings have significant implications for research and practice and we describe these in the article.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.834
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.031
GPT teacher head0.305
Teacher spread0.275 · 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