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Record W4375853726 · doi:10.51357/jei.v4i1.191

Higher Education Faculty Supports for to the Transition to Online Teaching during the COVID-19 Pandemic

2023· article· en· W4375853726 on OpenAlexaffabout
Rob Power, Robin Kay

Bibliographic record

VenueJournal of Educational Informatics · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsOntario Tech UniversityCape Breton University
Fundersnot available
KeywordsOnline teachingPandemicCoronavirus disease 2019 (COVID-19)Higher educationDistance educationTransition (genetics)Faculty developmentPerceptionMedical educationProfessional developmentSociologyPsychologyPedagogyPublic relationsPolitical scienceMathematics educationMedicine

Abstract

fetched live from OpenAlex

Canadian higher education institutions closed physical campuses in early 2020. It transitioned to online teaching and student service delivery because of the COVID-19 pandemic. For many faculty members and institutions unfamiliar with online teaching, this transition meant widespread innovation in digital technologies and pedagogical practices. While necessity created a perception of the usefulness of digital tools, faculty still needed to develop their technical skills and online teaching approaches. This research study found that faculty from two Canadian universities drew upon a combination of formal and informal support networks and resources to increase their technological self-efficacy. Faculty also found that formal professional development was most helpful when it focused on online teaching approaches rather than specific technical functionality. The barriers to innovation and changes to faculty use of digital tools and pedagogies point to recommendations for higher education institutions that must transition to online delivery.

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)0.003
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.133
GPT teacher head0.464
Teacher spread0.331 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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