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Record W3161300219 · doi:10.19173/irrodl.v22i2.5108

An Analysis of Digital Education in Canada in 2017-2019

2021· article· en· W3161300219 on OpenAlexaffvenueabout
George Veletsianos, Charlene VanLeeuwen, Olga Belikov, Nicole Johnson

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2021
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversity of Prince Edward IslandRoyal Roads University
Fundersnot available
KeywordsRigourElectronic publishingHigher educationEducational technologyDigital learningThe InternetDistance educationOpen educationSociologyPedagogyComputer scienceLibrary scienceWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

Digital education refers to in-person, blended, and fully online learning efforts, as well as attempts to capture a wide range of teaching and learning contexts which make use of digital technology. While digital education is pervasive in Canada, pan-Canadian data on digital education are relatively scarce. The Canadian Digital Learning Research Association/Association Canadienne de Recherche sur la Formation en Ligne conducted pan-Canadian surveys of higher education institutions (2017-2019), collecting data on the digital education landscape and publishing annual reports of its results. Previous analyses of the data have used quantitative approaches. However, the surveys also collected responses to open-ended questions. In this study, we report a systematic analysis of qualitative data exploring the digital education landscape in Canada and its changes over time. Findings shed light on the growth of digital education, the situated and multidimensional nature of digital education, the adoption of openness, quality, and rigour, and the development of alternative credentials.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.045
GPT teacher head0.439
Teacher spread0.394 · 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 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

Citations19
Published2021
Admission routes3
Has abstractyes

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