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Record W4403034383 · doi:10.18357/otessac.2024.4.1.421

Creating Capacity for Digital Transformation of Education:

2024· article· en· W4403034383 on OpenAlexaffvenueabout
Valerie Irvine, Mariel Miller, Colin Madland

Bibliographic record

VenueThe Open/Technology in Education Society and Scholarship Association Conference · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsUniversity of VictoriaThompson Rivers University
Fundersnot available
KeywordsTransformation (genetics)Digital transformationCapacity developmentComputer scienceEnvironmental scienceWorld Wide WebEnvironmental resource managementChemistry

Abstract

fetched live from OpenAlex

Educational institutions and training programs have faced an accelerated transformation toward the integration of technology. However, it is unclear whether the capacity to train qualified personnel to support this digital transformation in education. In this session, we review university websites across Canada looking at the availability of online PhD programs in Education Technology. Findings indicate only one is offered online only (5% or 1 out of 20) and two provide students with the possibility to study in a blended format for their PhD program, which is 10% (2 out of 20) of all Canadian universities that have PhD programs. Only 5% (1 out of 20) of Canadian institutions provide a PhD program in educational technology; however, this is offered on campus only. While this review excludes the EdD pathway, we did find two EdD programs in educational technology that could be accessed entirely online and one blended program. As technological and conceptual shifts of entire sectors that prioritize digital learning and digital literacy (e.g., B.C. Digital Learning Strategy), there is significant demand for PhD qualified individuals to lead or execute these initiatives. As such, there needs to be more discussion about how to make PhD study more accessible, specifically in educational technology.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.058
GPT teacher head0.340
Teacher spread0.281 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes3
Has abstractyes

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