Creating Capacity for Digital Transformation of Education:
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".