Institutional Perspectives on Faculty Development for Digital Education in Canada
Bibliographic record
Abstract
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.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.016 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
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".