A Holistic Approach to Building Institutional and Faculty Capacity for Digital Education and Digital Pedagogy
Bibliographic record
Abstract
Digital technology continues to create new pathways in disrupting the teaching and learning exchange. Higher education institutions must not only contend with ongoing demographic, financial, and political shifts, but also have to respond to changing market demands for flexible delivery, greater access, and increased student expectations. The absence of a robust and comprehensive institutional response to these disruptions, leads to uneven technology integration on the part of faculty resulting in inconsistent learning experience for students. This Dissertation-in-Practice (DiP) articulates a holistic approach to building institutional and faculty capacity for digital education and pedagogy at a public college in a Canadian urban center. Working within an interpretivist epistemology, it leverages an adaptive and distributed leadership approach coupled with a constructivist, critical, and experiential grounding to education, to address institutional gaps in educational technology integration. Faculty capacity building initiatives constitute the core of a change implementation plan that seeks to identify and remove affective, pedagogic, and organizational barriers to technology integration while developing faculty’s technological and pedagogic knowledge base and disposition as 21st century educators. This is further supported by a phased change path designed to holistically build institutional capacity in supporting seamless integration of digital education practices across all instructional modalities. Change agents draw on socially constructed, collaborative, and inclusive techniques consistent with the leadership approach and a change model that also draws on psychosocial factors to engage participants in and through an organizational digital transformation change process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".