Digital Transformation Principles Driving Journeys toward Educational Resilience
Bibliographic record
Abstract
The implementation, support, and ultimate success of digitally-informed innovations to teaching and learning practices requires focused intentionality whose approach is grounded in academic rigour, practical experience and organizational maturity. The success of technology-supported innovation in higher-education teaching and learning practices, driven by external factors like COVID and the resultant economic challenges, will be explored in relation to teaching faculty and administrative leaders developing and maintaining positive motivation towards change including addressing major organizational challenges. // Innovation, Change and Transformation // As Senior Instructor Emeritus, Ron Murch has more than 45 years of experience with the University of Calgary’s Haskayne School of Business. Ron maintains that digital transformation requires faculty members to adopt the requisite innovations in practice and technologies as imagined by the Technology Acceptance Model and expanded upon by two key principles - each new practice or technology must have recognizable and positive value for the individual who is changing; and it cannot be too difficult for the adopter to work with. // Guiding Principles // Dr. Peter Chatterton is a Chartered Physicist and digital innovator. He worked in roles such as critical friend, evaluator and change management consultant with the UK Government’s multi-million £s HEI digital innovation and transformation programmes during 2000-2020. From this experience, Peter asserts that HEIs can be both creative and effective at digital innovation. However, scaling-up and embedding such innovations to build long-term resilience and effect digital transformation across the institution invariably faces numerous challenges. These are explored through the lens of seven key guiding principles for digitally transforming learning programmes for open and flexible learning. // Commitment and Motivation // Michael Barr is Chief Information Officer at the Southern Alberta Institute of Technology in Calgary, Alberta, and a doctoral student in higher education management at the University of Bath, UK. Building on a theory of behavior in organizations, Michael explores the motivation process and its impact on the construction of strategic plans and the organization’s ability to deliver successful outcomes. He draws upon 28 years’ of IT practitioner experience to ground his scholarly work with practical advice and considerations for undertaking digital transformation of teaching and learning practices.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".