Reimagining Computing Education through ODL: Case Studies from Open University Malaysia (OUM)
Bibliographic record
Abstract
We are pleased to present Reimagining Computing Education through ODL: Case Studies from Open University Malaysia (OUM)—a curated collection of practical narratives, pedagogical innovations, and institutional insights on inclusive, equitable, and accessible computing education through Open and Distance Learning (ODL). This book was developed in response to the growing need for quality, flexible education in the digital age. The chapters highlight how OUM is addressing the challenges of the Fourth Industrial Revolution through adaptive learning technologies, AI-driven feedback, digital nudging, and human-centred approaches—particularly tailored to adult learners in diverse contexts. The volume also features a guest-authored chapter on computing education in the era of Artificial Intelligence and is enriched by contributions from across the university—from the Vice-Chancellor to academic staff—showcasing a unified commitment to learner-centred and future-ready education. What unites these contributions is a belief that technology becomes transformative only when guided by inclusive design and a focus on empowering learners. As editors, we are proud that this volume reflects both institutional vision and the realities of practice, offering valuable perspectives for educators, researchers, and decision-makers in the evolving landscape of digital learning. Published in conjunction with OUM’s 25th anniversary, this book stands as a timely milestone celebrating the university’s ongoing commitment to humanising digital education and leading innovation in ODL. Editors Jane-Frances Agbu Commonwealth of Learning (COL), CANADA Nantha Kumar Subramaniam Open University Malaysia (OUM), MALAYSIA
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".