Managing Digital Transformation in African Higher Education Institutions: Challenges and Opportunities
Bibliographic record
Abstract
Digital transformation in African higher education institutions is a complex and multifaceted process that requires deliberate investment, meticulous planning, and collaboration. This article highlights the challenges African higher education institutions face in implementing digital transformation in the context of digital transformation into six groups: digital infrastructure, leadership, financial costs, diversity, equity and inclusion, policy and framework, and external factors. The research underscores the challenges of African higher education institutions in integrating digital transformation into digital infrastructure. Digital infrastructure and leadership are critical elements in achieving digital transformation. Digital technologies can provide accessible, high-quality education that contributes to societal development and benefits all African students and staff. This approach optimises the positive impact of digital technologies by ensuring that the benefits are equitably shared throughout the continent and improving learning outcomes, accessibility, and the development and competitiveness of Africa's institutions globally.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".