Digital Capacity Building in Schools: Strategies, Challenges, and Outcomes
Bibliographic record
Abstract
The introduction of digital technologies in educational institutions has gained significant momentum worldwide as societies recognize the central role of education in preparing individuals for a technology-driven world. This paper explores the concept of 'school digital capacity', which encompasses various factors critical to the effective integration of technology into teaching and learning practices. Educational leadership emerges as a cornerstone of this capacity, with leaders playing a critical role in shaping the digital landscape of their institutions. Using a mixed-methods approach, this research explores the perceptions of educational leaders in the context of a large-scale digital education reform project. Key findings highlight the importance of leadership and a coherent digital strategy in improving digital efficacy and teacher engagement with technology. However, challenges are evident, including a lack of clear strategies, inadequate human resource allocation, limited knowledge of digital education and insufficient professional development opportunities. The study highlights the need for improved training programmes for educational leaders to equip them with the necessary digital skills and strategic acumen. Collaborative networks between schools and increased support from ministries of education are recommended to facilitate effective digital integration and capacity development.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".