Digital literacy in Ugandan teacher education: Insights from a case study
Bibliographic record
Abstract
This case study investigated the relationship between policy and practice with regard to advances in Information and Communication Technology (ICT) in Ugandan teacher education. Our qualitative study, conducted in 2008, focused on the experiences of six language teacher educators in an urban Primary Teachers’ College (PTC). We also drew on insights from an interview with the then Ugandan Minister of ICT, Doctor Ham-Mukasa Mulira and the national ICT policy. Whilst the Minister expressed the hope that technology would transform Ugandan education, our findings suggest that the success of ICT initiatives depends largely on whether local conditions support such initiatives. Despite their enthusiasm for digital technology, the participants were challenged by the expense of Internet connectivity, inadequate training, power outages, and culturally irrelevant material. We suggest that ICT policy should address teacher educators’ use of digital technology across diverse sites, and that innovations such as the eGranary portable digital library might be particularly useful in poorly resourced educational institutions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".