Engaging Academics in Training in Information Communication Technology (ICT): An African Experience with special focus on Uganda
Bibliographic record
Abstract
Training academics in ICT utilisation has been widely regarded as a key to successful staff development practice in higher education and, hence, considerable efforts and resources have been invested into ICT training programmes. However, little is known about the extent to which higher education policymakers in Africa give attention to the issue of preparing academics for ICT usage. This paper reports the findings of a research study exploring the utilisation of ICT in Uganda, Africa. Qualitative research methods were employed and data were collected through interviews, observations and open-ended questionnaires. The findings indicate that the university has put resources into the development of ICT policies, including policies related to training academics. Subsequently, academic staff were trained in ICT utilisation. However, little attention appears to have been given to the issue of engaging academics in ICT training. The paper also highlights the importance of engaging academics in ICT training for successful ICT staff-development outcomes.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".