School Information and Communication Technology in Developing Countries: Essential Considerations for Improvement
Bibliographic record
Abstract
In developing nations, such as many in Africa, providing teachers, students and other school personnel with adequate access to Information and Communications Technology (ICT) remains a daunting task for schools and education supervising/controlling agencies, such as school boards, school districts, Ministries of Education, etc. Although a relatively small portion of total school funding, ICT money is difficult to find and prevailing budget practices in developing countries make necessary changes even more difficult to accomplish. Finding innovative ways to plan, budget, and fund new and existing ICT infrastructure or redirect existing funds into new endeavors remain a daunting challenge to school personnel, especially at a time when new resources for schools appear to be limited. This article discusses considerations teachers and other school personnel, especially in developing nations such as those in Africa, should make regarding planning, budgeting and funding ICT in order to improve teaching and learning in the 21st century environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".