Modeling the Measurements of the Determinants of ICT Fluency and Evolution of Digital Divide Among Students in Developing Countries—East Africa Case Study
Bibliographic record
Abstract
During the last decade, information and communication technology has brought remarkable changes to the education style of developed countries, especially in the context of online learning materials accessibility. However, in developing nations such as the East African (EA) countries, university students may lack the necessary ICT training to take advantage of e-learning resources productively. Therefore, the comprehension of the key factors behind ICT fluency is a significant concern for this region and all the developing countries in general. This paper applies the Concentration Index and proposes a Logistic Regression based model to discover the key determinants of ICT fluency and to explore the evolution of the digital divide among EA students within the four years of undergraduate studies. To identify the principal determinants, data composing of 1237 participants is collected from three different universities in EA within a one year period. The experimental results indicate that the digital divide among students decreases quite fast from the first year to the fourth year. Regression computational findings show that the key determinants of ICT fluency are the student urban/rural origin, computer ownership, computer experience, class year, and major. The findings provide heuristic implications for developers, practitioners, and policy makers for an improved ICT environment implementation in EA and the developing nations in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".