ODL and the impact of digital divide on information access in Botswana
Bibliographic record
Abstract
Open and distance learning (ODL) has created room for the emergence of virtual education. Not only are students found everywhere and anywhere undertaking their studies and earning their degrees, but geographical boundaries between nations no longer appear to have much relevance. As the new education paradigm irretrievably alters the way teaching and learning is conducted, the application of modern educational ICTs has a major role to play. With students of transnational or cross-border education dispersed into various nooks and crannies of Botswana, many others enlist for the “home-baked” distance learning programmes from their diverse locations. Like the face-to-face conventional students, distance learners also have information needs which have to be met. But blocking the distance learners’ realization of their information needs is the digital divide, which further marginalizes the underclass of “info-poor.” The survey method was used, and a questionnaire administered to 519 students of four tertiary level distance teaching institutions that met the criteria set for the study yielded a 70.1% response rate. The results showed that while the Government of Botswana has made considerable effort to ensure country-wide access to ICT, which now constitutes an effective instrument for meeting information needs, a number of problems still exist. The factors impeding easy access are unearthed. The findings of an empirical study portraying some learners as information-rich and others, information-poor, and the consequence of distance learners studying on both sides of the digital divide, are discussed. Suggestions on bridging the digital divide are offered.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".