THE ROLES OF ICT DEVELOPMENT IN OPEN AND DISTANCE EDUCATION: ACHEIVEMENTS, PROSPECTS AND CHALLENGES
Bibliographic record
Abstract
The promises of information and communication technologies (ICT) have driven e-learning in transforming open distance education and thereby advancing the knowledge economy that rested on three arguments: E-learning could expand and widen access to tertiary education and learning; improve the quality of education; and reduce its cost. This article evaluates these three promises based on existing data and evidence. It concludes that the reality has not matched the promises so far in terms of pedagogic innovation. This does not mean that ICT development has not produced any significant positive results in improving the overall learning (and teaching) experience in the institutions and societies where it is implemented. That implies that what will help further to identify the new challenge. ICT development faces will be further research. Obstacles and problems of ICT that could have affected the open educational resource initiatives are yet to be established. The first section of the paper recalls some of the proposed values of e-learning. The second section compares achievements so far and suggests that e-learning could be only at an early stage of realising educational innovation aspirations. The third section highlights the challenges of future developments in e-learning.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".