The Contribution of Information and Communication Technology to Social Inclusion and Exclusion during the Appropriation of Open Educational Resources
Bibliographic record
Abstract
The information and communication technology (ICT) comprehends with the adoption and the development of open educational resources (OER) in the educational spheres. The vast existing body of knowledge portrays several positive aspects of ICT, as it is an enabler in various domains. Hence, the combination of ICT and OER negative aspects have been, as yet, under-investigated. This study aimed to investigate both the social inclusion and the social exclusion of ICT with users appropriating of OER in open distance e-learning (ODeL) institutions. The qualitative approach was used to interpret the inclusion and exclusion factors concerned. The Model of Technology Appropriation was applied as a main theoretical underpinning of the study. The study findings show that ICT has both positive and negative impacts on the appropriation of OER. The various impacts are mostly recognisable in those developing countries where inequalities still exist, as some of the findings postulate that the innovation that is enabled through the utilisation of ICT tends to favour a select minority of rich people. For many students, ICT continues to perpetuate social exclusion. ICT innovation, including OER, has yet to fully support societal needs. Instead, it continues to promote the agendas of the global north. The study recommends the development initiatives to close the current gaps which contribute to the social exclusion. For instance, the installation of fibre optic in most deprived townships and villages can assist in eliminating inequalities associated with ICT infrastructure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".