Factors Affecting MOOCs’ Adoption in the Arab World: Exploring Learners’ Perceptions on MOOCs’ Drivers and Barriers
Bibliographic record
Abstract
Despite the potentials promised by MOOCs to democratize education, the adoption rate of MOOCs is still low in certain parts of the world, including the Arab region. Research on MOOCs’ adoption in the Arab region is also limited. To fill this research gap, this quantitative study aims to explore Arab learners’ perceptions on the influential drivers and barriers of MOOCs’ usage. Participants of the study were 821 learners, all from Arabic-speaking countries, who were registered in at least one MOOC offered by one of the biggest Arabic MOOC platforms, Rwaq, during the year 2019. Data were collected using a web-based survey. Results indicated that participants were overall satisfied with their MOOC experience. The main reasons behind their enrolling in MOOCs were: they wanted to learn new things; they thought MOOCs were interesting; they needed credentials for their CVs. The main benefits they cited for participating in MOOCs were: the material learned through MOOCs was valuable to me, the MOOCs’ structure and learning activities were flexible and supported my learning, Participating in MOOCs developed my technological competency. The main MOOC barriers were: problems accessing MOOCs materials due to unreliable internet connection, not having enough time to complete all required tasks and assignments, lacking the proficiency to use different tools in MOOCs, and the instructor was not there to help. Other findings of the study: gender, age, academic levels were not correlated with learners’ satisfaction with MOOCs. On the other hand number of MOOCs previously completed was significantly related to learners’ satisfaction in MOOCs.
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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 | 0.000 | 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 | 0.000 |
| 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".