Challenges to web-based learning in pharmacy education in Arabic language speaking countries
Bibliographic record
Abstract
Web-based learning and web 2.0 tools which include new online educational technologies (EdTech) and social media websites like Facebook® are playing crucial roles nowadays in pharmacy and medical education among millennial learners. Podcasting, webinars, and online learning management systems like Moodle® and other web 2.0 tools have been used in pharmacy and medical education to interactively share knowledge with peers and students. Learners can use laptops, iPads, iPhones, or tablet devices with a stable and good Internet connection to enroll in many online courses. Implementation of novel online EdTech in pharmacy and medical curricula has been noticed in developed countries such as European countries, the US, Canada, and Australia. However, these trends are scarce in the majority of Arabic language speaking countries (ALSC), where traditional and didactic educational methods are still being used with some exceptions seen in Palestine, Kuwait, Jordan, Saudi Arabia, Egypt, UAE, and Qatar. Although these new trends are promising to push pharmacy and medical education forward, major barriers regarding adaptation of E-learning and new online EdTech in Arab states have been reported such as higher connectivity costs, information communication technology (ICT) problems, language barriers, wars and political conflicts, poor education, financial problems, and lack of qualified ICT-savvy educators. More research efforts are encouraged to study the effectiveness and proper use of web-based learning and emerging online EdTech in pharmacy education not only in ALSC but also in developing and developed countries.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".