Experience with a Massive Open Online Course in Rural Rwanda
Bibliographic record
Abstract
<p class="Style2">The growing utilization of massive open online courses (MOOCs) is opening opportunities for students worldwide, but the completion rate for MOOCs is low (Liyanagunawardena, Adams, &amp; Williams, 2013). Partners In Health (PIH) implemented a “flipped” MOOC in Rwanda that incorporated in-class sessions to facilitate participant completion.</p><p class="Style2">In October 2013, PIH invited its employees, as well as those at the Ministry of Health, to participate in an online MOOC. Each site had at least one volunteer facilitator who accompanied participants throughout the course by providing course materials and facilitating the understanding of the online material during the weekly class sessions. Following the conclusion of the course, all participants were asked to complete an online survey.</p><p class="Style2">A total of 38 out of 62 registered participants completed the survey and of these 38 participants, 20 (52.6%) successfully finished the course. The number of in-person sessions attended was significantly associated with course completion (<em>p </em>&lt; 0.05), and 85% who successfully completed the course attended at least three of seven sessions. Sixteen (80%) participants believed that the completion of this course would help them with career advancement. Half of the participants (19 of 38, 50%) were employed with a position related to research. Other job titles included the following: nurses (4 of 38, 10.5%), a pharmacist (1 of 38, 2.6%), a clinical psychologist (1 of 38, 2.6%), a dentist (1 of 38, 2.6%), and others (10 of 38, 26.3%). The job title was not significantly related to course completion.</p><p class="Style2">Our experience, with a completion rate of over 50%, yields several lessons for incorporating MOOCs into capacity-building programs to leverage the potential of online learning in resource-limited areas.</p>
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".