Challenges of Blended Learning in Refugee Camps: When Internet Connectivity Fails, Human Connection Succeeds
Bibliographic record
Abstract
In this paper, we studied the implementation of a course on global history as a blended section of a massive open online course (MOOC) and the learning challenges faced by the students in three crisis contexts: Azraq refugee camp (Jordan), Kakuma refugee camp (Kenya), and Amman urban refugee center (Jordan). The results showed that poor Internet connection in the camps severely impacted both students’ and instructors’ experience of the course. In the context of chronic Internet connectivity issues, the instructors had difficulties assessing their students’ needs and challenges. The results also showed that in light of these intermittent connection problems, the collaborative learning environment helped students navigate the challenges of a blended course. Also, the onsite visit by the online tutors and the face-to-face interactions that resulted from it had a noticeable impact on the human dynamics of the course by allowing instructors to provide targeted solutions to students’ problems as well as by building rapport between the students and the instructional team.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".