Reflection on MOOC Design in Palestine
Bibliographic record
Abstract
This paper will share Discover Palestine, an interdisciplinary Massive Online Open Course (MOOC) and the first MOOC to be created in Palestine, by the E-Learning Centre, Faculty from the Department of Geography, and Department of Tourism and Archaeology from An-Najah National University in Palestine. The paper traces the process of development of the Discover Palestine MOOC from its early inception as a cross institutional online course, to its current delivery and engagement with a global and diverse group of learners. Using a descriptive case study design and thematic analysis, the reflective experiences of four course team members involved as facilitators/designers in the design and delivery of the MOOC are shared. Three key themes, namely, “Informing pedagogies including delivery methods,” “A commitment to a national cause,” and “Teacher presence,” are presented and contextualized with data evidence. The findings share not only the hurdles the Discover Palestine team had to navigate during the MOOC development, but more importantly, how academic collaborations promoting open education practices offer powerful tools for the reciprocal exchange of knowledge, not least in shifting mindsets, and offering opportunities for shared fields of understanding to be realized in revealing creative, cultural practices, as well as lost histories.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".