Evaluating an e-learning program to strengthen the capacity of humanitarian workers in the MENA region: the Humanitarian Leadership Diploma
Bibliographic record
Abstract
BACKGROUND: The Middle East and North Africa (MENA) region is consistently plagued with humanitarian crises while having little response capacity. Despite their obvious growing need, there exist limited educational opportunities for humanitarian workers to develop their capacity in humanitarian topics. The present study evaluates an online training program, the Humanitarian Leadership Diploma (HLD), which targeted humanitarian workers across the MENA region. METHODS: A mixed-methods design was used, comprising short and long-term quantitative and qualitative data, targeting individual and organizational-level outcomes. A total of 28 humanitarian workers across the MENA region enrolled in the program starting September 2019 until October 2020, 18 of which completed the full diploma. Short-term quantitative data such as knowledge assessments, course evaluations, and reflective commentaries were collected from all learners, whereas long-term qualitative data was collected only from those who completed the full diploma and from peers at their organizations, 6 months after completion. Data was triangulated, analyzed using qualitative content analysis, and reported as themes. RESULTS: The program was overall successful given multiple factors reported by participants such as enhanced knowledge, high satisfaction, and improved practice, with some important challenges being identified. Themes under the strengths category related to (1) online learning, (2) significance of diploma, (3) course content, (4) instructors, (5) transfer of learning into practice, and (6) personal development. Themes under the challenges category related to (1) barriers to applying changes in behavior and performance, (2) engagement and interaction, and (3) pedagogical approach. CONCLUSION: This is one of very few evaluations of locally developed and delivered online learning programs for humanitarian actors in the MENA region. The findings are especially important as they may inform researchers and humanitarian actors looking to design and deliver similar programs in the MENA region or other fragile settings. Key recommendations are discussed in the manuscript, and include to combine synchronous and asynchronous approaches, design concise course materials, limit theoretical pedagogical approaches, ensure topics are contextualized to the region, and consider continuous engagement strategies for learners.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".