Blended learning across universities in a South–North–South collaboration: a case study
Bibliographic record
Abstract
BACKGROUND: Increased health research capacity is needed in low- and middle-income countries to respond to local health challenges. Technology-aided teaching approaches, such as blended learning (BL), can stimulate international education collaborations and connect skilled scientists who can jointly contribute to the efforts to address local shortages of high-level research capacity. The African Regional Capacity Development for Health Systems and Services Research (ARCADE HSSR) was a European Union-funded project implemented from 2011 to 2015. The project consortium partners worked together to expand access to research training and to build the research capacity of post-graduate students. This paper presents a case study of the first course in the project, which focused on a meta-analysis of diagnostic accuracy studies and was delivered in 2013 through collaboration by universities in Uganda, Sweden and South Africa. METHODS: We conducted a mixed-methods case study involving student course evaluations, participant observation, interviews with teaching faculty and student feedback collected through group discussion. Quantitative data were analysed using frequencies, and qualitative data using thematic analysis. RESULTS: A traditional face-to-face course was adapted for BL using a mixture of online resources and materials, synchronous online interaction between students and teachers across different countries complemented by face-to-face meetings, and in-class interaction between students and tutors. Synchronous online discussions led by Makerere University were the central learning technique in the course. The learners appreciated the BL design and reported that they were highly motivated and actively engaged throughout the course. The teams implementing the course were small, with individual faculty members and staff members carrying out many extra responsibilities; yet, some necessary competencies for course design were not available. CONCLUSIONS: BL is a feasible approach to simultaneously draw globally available skills into cross-national, high-level skills training in multiple countries. This method can overcome access barriers to research methods courses and can offer engaging formats and personalised learning experiences. BL enables teaching and learning from experts and peers across the globe with minimal disruption to students' daily schedules. Transforming a face-to-face course into a blended course that fulfils its full potential requires concerted effort and dedicated technological and pedagogical support.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".