A Design Based Research Framework for Implementing a Transnational Mobile and Blended Learning Solution
Bibliographic record
Abstract
The article proposes a modified Design-Based Research (DBR) framework which accommodates the various socio-cultural factors that emerged in the longitudinal PA-HELP research study at Central University College (CUC) in Ghana, Africa. A transnational team of stakeholders from Ghana, Canada, and the USA collaborated on the development, implementation, and subsequent modification of the DBR framework. The recommended framework is a result of lessons learned during this project in Ghana and as such, it is shaped by the need to be responsive to the local cultural and contextual contingencies. The article offers practical recommendations on the implementation of a mobile learning project in a cross-cultural setting, and provides a discussion of the salient cultural factors and the corresponding culturally-sensitive adaptations needed in the design research process. The Cross-Culture Design-Based Research (CC-DBR) framework is proposed to inform future transcultural m-learning studies.
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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 | 0.101 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".