Sustainability and Scalability of Digital Tools for Learning: ABRACADABRA in Kenya
Bibliographic record
Abstract
This paper explores factors to increase the likelihood that the implementation of ABRACADABRA, a technology-based approach to teaching and learning literacy, endures and expands beyond the initial research. Started as a pilot study in 12 classrooms, the implementation spread to more than 500 primary classrooms over six years in five areas of Kenya. Drawing from research about scalability and sustainability of educational interventions and value-expectancy-cost theory, an exploratory survey was designed to interview a range of actors involved in the software implementation. We used a combination of an a priori and data-driven coding approaches to analyse the narratives. We then built a model exploring the relationship between expectancy-value-cost beliefs and the factors associated with implementation and sustainability. The model explained an important portion of variance in the self-reported intent to use the software with the most significant contributions from policies, professional development, and students. These findings may be useful in the context of low- and medium-income countries where no research-proven principles exist to building sustainable and scalable educational interventions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".