Using Technology to Shift Education Paradigms in Low-Resource Environments
Bibliographic record
Abstract
As innovative and exponential technologies make their way into development projects and humanitarian aid interventions, pioneers are just starting to codify and publish their best practices, for example UNICEF’s Child-Friendly Technology Framework. Code Innovation designed and lead the Connecting Classrooms project over seven years, bringing technology and education innovations to secondary school students, out-of-school youth and young adults in eleven countries around sub-Saharan Africa. The majority of participants had never experienced being connected to the Internet and there were numerous and ongoing challenges. Using collaborative teaching methodologies and a group learning approach, the program brought young people and their teachers or adult facilitators through a blended learning curriculum around key issues of shared global concern. This paper seeks to expand on lessons learned from the program to make recommendations for others to get the greatest leverage out of technology-supported education initiatives. As there is relatively little research published around multi-year technology for education projects in developing countries to date, this article strives to offer some best practices and lessons learned that will guide similar initiatives in the future.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".