Mobile Learning During School Disruptions in Sub-Saharan Africa
Bibliographic record
Abstract
School closures due to teacher strikes or political unrest in low-resource contexts can adversely affect children's educational outcomes and career opportunities. Phone-based educational technologies could help bridge these gaps in formal schooling, but it is unclear whether or how children and their families will use such systems during periods of disruption. We investigate two mobile learning technologies deployed in Sub-Saharan Africa: a text message-based application with lessons and quizzes adhering to the national curriculum in Kenya (N = 1.3m), and a voice-based platform for supporting early literacy in Côte d’Ivoire (N = 236). We examine usage and beliefs surrounding unexpected school closures in each context via system log data and interviews with families about their motivations and methods for learning during the disruption. We find that mobile learning is used as a supplement for formal and informal schooling during disruptions with equivalent or higher intensity, as parents feel responsible to ensure continuity in schooling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.023 |
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; both teacher heads agree on what is shown here.
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".