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This is part of our coronavirus (COVID-19) and EdTech series. Written by Joe Watson, research assistant at the University of Cambridge One of the many consequences of COVID-19 is that more than a billion caregivers will soon face the stark (and often scary) realisation that they must become their children’s teachers. This will be particularly difficult in low-income contexts where…
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This Rapid Evidence Review (RER) gives an overview of the recent literature concerning how the use of educational television might support children’s learning in low- and middle-income countries (LMICs). In this review, educational television is defined as television designed with research-based knowledge of how children use and understand television that systematically incorporates academic or social curricula into its content. In low-income contexts, educational television material could...
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Previous studies have often demonstrated that educational television can have a positive effect on learning outcomes in low-income countries when delivered in controlled settings. However, existing research in low-resource contexts has scarcely considered the association between child outcomes and viewing in usual environments (ie, at their home, a friend’s home or a relative’s home). This lack of research is striking, as evidence from controlled settings might provide limited information on...
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This paper contributes to the scarce literature on factors affecting EdTech use in households. These factors were considered through exploratory mixed-methods analyses of cross-sectional data on Kenyan girls and caregivers, captured during the COVID-19 pandemic. Quantitative analysis of the child dataset (n = 544) suggested the importance of both structural factors—such as technology hardware availability—and non-structural factors—including caregiver permission. Findings were supported by a...
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This paper contributes to the scarce literature on factors affecting EdTech use in households. These factors were considered through exploratory mixed-methods analyses of cross-sectional data on Kenyan girls and caregivers, captured during the COVID-19 pandemic. Quantitative analysis of the child dataset (n = 544) suggested the importance of both structural factors—such as technology hardware availability—and non-structural factors—including caregiver permission. Findings were supported by a...
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