Africa has made unprecedented gains in school enrolment in recent years. In 1999, only 59% of primary school-age children were enrolled in school in Sub-Saharan Africa. By 2016, 80% were enrolled.1
Inputs alone do not improve learning outcomes
Many programmes aiming to improve the quality of education in Africa have been ineffective because they fail to address the needs of all students.
Teachers tend to teach to the top of the class
Despite the success in getting children to school, learning outcomes are still desperately low in many contexts.
Tailoring instruction to the level of the child improves learning outcomes
Multiple TaRL delivery models have been tested
- Tutor- or volunteer-led learning TaRL camps held for periodic bursts of time were effective in Uttar Pradesh, India, in a location with relatively weak government support structures. This model includes local instructors leading TaRL activities for forty days with supplementary support in summer camps.
- Government teacher-led TaRL instruction throughout the school year was effective in Haryana, India, a state with relatively strong government systems. This intervention included a dedicated time for TaRL during the school day and support for teachers through strong mentoring and monitoring.
Interested in learning more about how researchers, policymakers, and implementers conduct research and use rigorous evidence to guide programs and policymaking? Explore these resources:
1) The World Bank, n.d. “Adjusted net enrollment rate, primary (% of primary school age children).” Accessed, September 18, 2018. https://data.worldbank.org/indicator/SE.PRM.TENR?locations=XM&view=chart&year_low_desc=false
2) World Bank. 2018. “World Development Report 2018: Learning to Realize Education’s Promise.” Washington, DC: World Bank.
3)The Abdul Latif Jameel Poverty Action Lab, n.d. “Evaluations.” Accessed, September 18, 2018. https://www.povertyactionlab.org/evaluations?f=field_themes:2
4) Glewwe, Paul, Michael Kremer, Sylvie Moulin, and Eric Zitzewitz. 2004. “Retrospective vs. Prospective Analyses of School Inputs: the Case of Flip Charts in Kenya.” Journal of Development Economics 74(2004): 251-68.
5) Glewwe, Paul, Michael Kremer, and Sylvie Moulin. 2009. “Many Children Left Behind? Textbooks and Test Scores in Kenya.” American Economic Journal: Applied Economics 1(1): 112-35.
6) Sabarwal, Shwetlena, David K. Evans, and Anastasia Marshak. “The permanent input hypothesis: the case of textbooks and (no) student learning in Sierra Leone.” The World Bank (2014).
7) Barrera-Osario, and Leigh L. Linden. “The Use and Misuse of Computers in Education: Evidence from a Randomized Controlled Trial of a Language Arts Program.” Working Paper, Columbia University, 2009.
8) Beasley, Elizabeth, and Elise Huillery. “Willing but Unable? Short-Term Experimental Evidence on Parent Empowerment and School Quality.” The World Bank Economic Review (2016): lhv064.
9) Blimpo, Moussa Pouguinimpo, David Evans, Nathalie Lahire. 2015. Parental human capital and effective school management : evidence from The Gambia. Policy Research working paper; no. WPS 7238
10) Borkum, Evan, Fang He, and Leigh Linden. “The Effects of School Libraries on Language Skills: Evidence from a Randomized Controlled Trial in India.” Working Paper, March 2013.
11) Banerji, Rukmini, and Madhav Chavan. “Improving literacy and math instruction at scale in India’s primary schools: The case of Pratham’s Read India program.” Journal of Educational Change 17, no. 4 (2016): 453-475
12) Banerjee, Abhijit, Shawn Cole, and Esther Duflo. 2007. “Remedying Education: Evidence from Two Randomized Experiments in India.” The Quarterly Journal of Economics 122.3 (2007): 1235-1264.
13) Banerjee, Abhijit V., Rukmini Banerji, Esther Duflo, Rachel Glennerster, and Stuti Khemani. “Pitfalls of participatory programs: Evidence from a randomized evaluation in education in India.” The World Bank (2008)
14) Banerjee, Abhijit, Rukmini Banerji, James Berry, Esther Duflo, Harini Kannan, Shobhini Mukherji, Marc Shotland, and Michael Walton. “Mainstreaming an effective intervention: Evidence from randomized evaluations of “Teaching at the Right Level” in India.” No. w22746. National Bureau of Economic Research, 2016.
15) Duflo, Esther, Pascaline Dupas, and Michael Kremer. 2011. “Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya.” American Economic Review 101(5): 1739-74.
16) Innovations for Poverty Action. 2018. “Evaluating the Teacher Community Assistant Initiative.” Accessed July 19, 2018. https://www.poverty-action.org/study/evaluating-teacher-community-assistant-initiative-ghana
17) Duflo, Annie. 2017. “TaRL Webinar Series: Session 1.” Accessed July 24, 2018. https://www.povertyactionlab.org/sites/default/files/event/TaRL-Webinar-Session-1.pdf
18) Muralidharan, Karthik, Abhijeet Singh, and Alejandro J. Ganimian. “Disrupting education? Experimental evidence on technology-aided instruction in India.” No. w22923. National Bureau of Economic Research, 2016.
19) Cabezas, Verónica, José I. Cuesta, and Francisco A. Gallego. “Effects of short-term tutoring on cognitive and non-cognitive skills: Evidence from a randomized evaluation in Chile.” Santiago, Chile (2011).
20) Fryer Jr, Roland G., and Meghan Howard Noveck. “High-Dosage Tutoring and Reading Achievement: Evidence from New York City.” No. w23792. National Bureau of Economic Research, 2017. 21. Bates, Mary Ann, and Rachel Glennerster. “The generalizability puzzle.” Stanford Social Innovation Review 15, no. 3 (2017).