Abstract
On current trends the world will fail to reach the objectives set in the UN’s Sustainable Development Goals for Education by 2030 or even within the 21st century. Changing this trend will require a significant acceleration in learning outcomes. Digital personalised learning (DPL) tools are a potentially cost-effective intervention that can contribute to this acceleration. In particular, the continuous experimentation afforded by these tools through software A/B testing, has considerable potential to create compounding improvements in learning outcomes. This paper provides an overview of EIDU, an educational platform combining student focused DPL content with digital structured pedagogy programmes in public pre-primary schools in Kenya. Collection of student’s longitudinal unsupervised assessment data at scale creates the possibility of learning outcome focused A/B testing. This is a novel contribution to the development and research field as up until now this type of capability has largely been confined to students in high-income environments.
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References
Rodriguez-Segura, D.: Educational technology in developing countries: a systematic review. University of Virginia EdPolicy Works Working Papers, p. 2021 (2020). Accessed 17 Dec 2020
Major, L., Francis, G.A., Tsapali, M.: The effectiveness of technology-supported personalised learning in low-and middle-income countries: a meta-analysis. Br. J. Edu. Technol. 52(5), 1935–1964 (2021)
Angrist, N.: Mapping the global learning crisis. Educ. Next 22(2). (2022)
Levesque, K., Bardack, S., Chigeda, A., Bahlibi, A., Winiko, S.: Two-year RCT of EdTech in Malawi (2022)
Piech, C., et al.: Deep knowledge tracing. In: Advances in Neural Information Processing Systems, p. 28 (2015)
Ngware, M., et al.: Impact evaluation of Tayari school readiness program in Kenya (2018)
UNICEF: The state of the global education crisis: a path to recovery (2021)
Vaquero, L.M., Twomey, N., Dias, M.P., Camplani, M., Hardman, R.: Towards continuous compounding effects and agile practices in educational experimentation. arXiv preprint arXiv:2112.01243 (2021)
Ros, R., Runeson, P.: Continuous experimentation and A/B testing: a mapping study. In: Proceedings of the 4th International Workshop on Rapid Continuous Software Engineering, pp. 35–41, May 2018
Savi, A.O., Ruijs, N.M., Maris, G.K., van der Maas, H.L.: Delaying access to a problem-skipping option increases effortful practice: application of an A/B test in large-scale online learning. Comput. Educ. 119, 84–94 (2018)
Ritter, S., Murphy, A., Fancsali, S.: Curriculum-embedded experimentation. In: Proceedings of the Third Workshop on A/B Testing and Platform-Enabled Research (at Learning@ Scale 2022) (2022)
Friedberg, A.: Introducing EIDU’s solver platform: facilitating open collaboration in AI to help solve the global learning crisis. In: Proceedings of the 23rd International Conference on Artificial Intelligence in Education, AIED 2022, Part II, Durham, UK, 27–31 July 2022, pp. 104–108. Springer International Publishing, Cham, July 2022. https://doi.org/10.1007/978-3-031-11647-6_18
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Friedberg, A. (2023). Can A/B Testing at Scale Accelerate Learning Outcomes in Low- and Middle-Income Environments?. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_119
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DOI: https://doi.org/10.1007/978-3-031-36336-8_119
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