Can A/B Testing at Scale Accelerate Learning Outcomes in Low- and Middle-Income Environments?
Resource type
Conference Paper
Authors/contributors
- Friedberg, Aidan (Author)
- Wang, Ning (Editor)
- Rebolledo-Mendez, Genaro (Editor)
- Dimitrova, Vania (Editor)
- Matsuda, Noboru (Editor)
- Santos, Olga C. (Editor)
Title
Can A/B Testing at Scale Accelerate Learning Outcomes in Low- and Middle-Income Environments?
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.
Date
2023
Proceedings Title
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky
Place
Cham
Publisher
Springer Nature Switzerland
Pages
780-787
Series
Communications in Computer and Information Science
Language
en
ISBN
978-3-031-36336-8
Library Catalogue
Springer Link
Citation
Friedberg, A. (2023). Can A/B Testing at Scale Accelerate Learning Outcomes in Low- and Middle-Income Environments? In N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, & O. C. Santos (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (pp. 780–787). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-36336-8_119
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