Scalable Methods for Customized Digital Learning is a toolkit that provides education application developers with algorithms for assessment of student proficiency. Precise assessment of students’ strengths and weaknesses makes it possible to customize learning modules, find the right balance between easy and difficult materials, and substantially improve learning outcomes. These algorithms will make this type of personalization possible, and the accompanying toolkit includes notebooks that guide developers through comparing methods, evaluating performance, and creating teacher dashboards.
Susan Athey
Professor, Stanford Graduate School of Business
Susan Athey is a member of the National Academy of Science, and is the recipient of the John Bates Clark Medal. Her research focuses on the economics of digitization, marketplace design, and the intersection of econometrics and machine learning. She leads the Golub Capital Social Impact Lab, a lab that leverages social science research and technology to improve social sector effectiveness and has several collaborations with educational technology providers.
