Personalized Education at Scale
This addresses the problem of educational inequity and logistical challenges in tailoring learning to individual students' needs, though it appears incremental as it builds on existing technologies without specifying novel breakthroughs.
The paper tackles the challenge of providing personalized education at scale, which traditionally relies on expensive expert intervention, by proposing to leverage reinforcement learning and other emerging technologies to adapt educational presentations using large-scale student data from MOOCs and increased course sizes.
Tailoring the presentation of information to the needs of individual students leads to massive gains in student outcomes~\cite{bloom19842}. This finding is likely due to the fact that different students learn differently, perhaps as a result of variation in ability, interest or other factors~\cite{schiefele1992interest}. Adapting presentations to the educational needs of an individual has traditionally been the domain of experts, making it expensive and logistically challenging to do at scale, and also leading to inequity in educational outcomes. Increased course sizes and large MOOC enrollments provide an unprecedented access to student data. We propose that emerging technologies in reinforcement learning (RL), as well as semi-supervised learning, natural language processing, and computer vision are critical to leveraging this data to provide personalized education at scale.