Precision Learning: Data-Driven Experimentation of Learning Theories using Internet-of-Videos
This is a project to study what works to help students learn more effectively in the context of the ASSISTments system. ASSISTments is an online system that provides both assistance to students and real time assessment data to teachers. ASSISTments now supports 100,000 students who have completed more than 12 million mathematics problems. The system uses teacher input and artificial intelligence to provide assistance to students who are attempting to solve mathematics problems. This project will increase the assistance provided by the teacher and machine learning by incorporating video suggestions, such as those produced by the Kahn academy, targeted to the needs of the student. The experimentation will take content from three Open Educational Resource textbooks that are openly licensed and free to schools.
More specifically, the researchers will identify a large collection of videos that address mathematics skills in the textbooks and will extract features of these videos including language complexity, speaking rate, and other features. These videos and features will be checked by both teachers and through a Mechanical Turk process for usability before they are presented to students. Additionally, the project will develop a suite of novel technologies for precision learning including fine grained video feature extraction, student feature learning from heterogeneous raw data, causal modeling, and fairness aware and causal relationship enhanced optimized personalized recommendation. The research will advance theoretical understanding of fundamental issues related to personalized learning and will enable data-driven experimentation of learning theories. Causal modeling will enable the researchers to learn the features of video that are correlated with learning effectiveness.
This project is part of the National Science Foundation's Harnessing the Data Revolution Big Idea activity and is co-funded by the Division of Undergraduate Education and the Division of Research on Learning.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.