PIs: Min Chi
North Carolina State University
Interactive Learning Environments (ILEs) hold great promise for improving student performance in STEM education. While, traditionally, such systems have focused on teaching students subject matter, an equally important facet is to teach them how become better learners. The objective of this project is to develop an integrated research and education program that investigates the how to improve decision-making in ILEs and the impact of integrating ILE decisions with user-initiated decisions. The primary research goal lies in creating and improving ILEs directly from data, using state-of-the-art machine-learning techniques. The primary educational goal lies in preparing students to act independently and make good choices in new situations for which they do not immediately know how to act. The work will contribute in enabling ILEs to be more effective by improving student performance in STEM domains, by teaching students to make effective pedagogical decisions, and by making the decisions made by such systems more transparent to both teachers and domain experts.
The project will develop and empirically evaluate a general decision-making framework across three ILE or STEM domains. The project naturally integrates both the research and education goals. More specifically, the project will 1) advance research on the application of Reinforcement Learning by adapting it to make hierarchical decisions similar to those of human experts; 2) advance the understanding of ILEs and Reinforcement Learning algorithms by inducing compact policies that highlight key decisions and that can inform one?s understanding of the educational domain; and 3) close the loop by using data-driven policies to support student decision-making and eventually improve their long-term problem-solving abilities through hybrid human-machine interactive decision making in vivo experimentation.