Learning Data Analytics: Providing Actionable Insights to Increase College Student SuccessThe six-year higher-education graduation rate has been around 59% for over 15 years; less than half of college graduates finish within 4 years. This has high human, economic and societal costs. The National Research Council has identified a critical need to develop innovative approaches to improve student retention, graduation, and workforce-preparedness. The objective of this project is to develop new computational methods to analyze large and diverse types of education and learning data to help (a) discover successful academic pathways for students; (b) improve pedagogy for instructors; and (c) enhance student persistence and retention for institutions. The project outcomes are designed to help students select courses that fit their needs, capabilities, and learning styles, and are likely to lead to (faster) graduation; help instructors to better meet student needs; and give advisors and institutions the analytics needed to improve retention and persistence. The proposed research will produce new dynamical system modeling, collaborative filtering, and multi-task learning methods. Modeling the evolution of a student's knowledge using a dynamical state-space system is a key innovation; the proposed research will develop novel collaborative system identification and collaborative Kalman filtering techniques for grade prediction. Technical innovations include supervised learning approaches for evolving datasets, such as linear and non-linear multi-task learning and collaborative multi-regression models with controlled grouping of the latent variables. These innovations will coalesce into three pilot applications: DegreePlanner for students, CourseInsights for instructors, and StudentWatch for academic advisors. |