Theory and Practice: Improving Retention Performance through Student Modeling and System Building

• Main Author: Xiong, Xiaolu
• Other Authors: Piotr Mitros, Committee Member
• Format: Others
• Published: Digital WPI 2017
• Subjects: Educational data mining; Spacing effect; Recurrent neural networks; Deep learning; Performance factors analysis; Knowledge retention; Intelligent tutoring system; Student modeling; Robust learning; Knowledge tracing
• Online Access: https://digitalcommons.wpi.edu/etd-dissertations/139; https://digitalcommons.wpi.edu/cgi/viewcontent.cgi?article=1138&context=etd-dissertations

The goal of Intelligent Tutoring systems (ITSs) is to engage the students in sustained reasoning activity and to interact with students based on a deep understanding of student behavior. In order to understand student behavior, ITSs rely on student modeling methods to observes student actions in the tutor and creates a quantitative representation of student knowledge, interests, affective states. Good student models are going to effectively help ITSs customize instructions, engage student's interest and then promote learning. Thus, the work of building ITSs and advancing student modeling should be considered as two interconnected components of one system rather than two separate topics. In this work, we utilized the theoretical support of a well-known learning science theory, the spacing effect, to guide the development of an ITS, called Automatic Reassessment and Relearning System (ARRS). ARRS not only validated the effectiveness of spacing effect, but it also served as a testing field which allowed us to find out new approaches to improve student learning by conducting large-scale randomized controlled trials (RCTs). The rich data set we gathered from ARRS has advanced our understanding of robust learning and helped us build student models with advanced data mining methods. At the end, we designed a set of API that supports the development of ARRS in next generation ASSISTments platform and adopted deep learning algorithms to further improve retention performance prediction. We believe our work is a successful example of combining theory and practice to advance science and address real- world problems.