Title page for ETD etd-011013-004307

Document Type	thesis
Author Name	Qiu, Yumeng
Email Address	ymqiu at wpi.edu
URN	etd-011013-004307
Title	Leveraging Influential Factors into Bayesian Knowledge Tracing
Degree	MS
Department	Computer Science
Advisors	Neil T. Heffernan, Advisor Charles Rich, Reader
Keywords	Linear Regression Bayesian Knowledge Tracing Student Model Intelligent Tutoring System
Date of Presentation/Defense	2013-01-03
Availability	mixed
Abstract Predicting student performance is an important part of the student modeling task in Intelligent Tutoring System (ITS). The state-of-art model for predicting student performance - Bayesian Knowledge Tracing (KT) has many critical limitations. One specific limitation is that KT has no underlying mechanism for memory decay represented in the model, which means that no forgetting is happening in the learning process. In addition we notice that numerous modification to the KT model have been proposed and evaluated, however many of these are often based on a combination of intuition and experience in the domain, leading to models without performance improvement. Moreover, KT is computationally expensive, model fitting procedures can take hours or days to run on large datasets. The goal of this research work is to improve the accuracy of student performance prediction by incorporating the memory decay factor which the standard Bayesian Knowledge Tracing had ignored. We also propose a completely data driven and inexpensive approach to model improvement. This alternative allows for researchers to evaluate which aspects of a model are most likely to result in model performance improvements based purely on the dataset features that are computed from ITS system logs.
Files	Yumeng_Qiu_ms_thesis.pdf indicates that a file or directory is accessible from the WPI campus network only.

Browse by Author | Browse by Department | Search all available ETDs