Faculty Advisor or Committee Member

Neil T. Heffernan, Advisor

Faculty Advisor or Committee Member

Ivon Arroyo, Committee Member

Faculty Advisor or Committee Member

Ryan Baker, Committee Member

Co-advisor

Joseph E. Beck

Identifier

etd-091515-182403

Abstract

"Intelligent tutoring systems rely on student modeling to understand student behavior. The result of student modeling can provide assessment for student knowledge, estimation of student¡¯s current affective states (ie boredom, confusion, concentration, frustration, etc), prediction of student performance, and suggestion of the next tutoring steps. There are three focuses of this dissertation. The first focus is on better predicting student performance by adding more information, such as student identity and information about how many assistance students needed. The second focus is to analyze different performance and feature set for modeling student short-term knowledge and longer-term knowledge. The third focus is on improving the affect detectors by adding more features. In this dissertation I make contributions to the field of data mining as well as educational research. I demonstrate novel Bayesian networks for student modeling, and also compared them with each other. This work contributes to educational research by broadening the task of analyzing student knowledge to student knowledge retention, which is a much more important and interesting question for researchers to look at. Additionally, I showed a set of new useful features as well as how to effectively use these features in real models. For instance, in Chapter 5, I showed that the feature of the number of different days a students has worked on a skill is a more predictive feature for knowledge retention. These features themselves are not a contribution to data mining so much as they are to education research more broadly, which can used by other educational researchers or tutoring systems. "

Publisher

Worcester Polytechnic Institute

Degree Name

PhD

Department

Computer Science

Project Type

Dissertation

Date Accepted

2015-09-15

Accessibility

Unrestricted

Subjects

Student Modeling, Bayesian Networks, Educational Data Mining

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