Faculty Advisor

Neil T. Heffernan

Faculty Advisor

Gary Pollice

Abstract

Classroom instruction time is a valuable yet scarce resource to teachers, who must decide how to best meet their objectives by selecting which topics to spend time on and when to move forward. Intelligent Tutoring Systems (ITS) are a powerful tool for teachers in this regard, allowing them to measure their students' current level of knowledge, helping them gauge student knowledge acquisition, and providing them with valuable insight into learning methodologies. By using ITS to identify the effectiveness of proven methods of instruction, we can more effectively teach students both in and outside of the classroom. In this paper we review the results and contributions of a new Bayesian data mining method which can be used to identify what works in an ITS and how it can be used to learn from data which is not in the typical randomized controlled trial design. We then discuss modifications to this dataset which use more knowledge about the students to improve accuracy. Lastly we evaluate this model on detecting and predicting long term student retention, and discuss methods to improve its predictive accuracy.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Computer Science

Project Type

Thesis

Date Accepted

2011-05-03

Accessibility

Unrestricted

Subjects

educational data mining, student modeling

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