Faculty Advisor or Committee Member

Neil Heffernan, Advisor

Faculty Advisor or Committee Member

Neil Heffernan

Identifier

etd-050206-154628

Abstract

Analyzing human learning and performance accurately is one of the main goals of an Intelligent Tutoring System. The“ASSISTment" system is a web-based system that blends assisting students and assessing their performance by providing feedback to the teachers. Good cognitive models are needed for an Intelligent Tutoring system to do a better job at predicting student performance. The ASSISTment system uses a method of cognitive modeling which is called a transfer model. A Transfer Model is a matrix that maps questions to skills. Other researchers have shown that transfer models help in building better predictive models that in-turn help in assessing a student's performance [1, 8]. They provide a viable means of representing a subject matter expert's view of which skills are needed to solve a given problem. However, the process of building a transfer model requires a lot of time. Reducing the time in which a transfer model is built would in turn help reduce the cost of building an Intelligent Tutoring System. Being able to build better transfer models will provide more efficient means of predicting learning in an intelligent tutoring system [6]. In this thesis we studied the creation of one transfer model that maps approximately the 263 released MCAS items to approximately 90 skills. Recently, [5] and [9], using two different modeling methodologies, have both concluded that this transfer model can be used to predict MCAS scores more accurately. Currently the time spent in creating and storing a model is estimated to be approximately 65 hours. This thesis was motivated by the need of a set of tools that would reduce the time spent in building a transfer model. The goal of this thesis was to create a tool that would speed up the process of building a transfer model. The efficiency of this tool is measured by an estimate of the overall time reduced for building a model. The average time reduced by using the tool on per question basis is also measured. The tool is not evaluated for its usability or for the ability to build better fitting transfer models.

Publisher

Worcester Polytechnic Institute

Degree Name

MS

Department

Computer Science

Project Type

Thesis

Date Accepted

2006-05-02

Accessibility

Unrestricted

Subjects

predictive models, knowledge components, transfer models, Intelligent tutoring systems, Learning, Mathematical models

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