Date of Publication

12-10-2005

Document Type

Master's Thesis

Degree Name

Master of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Raymund C. Sison

Defense Panel Chair

Kai Shan L. Fernandez

Defense Panel Member

Rigan P. Ap-Apid

Abstract/Summary

Intelligent Tutoring Systems must make use of student models to represent the knowledge of their users. Although there are currently several different implementations of Bayesian network student models in existence, they all model the students current state of knowledge only. They do not address the need to come up with a way to integrate the representation of misconceptions that may exist in a students mind into their network. Because the presence of misconceptions can affect the thought processes of students, the ability to model them in a Bayesian network student model should improve that models ability to make approximations about the user. The aim of this research is to design a Bayesian network student model for Novice C Programmers.

Keywords: Intelligent Tutoring System, Bayesian network, Student Model, Misconception

Abstract Format

html

Language

English

Format

Electronic

Accession Number

CDTG003996

Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

vi, [81 leaves]

Keywords

Bayesian statistical decision theory; Intelligent tutoring systems; Computer programs

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