Date of Award

2016

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Computer Science

First Advisor

Christie I Ezeife

Keywords

Data Mining, Intelligent tutoring system, student model, domain model, example-based learning, task, worked-out examples, learning units, asymmetric Boolean data, vector space model, similarity functions, feature extraction, classification, prediction, clustering

Rights

CC BY-NC-ND 4.0

Abstract

Intelligent tutoring systems (ITS) aim to provide customized resources or feedback on a subject (commonly known as domain in ITS) to students in real-time, emulating the behavior of an actual teacher in a classroom. This thesis designs an ITS based on an instructional strategy called example-based learning (EBL), that focuses primarily on students devoting their time and cognitive capacity to studying worked-out examples so that they can enhance their learning and apply it to similar graded problems or tasks. A task is a graded problem or question that an ITS assigns to students (e.g. task T1 in C programming domain defined as “Write an assignment instruction in C that adds 2 integers”). A worked-out example refers to a complete solution of a problem or question in the domain. Existing ITS systems such as NavEx and PADS, that use EBL to teach their domain suffer from several limitations such as (1) methods used to extract knowledge from given tasks and worked-out examples require highly trained experts and are not easily applicable or extendable to other problem domains (e.g. Math), either due to use of manual knowledge extraction methods (such as Item Objective Consistency (IOC)) or highly complex automated methods (such as syntax tree generation) (2) recommended worked-out examples are not customized for assigned tasks and therefore are ineffective in improving student success rate.

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