Publication Date
2017
Document Type
Thesis
Committee Members
Jack Jean (Committee Member), Michael Raymer (Committee Co-Chair), Mateen Rizki (Advisor)
Degree Name
Master of Science in Computer Engineering (MSCE)
Abstract
The traditional education systems that have been used for several centuries have evolved very slowly and might be ineffective for addressing diverse learning styles and levels of preparation. This system is characterized by many students interacting with a single teacher, who is unable to address the individual needs of every student. Therefore, some students can become frustrated and fail to reach their educational potential. An Intelligent Tutoring System (ITS), which is a computer application used to provide students with one-to-one supplemental tutoring tailored to the student's learning style and pace, is of interest to educators for improving student learning. To evaluate the effectiveness of ITS, a systematic review of the recent literature was performed using a carefully crafted protocol designed to provide data to support a meta-study of the effectiveness of ITS. The research question guiding this study is: "Does an intelligent tutoring system improve students' learning abilities more than traditional learning?" A t-test, one-way ANOVA test, and KNIME program that does Latent Dirichlet allocation were performed. The results support the conclusion that ITS causes a significant improvement in learning over traditional instructional methods.
Page Count
122
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2017
Copyright
Copyright 2017, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0000-0002-2497-1501
