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Application Of Emerging Knowledge Discovery Methods In Engineering Education

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2009 Annual Conference & Exposition


Austin, Texas

Publication Date

June 14, 2009

Start Date

June 14, 2009

End Date

June 17, 2009



Conference Session

Engineering and Mathematics Potpourri

Tagged Division


Page Count


Page Numbers

14.218.1 - 14.218.12

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Paper Authors


Athanasios Tsalatsanis University of South Florida

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Athanasios Tsalatsanis received his Diploma degree in Production and Management Systems Engineering and his M.S. degree in Production Engineering from Technical University of Crete, Greece. He received his Ph.D. degree in Industrial Engineering from University of South Florida in 2008. Currently, he is a postdoctoral fellow with the Department of Industrial and Management Systems Engineering, University of South Florida.
His research interests are in information systems, data analysis and decision sciences; control of autonomous intelligent systems; modeling analysis and control of discrete event dynamic systems.

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Ali Yalcin University of South Florida


Autar Kaw University of South Florida

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Autar K Kaw is a Professor of Mechanical Engineering and Jerome Krivanek Distinguished Teacher at the University of South Florida. He is the author of the textbook - Mechanics of Composite Materials, CRC-LLC Press. With major funding from National Science Foundation, he is developing award winning web-based resources for an undergraduate course in Numerical Methods. He is the recipient of the 2004 Council for Advancement and Support of Education (CASE) & the Carnegie Foundation for the Advancement of Teaching (CFAT) Florida Professor of the Year and the 2003 American Society of Engineering Education (ASEE) Archie Higdon Distinguished Mechanics Educator Award. His current scholarly interests include development of instructional technologies, integrating research in classroom, thermal stresses, computational mechanics, and mechanics of nonhomogeneous nanolayers.

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract



The purpose of this study is to investigate the application of emerging knowledge discovery methodologies in analyzing student profiles to predict the performance of a student in a course. Knowledge discovery is the research area concerned with analyzing existing information and extracting implicit, previously unknown, hidden and potentially useful knowledge in an automated manner. The discovered knowledge is often represented by a set of rules or mathematical functions which has practical application. This type of knowledge can enable instructors to accommodate each student’s learning needs and abilities as well as aid the students in appropriate course selection. In this paper we present a pilot study which demonstrates the analysis of student profiles from 60 students. The methodology used for knowledge discovery is based on Rough Set Theory which combines theories such as fuzzy sets, evidence theory and statistics. The results of the pilot study show that the knowledge discovery methodologies are likely to discover knowledge which may be overlooked using traditional statistical approaches. Our preliminary results indicate that knowledge discovery methodologies can be successfully used in predicting student performance. Based on the experiences gained from this work, specific future research directions and tasks to ensure a successful comprehensive implementation are discussed.

1. Introduction

Can we reliably predict the performance of a student in a particular course before he/she starts the course? Or can we recommend a specific set of course materials to certain students to improve their learning? What are the key factors that help answer these questions? Is it a student’s past academic performance? Or is it their current work and/or class load? Or maybe it is their existing knowledge regarding the course material. More than likely it is an intricate combination of these and other factors some of which we do know and some we are yet to discover.

The overarching goal of our research is the development of a decision support system to enable both students and instructors to improve the quality of higher education. We envision that a decision support system which considers relevant information (including but not limited to a student’s past performance in college, current work and course load, performance in the courses which are pre-requisites, etc.) can be designed to predict a student’s performance in a course before the student begins the course. Based on this information, combined with the students learning style, a personalized learning strategy can be formulated to ensure the success of the student in the course.

We further envision that the development of such a decision support system can be achieved by utilizing emerging knowledge discovery methodologies (KDM). Knowledge discovery is the research area concerned with analyzing existing information and extracting implicit, previously unknown, hidden and potentially useful knowledge in an automated manner1. The discovered knowledge is often represented by a set of rules or mathematical functions which has practical

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