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Date of Award

Spring 2005


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Document Type

Thesis: EWU Only

Degree Name

Master of Science (MS) in Computer Science


Computer Science


Academic advising is an important task and any error made during this process may substantially delay a student's academic progress, and even worse, his or her graduation date. Advisors are constantly challenged by students to be more responsive, efficient, and error free. Therefore, technology must be utilized to meet these challenges. Currently, few universities in the United States have integrated Web-based expert systems to make the advising process more productive and pleasant for both advisors and students. However, the majority of these systems focus on degree requirements and ignore many other important factors, which makes the system less adaptive and weakens the quality of the generated advice. Therefore, there is a crucial need for the creation of a system that exploits artificial intelligence in order to improve the quality and the efficiency of the advising process. This thesis focuses on the design and implementation of a Web-based advising system called EWU Optimal Advisor, which utilizes artificial intelligence. This fuzzy expert system consists of two parts. The first part, known as symbolic reasoning, provides advice based on many factors, such as degree requirements, prerequisites, student transcript, course announcements, course catalog, seat availability, and student schedule. The second part, fuzzy logic, is incorporated within the expert system to increase the quality of the generated advice by using additional factors such as course offering, course dependency, and course priority. Another factor is this system is Web-based, which makes it convenient as it may be accessed at any location, anytime. The combination of these factors with appropriate computer technologies allowed me to design a highly versatile advising system that will reduce the stress of registration for both students and advisors. The proposed system also aids academic advisors in maximizing their productivity and minimizing errors and inconsistencies.