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The traditional academic advising process in many tertiary-level institutions today possess significant inefficiencies, which often account for high levels of student dissatisfaction. Common issues include high student-advisor loads, long waiting periods at advisory offices and the need for advisors to handle a significant number of redundant cases, among others. Utilizing semantic web expert system technologies, a solution was proposed that would complement the traditional advising process, alleviating its issues and inefficiencies where possible. The solution coined ‘AdviseMe’, an intelligent web-based application, provides a reliable, user-friendly interface for the handling of general advisory cases in special degree programmes offered by the Faculty of Science and Technology (FST) at the University of the West Indies (UWI), St. Augustine campus. In addition to providing information on handling basic student issues, the system’s core features include course advising, as well as infor-mation of graduation status and oral exam qualifications. This paper produces an overview of the solution, with special attention being paid to the its inference system exposed via its RESTful Java Web Server (JWS). The system was able to provide sufficient accurate advice for the sample set presented and showed high levels of acceptabil-ity by both students and advisors. Furthermore, its successful implementation demonstrated its ability to enhance the advisory process of any tertiary-level institution with programmes similar to that of FST.
Keston et al. (Thu,) studied this question.
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