Automatic academic advisor

Abstract

One of the problems that face a Distance Education academic advisor (and for lesser degree local academic advisors) is to identify courses that best suit a student's interests and academic skills from a wide collection of elective courses. This is because an advisor needs to select courses that suit both the interest and academic skills of the student. The student may not be able to know his interest in a course from merely its title or from the description of the course provided in the course catalogue. Also, the advisor needs to advise the student to take a course that suits the student's academic performance and skills. Towards this, the advisor needs to consider the performance of students in all his prior courses, which is time consuming. These problems can be overcome using a course recommender system. We introduce in this paper an XML user-based Collaborative Filtering (CF) system called AAA. The system advises a student to take courses that were taken successfully by students, who have the same interest and academic performance as the student. We experimentally evaluated AAA. Results showed marked improvement. © 2012 ICST.