8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

Research Article

Automatic Academic Advisor

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  • @INPROCEEDINGS{10.4108/icst.collaboratecom.2012.250338,
        author={Kamal Taha},
        title={Automatic Academic Advisor},
        proceedings={8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing},
        publisher={IEEE},
        proceedings_a={COLLABORATECOM},
        year={2012},
        month={12},
        keywords={course recommender system distance education automatic academic advisor collaborative filering},
        doi={10.4108/icst.collaboratecom.2012.250338}
    }
    
  • Kamal Taha
    Year: 2012
    Automatic Academic Advisor
    COLLABORATECOM
    ICST
    DOI: 10.4108/icst.collaboratecom.2012.250338
Kamal Taha,*
    *Contact email: kamal.taha@kustar.ac.ae

    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.