CRS-Reference-Cited by-同舟云学术

CRS

Published:2018 Issue: Volume: Page:553-570
ISSN:
Container-title:Student Engagement and Participation
language:
Short-container-title:

Author:

Taha Kamal¹

Affiliation:

1. Khalifa University of Science, Technology, and Research, UAE

Abstract

Most problems facing Distance Education (DE) academic advising can be overcome using a course recommender system. Such a system can overcome the problem of students who do not know their interest in courses from merely their titles or descriptions provided in course catalogues. The authors introduce in this chapter an XML user-based Collaborative Filtering (CF) system called CRS. The system aims at predicting a DE student's academic performance and interest on a course based on a collection of profiles of students who have similar interests and academic performance in prior courses. The system advises a student to take courses that were taken successfully by students who have the same interests and academic performance as the active student. The framework of CRS identifies a set of course features for every academic major. The authors experimentally evaluate CRS. Results show marked improvement.

Publisher

IGI Global

Reference17 articles.

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