Title: Analytical review and study on various course recommendation systems

Authors: V. Anupama; M. Sudheep Elayidom

Addresses: Division of Computer Science, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala, India ' Division of Computer Science, School of Engineering, Cochin University of Science and Technology, Kochi, Kerala, India

Abstract: In the educational system, online courses are significant in developing the knowledge of users. The selection of courses is important for college students because of large unknown optional courses. The course recommendation systems are provided with suggestions and improve course selection during the pre-registration stage. This survey presents the analysis of 50 research papers for course recommendation. The course recommendation systems are grouped under three categories, namely machine learning-based techniques, collaborative-based and data mining-based techniques. Besides, the classification of techniques, utilised tools, implemented software tools and performance metrics are considered for analysis. Moreover, the research gaps identified in the existing course recommendation system are discussed. The machine learning-based approach is mostly used for course recommendation among several approaches. Most existing course recommendation techniques use Java as the implementation tool and the Moodle log database. Also, F-measure, MAE, accuracy and RMS have been commonly used as performance metrics.

Keywords: recommendation system; root mean square; collaborative filtering; F-measure; data-mining.

DOI: 10.1504/IJWMC.2023.133066

International Journal of Wireless and Mobile Computing, 2023 Vol.25 No.2, pp.160 - 170

Received: 04 Feb 2022
Received in revised form: 06 Oct 2022
Accepted: 16 Nov 2022
Published online: 29 Aug 2023 *

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