A personalized auxiliary material recommendation system based on learning style on Facebook applying an artificial bee colony algorithm

Authors:
Chia-Cheng Hsu;Hsin-Chin Chen;Kuo-Kuang Huang;Yueh-Min Huang
Affiliations:
Department of Engineering Science, National Cheng Kung University, Tainan City 701, Taiwan;Department of Engineering Science, National Cheng Kung University, Tainan City 701, Taiwan;Department of Information Management, National Penghu University of Science and Technology, Penghu County 880, Taiwan;Department of Engineering Science, National Cheng Kung University, Tainan City 701, Taiwan and Department of Applied Geoinformatics, Chia Nan University of Pharmacy and Science, Tainan City 717, T ...
Venue:
Computers & Mathematics with Applications
Year:
2012

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Abstract

Facebook is currently the most popular social networking site in the world, providing an interactive platform that enables users to contact friends and other social groups, as well as post a large number of photos, videos, and links. Recently, many studies have investigated the effects of using Facebook on various aspects of education, and it has been used as a learning platform for sharing auxiliary materials. However, not all of the auxiliary materials posted may conform to the individual learning styles and abilities of each user. This study thus proposes a personalized auxiliary material recommendation system based on the degree of difficulty of the auxiliary materials, individual learning styles, and the specific course topics. An artificial bee colony algorithm is implemented to optimize the system. The results indicate that this method is superior to other schemes, and improves the execution time and accuracy of the recommendation system in an efficient manner.