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

Chia Cheng Hsu, Hsin Chin Chen, Kuo Kuang Huang, Yueh Min Huang

研究成果: Article

34 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)1506-1513
頁數8
期刊Computers and Mathematics with Applications
64
發行號5
DOIs
出版狀態Published - 2012 九月 1

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Computational Mathematics

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