Recommending Learning Peers for Collaborative Learning Through Social Network Sites

被引:11
|
作者
Hassan, Mohammed [1 ]
Hamada, Mohamed [1 ]
机构
[1] Univ Aizu, Grad Sch Comp Sci & Engn, Aizu Wakamatsu, Fukushima, Japan
关键词
recommender system; learning peers; collaborative learning; social network sites; Bayesian classifier;
D O I
10.1109/ISMS.2016.22
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With advances in social network sites and easy access to internet services, many learners depend on suggestions from other people on the internet for easy access to very essential information concerning learning materials, and also to collaborate with each other in order to exchange ideas. Current recommender systems for learning focus on recommending a sequence of learning materials based on learners similarities or similarities between the new learning objects and the ones the user is already familiar with in the past. Many learners prefer collaborative learning than learning on their own or in the classroom, but the major difficulty in engaging in an online collaborative learning is how to get a suitable collaborating partners(learning peers). This paper proposed a recommendation system that can search social network sites to find and recommend learning peers to the user based on their post, comment, and common friends on the social network.
引用
收藏
页码:60 / 63
页数:4
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