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Study Level Inference System using Education Video Watching Behaviors  

Kang, Sang Gil (Department Computer and Information Engineering, Inha University)
Kim, Jeonghyeok (Department Computer and Information Engineering, Inha University)
Heo, Nojeong (Department of Information and Communications, Dongyang University)
Lee, Jong Sik (Department Computer and Information Engineering, Inha University)
Publication Information
Journal of Information Technology and Architecture / v.10, no.3, 2013 , pp. 371-378 More about this Journal
Abstract
Video-demand learning through E-learning continuously increases on these days. However, not all video-demand learning systems can be utilized properly. When students study by education videos not matched to level of their own, it is possible for them to lose interest in learning. It causes to reduce the learning efficiency. In order to solve the problem, we need to develop a recommendation system which recommends customized education videos according the study levels of students. In this paper, we estimate the study level based on the history of students' watching behaviors such as average watching time, skipping and rewinding of videos. In the experimental section, we demonstrate our recommendation system using real students' video watching history to show that our system is feasible in a practical environment.
Keywords
E-learning; recommendation; cloud computing; collaborative filtering;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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