A Personalized Video Learning System by Artificial Bee Colony Algorithm on Facebook

Abstract

With the rapid development of internet technology, social network sites have been popular e-learning platforms, and Facebook is one of famous social network sites in the world, which facilitates information sharing and interpersonal relationships. Recently, Facebook has been regarded as a knowledge sharing and collaborative learning platform in a variety of fields for education, and it has been applied as a sharing video materials platform for learning English. However, those shared video materials may not conform to an individual’s learning abilities and needs. In order to enable learners to learn the most suitable and useful video materials for learning English, this study proposes a video recommendation system on Facebook based an artificial bee colony algorithm to provide video materials according to difficulty level of video materials, learner's behavior, and degree of association related to specific course topics to accommodate individual preferences. In addition, the proposed system supports video annotation function which includes textual and graphical annotations to assist learners to enhance learning effects of video learning. The experimental results indicate that the proposed system not only provides learners the suitable video materials for learning English, but also promotes their learning satisfaction and interest.