Learning Contents Type Recommendation using Profile in Emotion based Interactive e-Learning Environment
Minchul Shin, Kyung-Seok Jung, Yong Suk Choi · Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong · 2012
To date, most e-learning systems does not reflect emotion of users effectively as against off-line learning sufficiently consider the emotion of users. It may causes several problems that hinders effectiveness for e-learning. To solve this problem, a methodology that recognizes emotions based on brain wave and recommends learning contents to user based on emotions is attracted since this methodology can increase the effectiveness of learning. In this paper, first we analysis collected emotional data for users. Then we grasp the preference for contents types. Finally, we propose e-learning contents type recommendation using profile. Our system can solve cold- start problem that occur in typical recommendation system and we propose method that gives weights automatically to each profile attribute values for better accuracy of recommendation. We check advanced performance using several experiments.