Automatic Generation of Learning Channels by Using Semantic Relations among Lecture Slides and Recorded Videos for Self-Learning Systems

We present a method of automatically generating learning channels by using the semantic relations that implicitly exist in slides of a lecture that has accompanying recorded video. These days, many lecture videos with presentation files are shared over the Web from many universities through their own public sites. Although these materials are useful and valuable to many potential students, their use of sequential static media for self-learning purposes means there is still a lack of support for self-learners seeking learning channels suitable for various levels of understanding. Our newly generated learning channels let users easily focus on either highly detailed slides or introductory slides without needing to examine all of the data. We describe a prototype system supported by this learning-channel construction method.