Automatic Generation of the Learners Oriented Materials under E-learning Environments

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The goal of this research is to establish methods to identify automatically, from an e-learning course material which mainly consist of multi-media contents, the important parts for learners of the course. For the automatic identification and extraction, we utilized past viewing history of the material, in other words the descriptions of where they viewed well and skipped, etc. To adapt the difference of each learner, we assume that learners can be divided into several groups. We researched the clustering method and features for appropriate grouping.