Random walks in directed hypergraphs and application to semi-supervised image segmentation
Computer Vision and Image Understanding
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With the development of computers and networks, online learning resources are becoming richer and richer. However, different learners' learning backgrounds and objectives are different. For this reason, it is important to recommend an appropriate learning process and learning resources automatically to each learner. To optimize an elearning process personalized and automatically, a directed hyper graph based e-learning process model was presented, which does not only describe construction of a e-learning process, but also describe the relationship among the process, the learner and the resource environment. The hyper graph theory and the semantics appended on the model were used to optimize the learning process and its supporting resource, and a set of optimizing theorems, rules and methods were given too. Through contrasting with the method provided by Acampora, it showed the effectiveness of this method. Finally, the application and limitation of this method was discussed.