The Design And Implementation Of Personalized Indexing System Based On E-learning Courseware

With the development of network education, people put more focus on the corresponding network resource construction. As we all know, network education consists of three sections: hardware, software and education resource. Internet technique is quickly developing in a board band, high speed and multimedia orientation, and this weakens hardware and software's advantage, and the education resource becomes more and more important, which will finally determine whether the network education can succeed. The network coursewares are the main factor of network education. In a distance-learning environment, coursewares can make students learn more easily with an unlimited space and time, which becomes students'main learning resource. With the increase of coursewares, the indexing work for these courses becomes more difficult.Everyday large amount of resource are added into research lab, and people always take more care about the course they interest in. As a result, there is an urgent need to make an index for these coursewares and recommend some suitable course for online learners.Main issues of this article include:(1). In multimedia courseware generating subsystem, we proposed a multi-flow synchronized fusion of the whole-scene data technology, which can merage teachers'audio, vedio, and action into a WMV file as scripts. On the other hand, we achieve a web format conversion of PPT. Cmbining these two technology, we can gain a real-time and, efficient generation of high-quality streaming media courseware. At the same time, we finish the cutting and merging work for WMV file using WMSDK library.(2). In personalized multimedia indexing subsystem, we proposed a multimedia courseware indexing technology based on learners'interest feature vector. After users log in the distance learning system, they can watch and rate courseware and the user interest feature vectors are formed according to our rating value. At the same time, we can construct our learning friends, potential friends, stranger and unknown indexing with the rating value and user's interest feature vector. These indexing can provide suitable learning resource and enhance users'learning effectiveness. On the other hand, we can merge it into two other obtained indexing functions: title based and contented based, which can highly improve the indexing function for multimedia courseware.