In general, content-based recommender systems use a keyword vector to locate recommendations. However, this method does not consider relations of each keyword and it is also inscrutable to users, who may have a hard time determining which words in their profiles are important and which may be skewing their results to irrelevant recommendations. In contrast, the method proposed in this paper automatically creates a keyword map based user profile for each learner, based on visited learning materials and the learning processes in a web based learning system. The keyword maps describe each learner's existing knowledge with keywords and various relations of them. Our recommender system makes use of this user profile to suggest learning materials the learner might be interested in.In this paper, we report on our initial work on applying keyword map-based learner profile to a content-based recommender system. We believe that our method would provide good accuracy while avoiding many of the problems of both collaborative and keyword based approaches.
To compensate the lack of awareness in CSCL environment, a CSCL chat tool capable of displaying all the participants' key typing process was developed. The system displays key by key typed in letters to other learners. Smooth turn-taking and topic negotiation are expected by the realization of real time display. From our preliminary evaluation, learners evaluated the conversation using this system easier, even though number of sentences between the proposed system and the usual chat system showed no difference. Learners' comments after the usage reveals that there are both merit and demerit such as natural awareness or neglecting of writing complete sentences.
By introducing the concept of Cloud Computing, the architecture of e-Learning has been changed, So, we propose an autonomous learning support system that encourages learners' interaction by "the Architecture of Participation". We developed learning management system based on SNS. This system has two models that describe 1) learner's acquired knowledge, and 2) learner's participatory attributes. The learner's acquired knowledge is identified by its articles. The learner's participatory attributes are identified by learning activities in the system. Moreover, this system can recommend various learning objects, modules as "sub-contents" and persons based on the learner profile model. Then, Web-based services, applications/tools and learning contents along with cloud computing are utilized for building flexible learning environments. Based on those shared resources, it is possible to create a new Model of Learning Ecology. The principle of learning activities in this new ecology is supposed as followings; the social autonomous leaning-process by a) discover new facts, b) create new artifacts, c) change and/or transform shared web content in learning community.
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