We refer to a new generation of web technologies such as social networking to address a recommender system that emphasize online collaborative learning. We propose an approach for improving recommender system through exploiting the learners note taking activity. We maintain that notes' features can be exploited by collaborative learning systems in order to enrich and extend the user profile and improve personalized learning. Thus our approach stresses collaborative note as a new and powerful kind of feedback and as a way to infer learner profiles. The experiment results show that our approach is effective.
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.
Recommender systems are now a popular research area and have become powerful tools to present personalized offers to users in many domains (e.g. e-commerce, e-learning). In this paper, we introduced an approach of personalization which extracts learners' preferences based on learning processes and learning activities (e.g. writing summary) and provides more relevant, personalized recommendations. Keyword maps proposed with keywords and various relations among them in this article describe content of each learning object and knowledge of each learner existing. The research hypothesizes that keyword maps should help to increase both the relevance and complement of learning materials recommendation. Thereafter, learners' comprehension degree and proficiency level are inferred by these keyword maps. According to the learners' comprehension degree and learners' proficiency levels, the system filters out the irrelevant learning processes and recommends the learning materials separately.
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