Modern Information Retrieval
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Collaborative Representations: Supporting Face-to-Face and Online Knowledge-Building Discourse
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 4 - Volume 4
A mobile location-based information recommendation system based on GPS and WEB2.0 services
WSEAS Transactions on Computers
An optimized location-based mobile restaurant recommend and navigation system
WSEAS Transactions on Information Science and Applications
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Finding similar e-learners in a distributed and open e-learning environment and help them to learn collaboratively is becoming one of the urgent challenges of personalized e-learning services. Literature shows that current e-learner community building approaches are generated from qualitative studies of small-sized learner-centered classrooms which may need the teacher's participation. However, the findings might not apply to large classes in distributed learning environments, which make the teachers to face hundreds of e-learners in each class. In such situations, teachers also find it impossible to analyze the learning behaviors of each e-learner and divide them into different learning communities accurately. This paper addresses this problem in the adaptive e-learner community self-organizing point of view. Considering both the feature vector of learning resources and a learner's rating value on each resource, this paper firstly defines the learning interest feature vector to model the learner's behavior. Based on this an accurate learning interest feature representation method and, an innovative e-learner community self-organizing algorithm, called IFV- SORC, are proposed in this paper. Experimental results show that this algorithm exhibits good community organizing efficiency and scalability.