ENHANCING COLLABORATIVE LEARNING BASED ON PERSONALIZED RECOMMENDATION AMONG COMMUNITIES

F. Yang, B. Krämer, and P. Han

Keywords

Collaborative learning, personalized recommendation, elearner community, JADE, intelligent agent

Abstract

Although there are abundant learning resources for e-learners to access conveniently, they still find it difficult to decide which learning materials best meet their needs in a given situation. Collaborative learning provides a good solution to this problem, as it can help e-learners to communicate with each other and share learning resources or experiences. However, e-learners are distributed in an open e-learning environment, and they may have no chance to know each other personally. Furthermore, they may have different learning interests, statuses, and backgrounds, which makes it very difficult to decide which e-learners should communicate with each other and whose collaborative interaction finally did enhance the learning effects. This paper proposes an innovative collaborative learning platform that connects similar e-learners. It also enables e-learners to recommend and evaluate learning resources and communicate with similar e-learners instantly. This platform is implemented based on the intelligent agent platform JADE, which generates a collaborative learning agent for each e-learner in charge of e-learner behaviour monitoring and communication with other agents. A novel e-learner community organization algorithm is also proposed. Experimental results have shown that this system helps e-learners to enhance their learning effect.

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