Affiliations: Department of Computer Science and Engineering,
Shanghai Jiaotong University, Shanghai, PR China | Faculty of Electrical and Information Engineering,
FernUniversität in Hagen, Germany | Department of Computer Science and Engineering,
Shanghai Jiaotong University, Shanghai, PR China
Abstract: E-learning settings such as on-line courses offered in China often
involve large numbers of geographically dispersed students who have diverse
professional background, learning preferences, and disparate learning needs.
However, current e-learning and classroom teaching methods are extremely
limited with respect to personalized learning as they typically provide the
same content to all students. Hence, students are finding it difficult to make
a decision about which learning materials best meet their personal demands,
whilst instructors are finding it almost impossible to recommend personalized
materials most appropriate to students since the individual study situations
are disparate and hard to analyze. This paper proposes an e-learner Community
Building strategy based on Hebbian Learning Rule, which helps learners to set
up trusted neighbor connections to peer students with similar study situations.
In addition, students can get useful recommendations for proper learning
resources from their peers. Based on this theory a recommendation platform has
been developed that enables a learner to enquire recommendations, vote on
recommended resources and communicate with neighbor learners. Experimental
results derived from real learner data have shown that this system can organize
learners properly and efficiently, and the cooperative recommendation service
indeed improves the students' learning achievements.
