A novel resource recommendation system based on connecting to similar e-learners

Authors:
Fan Yang;Peng Han;Ruimin Shen;Zuwei Hu
Affiliations:
Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, China;Department of Computer Science and Engineering, Shanghai JiaoTong University, Shanghai, China
Venue:
ICWL'05 Proceedings of the 4th international conference on Advances in Web-Based Learning
Year:
2005

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Abstract

E-learners always finds it is difficult to make a decision about which of learning materials best meet their situation and need to read, whilst instructors are finding it is almost impossible to reorganize different materials corresponding to individuals. Based on the investigation on real learners in the Network Education College of Shanghai Jiaotong University, we found that many learners share common need of learning resources if they have similar learning preferences and status during learning process. This paper proposes a novel E-Learning resource recommendation system based on connecting to similar E-Learners, which can find and reorganize the learners share similar learning status into smaller communities. Furthermore a recommendation platform is developed to enable the learner to share filtered resources.