¹ Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; Graduate School of Chinese Academy of Sciences, Beijing, China
² Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
³ 2College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, China

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Abstract—Personalized knowledge acquisition is very important for promoting learning efficiency within Elearning system. To achieve this, two key problems involved are acquiring user’s knowledge requirements and discovering the people that can meet the requirements. In this paper, we present two approaches to realize personalized knowledge acquisition. The first approach aims to mine what knowledge the student requires and to what degree. All the interactive logs, accumulated during question answering process, are taken into account to compute each student’s knowledge requirement. The second approach is to construct and analyze user network based on the interactive data, which aims to find potential contributors list. Each student’s potential contributors may satisfy his/her requirement timely and accurately. Then we design an experiment to implement the two approaches. In order to evaluate the performance of our approaches, we make an evaluation with the percentage of satisfying recommendations. The evaluation results show that our approaches can help each student acquire the knowledge that he/she requires efficiently.

Index Terms—E-learning, knowledge acquisition, knowledge requirement, potential contributor

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Cite: Zhongying Zhao, Shengzhong Feng, Qingtian Zeng, Jianping Fan, and Xiaohong Zhang, " Personalized Knowledge Acquisition through Interactive Data Analysis in E-learning System," Journal of Computers vol. 5, no. 5, pp. 709-716, 2010.