Automatic leveling system for e-learning examination pool using entropy-based decision tree

Authors:
Shu-Chen Cheng;Yueh-Min Huang;Juei-Nan Chen;Yen-Ting Lin
Affiliations:
Department of Computer Science and Information Engineering, Southern Taiwan University of Technology, Tainan, Taiwan;Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan;Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan;Department of Computer Science and Information Engineering, Southern Taiwan University of Technology, Tainan, Taiwan
Venue:
ICWL'05 Proceedings of the 4th international conference on Advances in Web-Based Learning
Year:
2005

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Abstract

In this paper, we propose an automatic leveling system for e-learning examination pool using the algorithm of the decision tree. The automatic leveling system is built to automatically level each question in the examination pool according its difficulty. Thus, an e-learning system can choose questions that are suitable for each learner according to individual background. Not all attributes are relevant to the classification, in other words, the decision tree tells the importance of each attribute.