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| Annotating Knowledge Points & Recommending Questions Based on Semantic Association Rules |
Wei Wei1,2,Guo Chonghui2( ),Xing Xiaoyu2 |
1Center for Energy, Environment & Economy Research, Zhengzhou University, Zhengzhou 450001, China
2Institution of Systems Engineering, Dalian University of Technology, Dalian 116024, China |
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Abstract [Objective] This paper proposes a method automatically annotating the knowledge points of test questions from online education resources.[Methods] First, we introduced the concept of text semantics to establish new association rules. Then, considering the semantic matching degrees between the target questions and the rules, we proposed an automatic method for knowledge point annotation. Finally, we presented a personalized question recommendation mechanism.[Results] We examined the proposed method with test questions from middle school mathematics and high school history courses. We also compared our model’s labeling accuracy with naive Bayes, K nearest neighbor, random forest and support vector machine, and yielded better results.[Limitations] The understanding of the semantics of test questions and the labeling accuracy could be further improved.[Conclusions] The knowledge point annotation and the personalized question recommendation methods could improve smart teaching and online learning.
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Received: 06 June 2019
Published: 26 April 2020
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Corresponding Authors:
Chonghui Guo
E-mail: dlutguo@dlut.edu.cn
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