模式识别与人工智能
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  2018, Vol. 31 Issue (1): 77-90    DOI: 10.16451/j.cnki.issn1003-6059.201801007
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Research on Educational Data Mining for Online Intelligent Learning
LIU Qi1, CHEN Enhong1, ZHU Tianyu1, HUANG Zhenya1, WU Runze1, SU Yu2, HU Guoping2
1.Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027
2.USTC iFLYTEK Co., Ltd., Hefei 230088

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Abstract  

With the rapid informationization of education, extensive data records from online education of students are accumulated, and it provides a good opportunity for both data-driven educational assessment and intelligent tutoring. However, existing models are hard to accurately analyze the characteristics of questions and the academic levels of students from the massive and sparse data with high noise. Meanwhile, it is difficult for these models to satisfy the personalized needs of students and teachers. In this paper, educational data mining studies on these problems are summarized. To improve the student academic level, these studies focus on modeling three objects in education (i.e., questions, students and teachers) and apply effective techniques, such as personalized recommendation methods, combined with the domain knowledge from education. Specifically, a question text embedding framework is presented for question analysis and question retrieval. Then, personalized recommendation methods on learning resources are illustrated based on the cognitive diagnosis of students. Moreover, the way of providing effective guidance and suggestions for teachers is showed. Some of these research achievements are applied to the online educational system “ZHIXUE” in iFlyTek. Finally, the possible research directions in the future are discussed.

Key wordsOnline Intelligent Learning      Educational Assessment      Cognitive Diagnosis      Recommender System     
Received: 08 December 2017     
About author:: LIU Qi, Ph. D., associate professor. His research interests include data mining, know-ledge discovery and machine learning. CHEN EnhongCorresponding author, Ph. D., professor. His research interests include machine learning, data mining, social network and personalized recommender system. ZHU Tianyu, master student. Her research interests include data mining and recommender system.HUANG Zhenya, Ph.D. candidate. His research interests include educational data mining and recommender system.WU Runze, Ph. D. candidate. His research interests include educational data mi-ning and cognitive diagnosis.SU Yu, Ph. D. candidate. His research interests include data mining and image identification.HU Guoping, Ph. D., engineer. His research interests include intelligent voice and language core technology.
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LIU Qi
CHEN Enhong
ZHU Tianyu
HUANG Zhenya
WU Runze
SU Yu
HU Guoping
Cite this article:   
LIU Qi,CHEN Enhong,ZHU Tianyu等. Research on Educational Data Mining for Online Intelligent Learning[J]. , 2018, 31(1): 77-90.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201801007      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I1/77
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