面向在线智慧学习的教育数据挖掘技术研究

doi:10.16451/j.cnki.issn1003-6059.201801007

Abstract
Figure/Table
References (0)
Related Citation (6)

Download: PDF (2485 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

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 words： Online 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.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	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.

URL:

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

[1] 傅钢善.教育技术发展轨迹探讨.电化教育研究, 2005(9): 22-26.
(FU G S. Exploration of Educational Technology Development. e-Education Research, 2005(9): 22-26. )
[2] HONG C M, CHEN C M, CHANG M H, et al. Intelligent Web-Based Tutoring System with Personalized Learning Path Guidance. Computers & Education, 2008, 51(2): 787-814.
[3] BRESLOW L, PRITCHARD D E, DEBOER J, et al. Studying Learning in the Worldwide Classroom Research into edX′s First MOOC. Research & Practice in Assessment, 2013, 8: 13-25.
[4] POLSON M C. Foundations of Intelligent Tutoring Systems. Hove, UK: Psychology Press, 2013.
[5] 陈池,王宇鹏,李超,等.面向在线教育领域的大数据研究及应用.计算机研究与发展, 2014, 51(S1): 67-74.
(CHEN C, WANG Y P, LI C, et al. The　Research　and　Application　of　Big　Data　in　the　Field　of　Online　Education. Journal　of　Computer　Research　and　Development,　 2014, 51(S1): 67－74.)
[6] 管佳,李奇涛.中国在线教育发展现状,趋势及经验借鉴.中国电化教育, 2014(8): 62-66.
(GUAN J, LI Q T. China′s Online Education Current Situation, Trend and Experience for Reference. Chinese Journal of Computers, 2014(8): 62-66.)
[7] PEA-AYALA A. Educational Data Mining: A Survey and a Data Mining-Based Analysis of Recent Works. Expert Systems with Applications, 2014, 41(4): 1432-1462.
[8] LEIGHTON J P, GIERL M J. Cognitive Diagnostic Assessment for Education: Theory and Applications. Cambridge, UK: Cambridge University Press, 2007.
[9] ANDERSON T. The Theory and Practice of Online Learning. Athabasca, Canada: Athabasca University Press, 2008.
[10] MCAULEY A, STEWART B, SIEMENS G, et al. The MOOC Model for Digital Practice[M/OL]. [2017-11-20]. http://elearnspace.org/Articles/MOOC_Final.pdf.
[11] YUDELSON M, HOSSEINI R, VIHAVAINEN A, et al. Investigating Automated Student Modeling in a Java MOOC // Proc of the 7th International Conference on Educational Data Mining. New York, USA: ACM, 2014: 261-264.
[12] CHAKOR Y A. Abandonment of Learners MOOC Problematic
Analysis and Proposed Solutions. International Journal of Computer Applications, 2016, 153(2): 35-37.
[13] BRUFF D, FISHER D, MCEWEN K, et al. Wrapping a MOOC: Student Perceptions of an Experiment in Blended Learning[J/OL]. [2017-11-20]. https://my.vanderbilt.edu/douglasfisher/files/2013/06/JOLTPaperFinal6-9-2013.pdf.
[14] 朱天宇,黄振亚,陈恩红,等.基于认知诊断的个性化试题推荐方法.计算机学报, 2017, 41(1): 176-191.
(ZHU T Y, HUANG Z Y, CHEN E H, et al. Cognitive　Diagnosis　Based　Personalized　Question　Recommendation. Chinese　Journal　of　Computers, 2017, 41(1): 176-191.)
[15] KONGSAKUN K, FUNG C C. Neural Network Modeling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand. WSEAS Transactions on Computers, 2012, 11(2): 34-44.
[16] DE LA TORRE J. DINA Model and Parameter Estimation: A Didactic. Journal of Educational and Behavioral Statistics, 2009, 34(1): 115-130.
[17] MIDGLEY C. Goals, Goal Structures, and Patterns of Adaptive Learning. New York, USA: Routledge, 2014.
[18] L Y J, ZHAO G, MIAO P, et al. Construction of Multidimensional Dynamic Knowledge Map Based on Knowledge Requirements and Knowledge Connection // WEN Z, LI T, eds. Knowledge Engineering and Management. Berlin, Germany: Springer, 2014: 83-94.
[19] HUDAK G M, KIHN P. Labeling: Pedagogy and Politics. New York, USA: Routledge, 2014.
[20] 刘淇,陈恩红,黄振亚,等.面向个性化学习的学生认知能力分析.中国计算机学会通讯, 2017, 13(4): 28-34.
(LIU Q, CHEN E H, HUANG Z Y, et al. Cognitive Analysis for Personalized Student Learning. Communications of the CCF, 2017, 13(4): 28-34.)
[21] BOOPATHIRAJ C, CHELLAMANI K. Analysis of Test Items on Difficulty Level and Discrimination Index in the Test for Research in Education. International Journal of Social Science & Interdisciplinary Research, 2013, 2(2): 189-193.
[22] ALAGUMALAI S, CURTIS D D. Classical Test Theory // MACLEAN R, WATANABE R, BAKER R, et al., eds. Applied Rasch Measurement: A Book of Exemplars. Berlin, Germany: Springer, 2005: 1-14.
[23] ZHANG L, YANG L. Repeater Analyses for TOEFL iBT[J/OL]. [2017-11-20]. https://www.ets.org/Media/Research/pdf/RM-08-05.pdf.
[24] FUCHS L S, FUCHS D, HAMLETT C L, et al. Effects of Expert System Consultation within Curriculum-Based Measurement, Using a Reading Maze Task. Exceptional Children, 1992, 58(5): 436-450.
[25] HUANG Z Y, LIU Q, CHEN E H, et al. Question Difficulty Prediction for READING Problems in Standard Tests // Proc of the 31st AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2017: 1352-1359.
[26] DI BELLO L V, ROUSSOS L A, STOUT W. 31A Review of Cognitively Diagnostic Assessment and a Summary of Psychometric Models. Handbook of Statistics, 2006, 26(6): 979-1030.
[27] EMBRETSON S E, REISE S P. Item Response Theory. Hove, UK: Psychology Press, 2013.
[28] RUPP A A, TEMPLIN J. The Effects of Q-Matrix Misspecification on Parameter Estimates and Classification Accuracy in the DINA Model. Educational and Psychological Measurement, 2008, 68(1): 78-96.
[29] WU R Z, LIU Q, LIU Y P, et al. Cognitive Modelling for Predicting Examinee Performance // Proc of the 24th International Joint Conference on Artificial Intelligence. New York, USA: ACM, 2015: 1017-1024.
[30] BAKER R S, CORBETT A T, KOEDINGER K R. Detecting Stu-dent Misuse of Intelligent Tutoring Systems // Proc of the International Conference on Intelligent Tutoring Systems. Berlin, Germany: Springer, 2004: 531-540.
[31] VAESSEN B, PRINS F, JEURING J. University Students′ Achieve-ment Goals and Help-Seeking Strategies in an Intelligent Tutoring System. Technical Report, UU-CS-2013-019. Utrecht, The Netherlands: Utrecht University, 2013.
[32] WU R Z, XU G D, CHEN E H, et al. Knowledge or Gaming? Cognitive Modelling Based on Multiple-Attempt Response // Proc of the 26th International World Wide Web Conference. New York, USA: ACM, 2017: 321-329.
[33] BAKER R S, CORBETT A T, KOEDINGER K R, et al. Adapting to When Students Game an Intelligent Tutoring System // Proc of the International Conference on Intelligent Tutoring Systems. Berlin, Germany: Springer, 2006: 392-401.
[34] BAKER R S, CORBETT A T, KOEDINGER K R, et al. Off-Task Behavior in the Cognitive Tutor Classroom: When Students Game the System // Proc of the SIGCHI Conference on Human Factors in Computing Systems. New York, USA: ACM, 2004: 383-390.
[35] THAI-NGHE N, DRUMOND L, HORVATH T, et al. Factorization Techniques for Predicting Student Performance[J/OL]. [2017-11-20]. https://www.ismll.uni-hildesheim.de/pub/pdfs/Nguyen_et_al_ERSAT_2011.pdf.
[36] AGRAWAL R, GOLSHAN B, TERZI E. Grouping Students in Educational Settings // Proc of the 20th International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2014: 1017-1026.
[37] GALL M D, GALL J P. The Discussion Method. The Psychology of Teaching Methods, 1976, 75(S1): 166-216.
[38] LIU Y P, LIU Q, WU R Z, et al. Collaborative Learning Team Formation: A Cognitive Modeling Perspective // Proc of the 21st International Conference on Database Systems for Advanced Applications. Berlin, Germany: Springer, 2016: 383-400.
[39] BRUSILOVSKY P, PEYLO C. Adaptive and Intelligent Web Ba-sed Educational Systems. International Journal of Artificial Intelligence in Education, 2003, 13(2/3/4): 159-172.
[40] 路平,王敏娟,万昆.试题库自动组卷策略研究.电脑开发与应用, 2001, 14(2): 9-11.
(LU P, WANG M J, WAN K. Tactics Study on Automatic Creating Test Paper from Item Pool. Computer Development & Application, 2001, 14(2): 9-11.)
[41] 戴海奇,张峰,陈雪枫.心理与教育测量.第3版.广州:暨南大学出版社, 2011.
(DAI H Q, ZHANG F, CHEN X F. Psychological and Educational Measurement. 3rd Edition. Guangzhou, China: Jinan University Press, 2011)