ISSN 0253-2778

CN 34-1054/N

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An intelligent tutoring platform for educational assessment

  • 1.

    Department of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027

  • 2.

    Department of Computer Science and Technology, Anhui University, Hefei, 230039

  • 3.

    USTC iFLYTEK Co., Ltd., Hefei 230088

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.10.007
  • Received Date: 27 August 2015
  • Accepted Date: 29 September 2015
  • Rev Recd Date: 29 September 2015
  • Publish Date: 30 October 2015
    Abstract

  • Abstract

    K-12 education is an important part of educational psychology. Recently, online-learning has been widely accepted because of its significant effect on K-12 education. However, mostly based on the educational database, though existing online-learning systems and intelligent tutoring systems can provide useful resources for teachers and students, they seldom utilize offline test data to offer personalized services. To the end, an intelligent tutoring platform for educational assessment(ITPEA) was proposed and implemented. The platform combines both offline examinations and online resources to offer analysis from the perspectives of test papers, students and teachers. Specifically, based on the data from offline examinations, educational theories were first employed to evaluate the quality of the questions. Then, diagnostic models were constructed on the key knowledge points that students should master to meet one’s personalized demands. Finally, new analytical methods for evaluating teacher’s influence on class abilities were proposed with data mining technologies to help find "unusual" students in class. The core technology of ITPEA is currently operating on an online-learning system, and obtains good results.

    Abstract

    K-12 education is an important part of educational psychology. Recently, online-learning has been widely accepted because of its significant effect on K-12 education. However, mostly based on the educational database, though existing online-learning systems and intelligent tutoring systems can provide useful resources for teachers and students, they seldom utilize offline test data to offer personalized services. To the end, an intelligent tutoring platform for educational assessment(ITPEA) was proposed and implemented. The platform combines both offline examinations and online resources to offer analysis from the perspectives of test papers, students and teachers. Specifically, based on the data from offline examinations, educational theories were first employed to evaluate the quality of the questions. Then, diagnostic models were constructed on the key knowledge points that students should master to meet one’s personalized demands. Finally, new analytical methods for evaluating teacher’s influence on class abilities were proposed with data mining technologies to help find "unusual" students in class. The core technology of ITPEA is currently operating on an online-learning system, and obtains good results.
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    • References(29)
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    Bovo A, Sanchez S, Héguy O, et al. Analysis of students clustering results based on Moodle log data[C]// 6th International Conference on Educational Data Mining-EDM. Memphis, USA: ACM Press, 2013: 306-307.
    [26]
    Shi N Y, Chen K, Li C H. The application of fuzzy clustering in teacher-evaluating model[C]// IEEE International Symposium on IT in Medicine & Education. IEEE Press, 2009, 1: 872-875.
    [27]
    全通教育[EB/IL]. http://www.qtone.cn/.
    [28]
    猿题库[EB/IL]. http://www.yuantiku.com/.
    [29]
    Knewton[EB/IL]. https://www.knewton.com/.)
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    [1]
    Cha H J, Kim Y S, Park S H, et al. Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system[C]// Proceedings of the 8th International Conference on Intelligent tutoring systems. Taiwan: Springer, 2006: 513-524.
    [2]
    Burns H, Luckhardt C A, Parlett J W, et al. Intelligent Tutoring Systems: Evolutions in Design[M]. Psychology Press, 2014.
    [3]
    Anderson A, Huttenlocher D, Kleinberg J, et al. Engaging with massive online courses[C]// Proceedings of the 23rd International Conference on World Wide Web. Seoul, Korea: ACM Press, 2014: 687-698.
    [4]
    Romero C, Ventura S. Educational data mining: a review of the state of the art[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2010, 40(6): 601-618.
    [5]
    Scheuer O, McLaren B M. Educational data mining[C]//Encyclopedia of the Sciences of Learning. New York, USA: Springer, 2012: 1075-1079.
    [6]
    Baker R S J D, Yacef K. The state of educational data mining in 2009: A review and future visions[J]. JEDM-Journal of Educational Data Mining, 2009, 1(1): 3-17.
    [7]
    Calders T, Pechenizkiy M. Introduction to the special section on educational data mining[J]. ACM SIGKDD Explorations Newsletter, 2012, 13(2): 3-6.
    [8]
    DeVellis R F. Classical test theory[J]. Medical Care, 2006, 44(11S): S50-S59.
    [9]
    Fan X T. Item response theory and classical test theory: An empirical comparison of their item/person statistics[J]. Educational and Psychological Measurement, 1998, 58(3): 357-381.
    [10]
    DiBello L V, Roussos L A, Stout W. 31A Review of Cognitively Diagnostic Assessment and a Summary of Psychometric Models[M]. Handbook of statistics, 2006, 26: 979-1030.
    [11]
    Harwell M R, Baker F B, Zwarts M. Item parameter estimation via marginal maximum likelihood and an EM algorithm: A didactic[J]. Journal of Educational and Behavioral Statistics, 1988, 13(3): 243-271.
    [12]
    de La Torre J. DINA model and parameter estimation: A didactic[J]. Journal of Educational and Behavioral Statistics, 2009, 34(1): 115-130.
    [13]
    Rupp A A, Templin J. The effects of Q-matrix misspecification on parameter estimates and classification accuracy in the DINA model[J]. Educational and Psychological Measurement, 2008, 68(1): 78-96.
    [14]
    Thai-Nghe N, Drumond L, Horváth T, et al. Factorization techniques for predicting student performance[J]. Educational Recommender Systems and Technologies: Practices and Challenges, 2011, 37(2): 157-186.
    [15]
    Mnih A. Salakhutdinov R. Probabilistic matrix factorization[C]//Advances in neural information processing systems , 2007: 1257-1264.
    [16]
    Castro F, Vellido A, Nebot , et al. Applying data mining techniques to e-learning problems[A]// Evolution of Teaching and Learning Paradigms in Intelligent Environment. Berlin Heidelberg: Springer, 2007: 183-221.
    [17]
    Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30-37.
    [18]
    Nichols P D, Chipman S F, Brennan R L, et al. Cognitively Diagnostic Assessment[M]. Mahwah, USA: Lawrence Erlbaum Associates, 1995.
    [19]
    Wu R Z, Liu Q, Liu Y P, et al. Cognitive modelling for predicting examinee performance[C]// International Joint Conference on Artificial Intelligence. Buenos Aires, Argentina: ACM Press, 2015.
    [20]
    Teven J J, McCroskey J C. The relationship of perceived teacher caring with student learning and teacher evaluation[J]. Communication Education, 1997, 46(1): 1-9.
    [21]
    Baker E L, Barton P E, Darling-Hammond L, et al. Problems with the use of student test scores to evaluate teachers[R]. EPI Briefing Paper# 278, Economic Policy Institute, 2010.
    [22]
    Darling-Hammond L, Beardsley A, Haertel E, et al. Evaluating teacher evaluation: What we know about value-added models and other methods[J]. Phi Delta Kappan, 2012, 93(6): 8-15.
    [23]
    Ballou D, Sanders W, Wright P. Controlling for student background in value-added assessment of teachers[J]. Journal of Educational and Behavioral Statistics, 2004, 29(1): 37-65.
    [24]
    任玉丹,边玉芳.美国学校增值性评价模式研究[J].比较教育研究, 2012, (2): 76-79.
    Ren Y D, Bian Y F. Study on the value added assessment System for school in America[J]. Comparative Education Review, 2012, (2): 76-79.
    [25]
    Bovo A, Sanchez S, Héguy O, et al. Analysis of students clustering results based on Moodle log data[C]// 6th International Conference on Educational Data Mining-EDM. Memphis, USA: ACM Press, 2013: 306-307.
    [26]
    Shi N Y, Chen K, Li C H. The application of fuzzy clustering in teacher-evaluating model[C]// IEEE International Symposium on IT in Medicine & Education. IEEE Press, 2009, 1: 872-875.
    [27]
    全通教育[EB/IL]. http://www.qtone.cn/.
    [28]
    猿题库[EB/IL]. http://www.yuantiku.com/.
    [29]
    Knewton[EB/IL]. https://www.knewton.com/.)
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