It is very important to identify the attribute mastery patterns of the
examinee in cognitive diagnosis assessment. There are many methods to classify
the attribute mastery patterns and many studies have been done to diagnose what
the individuals have mastered and or Montel
Carl Computer Simulation is used to study the classification of the attribute
mastery patterns by Deep Learning. Four results were found. Firstly, Deep
Learning can be used to classify the attribute mastery patterns efficiently.
Secondly, the complication of the structures will decrease the accuracy of the
classification. The order of the influence is linear, convergent, unstructured
and divergent. It means that the divergent is the most complicated, and the
accuracy of this structure is the lowest among the four structures. Thirdly,
with the increasing rates of the slipping and guessing, the accuracy of the
classification decreased in verse, which is the same as the existing research
results. At last, the results are influenced by the sample size of the
training, and the proper sample size is in need of deeper discussion.
References
[1]
Leighton, J.P., Gierl, M.J. and Hunka, S.M. (2004) The Attribute Hierarchy Method for Cognitive Assessment: A Variation on Tatsuoka’s Rule-Space Approach. Journal of Educational Measurement, 41, 205-236. https://doi.org/10.1111/j.1745-3984.2004.tb01163.x
[2]
Haertel, E.H. (1989) Using Restricted Latent Class Models to Map the Skill Structure of Achievement Items. Journal of Educational Measurement, 26, 301-321. https://doi.org/10.1111/j.1745-3984.1989.tb00336.x
[3]
De la Torre, J. and Douglas, J.A. (2004) Higher-Order Latent Trait Models for Cognitive Diagnosis. Psychometrika, 69, 333-353. https://doi.org/10.1007/BF02295640
[4]
De la Torre, J. (2011) The Generalized DINA Model Framework. Psychometrika, 76, 179-199. https://doi.org/10.1007/s11336-011-9207-7
[5]
Decarlo, L.T. (2011) On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix. Applied Psychological Measurement, 35, 8-26. https://doi.org/10.1177/0146621610377081
Ma, W.C. and De la Torre, J. (2016) A Sequential Cognitive Diagnosis Model for Polytomous Responses. British Journal of Mathematical and Statistical Psychology, 69, 253-275. https://doi.org/10.1111/bmsp.12070
[8]
Huang, H.-Y. (2017) Multilevel Cognitive Diagnosis Models for Assessing Changes in Latent Attributes. Journal of Educational Measurement, 54, 440-480. https://doi.org/10.1111/jedm.12156
[9]
Cui, Y., Gierl, M. and Guo, Q. (2016) Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach. Educational Psychology, 36, 1065-1082. https://doi.org/10.1080/01443410.2015.1062078
[10]
Kang, C.H., Ren, P. and Zhen, P.F. (2015) Nonparametric Cognitive Diagnosis: A Cluster Diagnostic Method Based on Grade Response Items. Acta Psychologica Sinica, 47, 1077-1088. https://doi.org/10.3724/SP.J.1041.2015.01077
[11]
Kang, C.H., Ren, P. and Zhen, P.F. (2016) The Influence Factors of Grade Response Cluster Diagnostic Method. Acta Psychologica Sinica, 48, 891-902. https://doi.org/10.3724/SP.J.1041.2016.00891
[12]
Chiu, C.Y., Sun, Y. and Bian, Y.H. (2018) Cognitive Diagnosis for Small Educational Programs: The General Nonparametric Classification Method. Psychometrika, 83, 355-375. https://doi.org/10.1007/s11336-017-9595-4
[13]
Luo, Z.S., Yu, X.F., Gao, C.L. and Peng, Y.F. (2015) A Simple Cognitive Diagnosis Method Based on Q-Matrix Theory. Acta Psychologica Sinica, 47, 264-272. https://doi.org/10.3724/SP.J.1041.2015.00264
[14]
Guo, L., Yang, J. and Song, N.Q. (2018) Application of Spectral Clustering Algorithm under Various Attribute Hierarchical Structures for Cognitive Diagnostic Assessment. Journal of Psychological Science, 41, 735-742.
[15]
Gierl, M.J., Cui, Y. and Hunka, S. (2008) Using Connectionist Models to Evaluate Examinees’ Response Patterns on Tests. Journal of Modern Applied Statistical Methods, 7, 234-245. https://doi.org/10.22237/jmasm/1209615480
[16]
Guo, Q., Cutumisu, M. and Cui, Y. (2017) A Neural Network Approach to Estimate Student Skill Mastery in Cognitive Diagnostic Assessments. Proceedings of the 10th International Conference on Educational Data Mining, Wuhan, 25-28 June 2017, 370-371.
[17]
Tu, D.B., Cai, Y. and Dai, H.Q. (2013) Comparison and Selection of Five Noncompensatory Cognitive Diagnosis Models Based on Attribute Hierarchy Structure. Acta Psychologica Sinica, 45, 243-252. https://doi.org/10.3724/SP.J.1041.2013.00243
[18]
Cai, Y., Tu, D.B. and Ding, S.L. (2013) A Simulation Study to Compare Five Cognitive Diagnostic Models. Acta Psychologica Sinica, 45, 1295-1304. https://doi.org/10.3724/SP.J.1041.2013.01295
[19]
Gao, X.L., Wang, D.X., Yan, C. and Tu, D.B. (2018) Comparison of CDM and Its Selection: A Saturated Model, a Simple Model or a Mixed Method. Journal of Psychological Science, 41, 727-734.
[20]
Bengio, Y., Lamblin, P., Popovici, D. and Larochelle, H. (2007) Greedy Layer-Wise Training of Deep Networks. In: Advances in Neural Information Processing Systems 19, MIT Press, Cambridge, 153-160.
[21]
Schmidhuber, J. (2015) Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
[22]
Hinton, G.E., Osindero, S. and Teh, Y.-W. (2006) A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18, 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
[23]
Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning Representation by Back-Propagating Errors. Nature, 323, 533-536. https://doi.org/10.1038/323533a0
[24]
Lecun, Y. (1987) Modeles connectionistes de l’appretissage. Ph.D. Thesis, University de Paris VI, Paris.
[25]
Tatsuoka, K.K. (1995) Architecture of Knowledge Structures and Cognitive Diagnosis: A Statistical Pattern Recognition and Classification Approach. In: Nichols, P.D., Chipman, S.F. and Brennan, R.L., Eds., Cognitively Diagnostic Assessment, Earlbaum, Hillsdale, 327-359.
[26]
Tatsuoka, K.K. (2009) Cognitive Assessment—An Introduction to the Rule Space Method. Routledge, New York. https://doi.org/10.4324/9780203883372
[27]
Ding, S.L., Wang, W.Y. and Luo, F. (2012) The Q Matrix and the Q Matrix Theory in the Cognitive Diagnosis Assessment. Journal of Jiangxi Normal University (Natural Science Edition), 36, 441-445.
[28]
Hu, J.X., Miller, M.D., Corinne, A. and Chen, Y.-H. (2016) Evaluation of Model Fit in Cognitive Diagnosis Models. International Journal of Testing, 16, 119-141. https://doi.org/10.1080/15305058.2015.1133627
[29]
Cohen, A.S., Kane, M.T. and Kim, M.S.H. (2001) The Precision of Simulation Study Results. Applied Psychological Measurement, 25, 136-145. https://doi.org/10.1177/01466210122031966
[30]
Wang, W.Y., Song, Y.H. and Ding, S.L. (2016) Application of Neural Networks and Support Vector Machines to Cognitive Diagnosis. Journal of Psychological Science, 39, 777-782.
