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A Probabilistic Model for Diagnosing Misconceptions By The Pattern Classification Approach

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  • Kikumi K. Tatsuoka

Abstract

This paper introduces a probabilistic approach to the classification and diagnosis of erroneous rules of operations that result from misconceptions (“bugs†) in a procedural domain of arithmetic. The model is different from the usual deterministic strategies common in the field of artificial intelligence because variability of response errors is explicitly treated through item response theory. As a concrete example, we analyze a dataset that reflects the use of erroneous rules of operation in problems of signed-number subtraction. The same approach, however, is applicable to the classification of several different groups of response patterns caused by a variety of different underlying misconceptions, different backgrounds of knowledge, or treatment.

Suggested Citation

  • Kikumi K. Tatsuoka, 1985. "A Probabilistic Model for Diagnosing Misconceptions By The Pattern Classification Approach," Journal of Educational and Behavioral Statistics, , vol. 10(1), pages 55-73, March.
    • Handle: RePEc:sae:jedbes:v:10:y:1985:i:1:p:55-73
    DOI: 10.3102/10769986010001055
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    Cited by:

    1. Hans-Friedrich Köhn & Chia-Yi Chiu, 2017. "A Procedure for Assessing the Completeness of the Q-Matrices of Cognitively Diagnostic Tests," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 112-132, March.
    2. Chia-Yi Chiu & Hans-Friedrich Köhn & Yi Zheng & Robert Henson, 2016. "Joint Maximum Likelihood Estimation for Diagnostic Classification Models," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1069-1092, December.
    3. Yuqi Gu & Jingchen Liu & Gongjun Xu & Zhiliang Ying, 2018. "Hypothesis Testing of the Q-matrix," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 515-537, September.
    4. Chen, Yunxiao & Li, Xiaoou & Liu, Jingchen & Ying, Zhiliang, 2017. "Regularized latent class analysis with application in cognitive diagnosis," LSE Research Online Documents on Economics 103182, London School of Economics and Political Science, LSE Library.
    5. Chia-Yi Chiu & Yan Sun & Yanhong Bian, 2018. "Cognitive Diagnosis for Small Educational Programs: The General Nonparametric Classification Method," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 355-375, June.
    6. Hans-Friedrich Köhn & Chia-Yi Chiu, 2018. "How to Build a Complete Q-Matrix for a Cognitively Diagnostic Test," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 273-299, July.
    7. Yunxiao Chen & Xiaoou Li & Jingchen Liu & Zhiliang Ying, 2017. "Regularized Latent Class Analysis with Application in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 660-692, September.
    8. Chen, Yunxiao & Liu, Jingchen & Xu, Gongjun & Ying, Zhiliang, 2015. "Statistical analysis of Q-matrix based diagnostic classification models," LSE Research Online Documents on Economics 103183, London School of Economics and Political Science, LSE Library.
    9. Hans-Friedrich Köhn & Chia-Yi Chiu, 2019. "Attribute Hierarchy Models in Cognitive Diagnosis: Identifiability of the Latent Attribute Space and Conditions for Completeness of the Q-Matrix," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 541-565, October.
    10. Guanhua Fang & Jingchen Liu & Zhiliang Ying, 2019. "On the Identifiability of Diagnostic Classification Models," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 19-40, March.

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