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A Comprehensive Comparison of Bayesian Networks Classification Model and Sequential Polytomous Scoring Diagnosis Model
Received date: 2022-01-16
Online published: 2023-03-23
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In recent years, Bayesian networks have been widely used in the field of artificial intelligence, but have received relatively little attention in the field of psychology. Most of the existing studies have applied Bayesian networks to binary items. This research combines Bayesian networks with polytomous items, which has important theoretical and practical implications. Two Bayesian network classification models were comprehensively compared with the sequential-GDINA (S-GDINA). The results showed that the classification performance of the naive Bayesian classifier (NBC) was comparable to that of the S-GDINA model, which could achieve equally good classification performance. Also, the classification performance of the tree augmented naive Bayesian classifier (TAN) could achieve good classification performance when the percentage of errors contained in the Q matrix was 0% (the Q matrix was correctly defined by the expert). Both the NBC and the TAN had better classification results in the polytomous scored test. The response classification consistency of the NBC and TAN reached 84% and 71%, respectively, indicating that Bayesian networks can be well applied to polytomous scored items.
Key words： cognitive diagnosis; S-GDINA; Bayesian networks
Xiaofeng YU , Yuchun XIAO , Chunying QIN . A Comprehensive Comparison of Bayesian Networks Classification Model and Sequential Polytomous Scoring Diagnosis Model[J]. Studies of Psychology and Behavior, 2023 , 21(1) : 49 -57 . DOI: 10.12139/j.1672-0628.2023.01.008
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