A Comparison of the Classification Performances of the DINO Model, Artificial Neural Networks and Non-Parametric Cognitive Diagnosis

The purpose of this study was to compare the attribute (ACR) and pattern-level (PCR) classification rates of the Deterministic-Input, Noisy-Or Gate (DINO) model, Artificial Neural Networks (ANNs), and Non-Parametric Cognitive Diagnosis (NPCD) on simulation datasets. As a comparison condition, the number of attributes, sample size, the number of items, and missing data rate were chosen. A further purpose was to examine the similarities between the classification rates of the DINO model, ANNs, and NPCD on the PISA 2015 collaborative problem -solving (CPS) datasets in various numbers of attributes and sample sizes. For the study, simulation datasets were generated on the basis of the complex Q matrix structures and the DINO model. The conditions for the sample size factor for the real datasets were determined by simple random selection among the participants in the PISA 2015 administration. As a result, it was found that there was a similarity between the DINO model and NPCD classification rates in both simulation and real datasets. In addition, regarding the increase in sample size in both simulation and real datasets, no consistency was found in the increase or decrease of the classification rates of ANNs and NPCD and the similarities of these rates