TY  - JOUR
TI  - A Comparison of the Classification Performances of the DINO Model, Artificial Neural Networks and Non-Parametric Cognitive Diagnosis
AB  - 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) from datasets of simulation in various conditions such as the number of attributes, sample size, the number of items and missing data rate. 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 purpose of 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.
AU  - Yavuz, Emine
AU  - Atar, Hakan Yavuz
DO  - 10.21031/epod.1246752
PY  - 2023
JO  - Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi
VL  - 14
IS  - 4
SN  - 1309-6575
SP  - 413
EP  - 439
DB  - TRDizin
UR  - http://search/yayin/detay/1220809
ER  -