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Journals / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2023 / Volume: 14 Issue: 4

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

Emine Yavuz(Erciyes Üniversitesi)0000-0002-1991-1416
Hakan Yavuz Atar(GAZI UNIVERSITY, GAZİ FACULTY OF EDUCATION)0000-0001-5372-1926
Pages
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Publication Date
2023-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

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Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:2015 Verileri
⚙️Variables:diagnostic assessment · non-parametric cognitive diagnosis · artificial neural network

Abstract

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.

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Cite This Article

Yavuz, E., Atar, H. Y. (2023). A Comparison of the Classification Performances of the DINO Model, Artificial Neural Networks and Non-Parametric Cognitive Diagnosis. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Volume: 14 Issue: 4. https://doi.org/10.21031/epod.1246752

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