Bilişsel Tanı Modellerinde Madde Düzeyinde Tanımlanmış En Uygun İndirgenmiş Modelinin Belirlenmesi

Year 2023, , 1526 - 1535, 29.10.2023

Mahmut Sami Koyuncu , Şeref Tan

https://doi.org/10.38151/akef.2023.125

Abstract

Bu çalışmanın amacı, Q-matrisinde en az iki nitelik gerektiren maddeler için madde düzeyinde tanımlanan en uygun indirgenmiş modelin WALD, LR, 2LR ve LM test yöntemlerine göre belirlenmesidir. Ayrıca her bir madde için belirlenen indirgenmiş model kullanılarak tanımlanan modelin bağıl ve mutlak model uyum değerlerini G-DINA model uyum değerleriyle karşılaştırmak amaçlanmıştır. Araştırmanın çalışma grubu Ankara ilinde yedi farklı lisede 9.sınıfta öğrenim gören 712 öğrenciden oluşmaktadır. Çalışmada verileri araştırmacı tarafından geliştirilen beş niteliğin yoklandığı, çoktan seçmeli ve 26 maddeden oluşan tanılayıcı bir matematik testinden elde edilmiştir. Verilerin analizi RStudio yazılımında “GDINA” paket 2.9.4 versiyonu ile gerçekleştirilmiştir. Çalışma sonucunda, Wald, LR ve 2LR test yöntemlerinde en çok LLM modelinin, LM test yönteminde ise DINA modelin maddeler için en uygun indirgenmiş model olarak belirlendiği tespit edilmiştir. Tüm modellerde mutlak model uyum değerlerinin iyi olduğu, en düşük RMSEA değerinin G-DINA modelde, en düşük SRMSR değerinin ise TMLM modelinde olduğu belirlenmiştir. TMwald ve TMLM modellerinin G-DINA model kadar iyi uyuma sahip olmadığı, TMLR ve TM2LR modellerinin G-DINA model kadar iyi model uyumuna sahip olduğu belirlenmiştir. Bu modellerde madde düzeyinde sadeleştirmeye gidilebileceği ve G-DINA model yerine sadeleştirilmiş bu modellerin kullanılabileceği sonucuna ulaşılmıştır. Araştırmacılara madde düzeyinde hangi yöntem kullanılarak daha güvenilir ve geçerli bir şekilde model seçilebileceği konusunda çalışmalar yapması önerilmektedir.

Keywords

Bilişsel Tanı Modeli, G-DINA Model, Madde Düzeyinde Model, Model Uyumu.

Supporting Institution

YOK

Project Number

YOK

Thanks

Bu makale birinci yazarın doktora tezinin bir kısmından üretilmiştir. Doktora eğitimim süresince sağladığı maddi destekten ötürü Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK)’na teşekkürlerimi sunarım.

Determination of the Most Appropriate Reduced Model Defined at the Item Level in Cognitive Diagnostic Models

Abstract

This study aims to identify the most suitable reduced model at the item level for items requiring a minimum of two attributes in the Q-matrix utilizing the WALD, LR, 2LR, and LM test methods. Additionally, the study aims to compare the relative and absolute model fit values of the reduced model for each item with those of the G-DINA model. The research participants consists of 712 9th grade students attending seven different high schools in Ankara. Data were collected from a diagnostic mathematics test comprising 26 multiple-choice items developed by the researcher to assess five attributes. The analysis employed the "GDINA" package version 2.9.4 within RStudio software. The study's findings indicate that the LLM model serves as the most appropriate reduced model for items based on the Wald, LR, and 2LR test methods, while the DINA model provides the most suitable reduced model for items in the LM test method. All models exhibited good absolute model fit values; with the G-DINA model displaying the lowest RMSEA value, and the TMLM model showing the lowest SRMSR value. It was determined that TMwald and TMLM models did not exhibit as good a fit as the G-DINA model, whereas TMLR and TM2LR models displayed a model fit as good as that of the G-DINA model. Consequently, the study concludes that these models can be simplified at the item level and used as alternativesto the G-DINA model. It is recommended that researchers undertake further investigations into the methods for selecting item-level models that are both reliably and validly.

Keywords

Cognitive Diagnostic Model, G-DINA Model, Item-Level Model, Model Fit.

Project Number

YOK

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