Longitudinal Cognitive Diagnostic Assessment Based on the HMM/ANN Model

Abstract

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Cognitive diagnostic assessment (CDA) is able to obtain information regarding the student’s cognitive and knowledge development based on the psychometric model. Notably, most of previous studies use traditional cognitive diagnosis models (CDMs). This study aims to compare the traditional CDM and the longitudinal CDM, namely, the hidden Markov model (HMM)/artificial neural network (ANN) model. In this model, the ANN was applied as the measurement model of the HMM to realize the longitudinal tracking of students’ cognitive skills. This study also incorporates simulation as well as empirical studies. The results illustrate that the HMM/ANN model obtains high classification accuracy and a correct conversion rate when the number of attributes is small. The combination of ANN and HMM assists in effectively tracking the development of students’ cognitive skills in real educational situations. Moreover, the classification accuracy of the HMM/ANN model is affected by the quality of items, the number of items as well as by the number of attributes examined, but not by the sample size. The classification result and the correct transition probability of the HMM/ANN model were improved by increasing the item quality and the number of items along with decreasing the number of attributes.

Keywords

• cognitive diagnostic assessment
• longitudinal assessment
• hidden Markov model
• SSOM neural network
• reading comprehension