A Semi-supervised Learning-Based Diagnostic Classification Method Using Artificial Neural Networks.

ABSTRACT: The purpose of cognitive diagnostic modeling (CDM) is to classify students' latent attribute profiles using their responses to the diagnostic assessment. In recent years, each diagnostic classification model (DCM) makes different assumptions about the relationship between a student's response pattern and attribute profile. The previous research studies showed that the inappropriate DCMs and inaccurate Q-matrix impact diagnostic classification accuracy. Artificial Neural Networks (ANNs) have been proposed as a promising approach to convert a pattern of item responses into a diagnostic classification in some research studies. However, the ANNs methods produced very unstable and unappreciated estimation unless a great deal of care was taken. In this research, we combined ANNs with two typical

SUBMITTER: Xue K 

PROVIDER: S-EPMC7856146 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature