An Application of Bayesian Network Based Cognitive Diagnostic Modeling

Bayesian networks(BN) are a probabilistic graphical model that represents a system of variables and their conditional interdependencies using directed acyclic graphs. Once the probabilistic models are built and embedded in a graphical structure, evidence of variables can be efficiently propagated through the network and inference about students can be made. This study is to explore the potential of BN as a new framework for cognitive diagnostic modeling by developing and applying BN-based cognitive diagnostic models to a real test data set and comparing the results with those from the conventional cognitive diagnostic modeling. For this purpose, the current study surveyed theoretical and pedagogical aspects of reading in English as a foreign/second language, introduced provisional models, and applied the models to a set of reading comprehension data from a TOEIC practice test. The results show that BN-based cognitive diagnostic modeling can be an efficient framework that can handle diverse aspects of reading comprehension. The study then addresses some issues from the analysis and discusses the implications in relation to application of cognitive diagnostic modeling.