베이지언 네트워크를 이용한 인지진단모형의 적용

Abstract

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.