In this paper, our study aimed to use Grey entropy to help decide which attributes, so called items in educational assessment, should be eliminated to prevent the Bayesian network modeling process from over-fitting and to obtain better accuracy. Although Bayesian network is proving to be the best technology available for diagnosing students' learning status in educational assessment, in the process of constructing a Bayesian network, the criteria of selecting testing attributes such as items or tasks will influence the diagnosing accuracy. Experiment results indicats that the Bayesian network with Grey entropy data pre-processing obtains the better more than 10% in accuracy than the man-made Bayesian network.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.