Application of Naive Bayesian Classifier for Teaching Reform Courses Examination Data Analysis in China Open University System

Abstract

Open education quality guarantee is a core issue in field of distance education. Data mining techniques are used to design effective teaching reform courses examination data analysis method would be a good way for checking teaching reform effects and could provide objective basis for open education quality assurance. This paper proposes a teaching reform courses examination data analysis solution based on Naive Bayesian classifier for checking the impacts of teaching reform measures act on open education quality. Naive Bayesian classifier is a famous classifying method, as a supervised learning, can extract valuable classifying rules by using data whose class label is known to train the classifier, and the trained classifier or classifying rules can be used to classify new data whose class label is unknown, and who is based on Bayes principal, has characteristics of accuracy and fast in aspect of classifying data in large scale database. Proposed solution's effectiveness is verified by processing practical teaching reform courses examination data in China Open University system. Hidden rules in teaching reform courses examination data are revealed, and also changing conditions of examination data caused by teaching reform measures are presented, which would be valuable in aspect of modifying open education quality assurance measures. © 2015 IEEE.