文章基本信息

标题：Using Educational Data Mining (EDM) to Prediction and Classify Students
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作者：Samira Talebi ; Ali Asghar Sayficar
期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2014
卷号：3
期号：12
页码：9395-9398
出版社：IJECS
摘要：The aim of this paper is to predict the students’ academic performance. It is useful for identifying weak students at an earlierstage. In this study, we used WEKA open source data mining tool to analyze attributes for predicting students’ academic performance. Thedata set comprised of 180 student records and 21attributes of students registered between year 2010 and 2013. We chosethem fromFERDOWSIUniversity of Mashhad .We applied the data set to four classifiers (Naive Bayes, LBR,NBTree, Best -First Decision Tree) andobtained the accuracy of predicting the students’ performance into either successful or unsuccessful class. The student's academicperformance can be predicted by using past experience knowledge discovered from the existing database. A cross-validation with 10 foldswas used to evaluate the prediction accuracy. The result showed that Naive Bayes classifier scored the higher percentage of prediction FMeasureof 83.9%.
关键词：Data Mining; Prediction; Average; Attributes for predicting students; Educational Data Mining (EDM)