文章基本信息

标题：Using Educational Data Mining(EDM)to Predictionand 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：9606-9609
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 fromAZADUniversity of Mashhad .We applied the data set to four classifiers (Naive Bayes, LBR,NBTree,Best-First Decision Tree) and obtainedthe accuracy of predicting the students’ performance into either successful or unsuccessful class. The student's academic performance canbe predicted by using past experience knowledge discovered from the existing database. A cross-validation with 10 folds was used to evaluatethe prediction accuracy. The result showed that Naive Bayes classifier scored the higher percentage of prediction F-Measure of 88.7%.
关键词：Data Mining; Prediction; Average; Attributes for predicting students; Educational Data Mining (EDM)