Using data mining in prediction of educational status (International Journal of Engineering and Computer Science, 2014)
The aim of this paper is to predict the students’ academic performance. It is useful for identifying weak students at an earlier stage. In this study, we used WEKA open source data mining tool to analyze attributes for predicting students’ academic performance. The data set comprised of 180 student records and 21attributes of students registered between year 2010 and 2013. We chosethem from AZADUniversity of Mashhad .We applied the data set to four classifiers (Naive Bayes, LBR,NBTree,Best-First Decision Tree) and obtained the accuracy of predicting the students’ performance into either successful or unsuccessful class. The student's academic performance can be predicted by using past experience knowledge discovered from the existing database. A cross-validation with 10 folds was used to evaluate the prediction accuracy. The result showed that Naive Bayes classifier scored the higher percentage of prediction F-Measure of 88.7%.