Predicting Student Academic Achievement by Using the Decision Tree and Neural Network Techniques
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Abstract
The aims of this study are 1) to study the prediction accuracy rate between the two data mining techniques: decision tree and neural network in classifying a group of student academic achievement, 2) to analyze factors affecting academic achievement that contribute to the prediction of students’ academic performance. In this study, the researcher used WEKA open source data mining tool to analyze attributes for predicting undergraduate students’ academic performance in an international program. The data set comprised of 1,600 student records with 22 attributes of students registered between year 2001 and 2011 in a university in Thailand. Preprocessing included attribute importance analysis. The researcher applied the data set to differentiate classifiers (Decision Tree, Neural Network). A cross-validation with 10 folds was used to evaluate the prediction accuracy. An experimental comparison of the performance of the classifiers was also conducted. Results show that the decision tree classifier achieves high accuracy of 85.188%, which is higher than that of neural network classifier by 1.313%.
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