Abstract
Students’ performance prediction can give a prior approximate knowledge of the students’ performance in future academic to the educators. However, it is not any easy task to perform prediction due to the poor identification of parameters and the lack of prediction techniques. In this paper, few parameters will be proposed and the most influenced parameters on students’ performance will be identified using chi squared. The hybrid of Decision Tree and Naïve Bayes algorithms, NBTree will be used to classify the performance of new students. NBTree classifier undergoes the training and testing process using 10-folds cross validation technique and obtained the classification accuracy of 85.9 %, which is better than the accuracy of Decision Tree and Naïve Bayes classifiers which are having 63.7 % and 72.6 % respectively. The classified performance result can be used by the educators to improve the teaching and learning process by developing new teaching methods and new teaching styles.
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We would like to show our gratitude to Universiti Sains Malaysia (USM) for supporting this research.
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Chuan, Y.Y., Husain, W., Shahiri, A.M. (2017). An Exploratory Study on Students’ Performance Classification Using Hybrid of Decision Tree and Naïve Bayes Approaches. In: Akagi, M., Nguyen, TT., Vu, DT., Phung, TN., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2016. Advances in Intelligent Systems and Computing, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-319-49073-1_17
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