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2017 | OriginalPaper | Chapter

An Exploratory Study on Students’ Performance Classification Using Hybrid of Decision Tree and Naïve Bayes Approaches

Authors : Yoong Yen Chuan, Wahidah Husain, Amirah Mohamed Shahiri

Published in: Advances in Information and Communication Technology

Publisher: Springer International Publishing

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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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Metadata
Title
An Exploratory Study on Students’ Performance Classification Using Hybrid of Decision Tree and Naïve Bayes Approaches
Authors
Yoong Yen Chuan
Wahidah Husain
Amirah Mohamed Shahiri
Copyright Year
2017
Book
Advances in Information and Communication Technology

Print ISBN: 978-3-319-49072-4

Electronic ISBN: 978-3-319-49073-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-49073-1_17

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