文章基本信息

标题：Prediction of Student’s Performance using Selected Classification Methods: A Data Mining Approach
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作者：Abba Babakura ; Abubakar Roko ; Aminu Bui 等
期刊名称：International Journal of Computer Science and Network
印刷版ISSN：2277-5420
出版年度：2019
卷号：8
期号：3
页码：276-284
出版社：IJCSN publisher
摘要：Educational Data Mining (EDM) research have emerged as an interesting area of research, which are extracting useful knowledge from educational databases for purposes such as predicting student’s success. The extracted knowledge helps the institutions to improve their teaching methods and learning process. In this paper, we applied Decision Tree, Naïve Bayes and Neural Network classification methods for predicting the student’s performance based on the grade level. This aim to resolve the problem of difficulty in predicting the performance of student’s in institutions. The objectives of this paper are to (i) implement three classification methods independently on the student’s performance dataset, and (ii) determine the best method among the three classification methods. The results shows that the Decision Tree produces the highest accuracy rate of 77.778%, followed by the Neural Network with accuracy rate of 70.886% and the Naïve Bayes produces the lowest at accuracy rate 66.865%. The result recommendsthat Decision Tree is used in predicting student’s performance rather than Naïve Bayes and Neural Network.
关键词：Educational Data Mining; Prediction; Student performance; Decision Tree; Neural Network and Naïve Bayes