A Comparative Analysis of Classification Algorithms on Students’ Performance

  • Nilar Aye University of Computer Studies, Yangon
Keywords: Data Mining, Error Measurement, Accuracy, Naive Bayesian, BayesNet, J48, Decision Stump

Abstract

Recently educational system, many features control a student’s performance. Students should be well stimulated to study their education. Motivation leads to interest, interest leads to success in their lives. Appropriate assessment of abilities encourages the students to do better in their education. Data mining is to find out patterns by analyzing a large dataset and apply those patterns to predict the possibility of the future events. Data mining is a very critical field in educational area and it provides high potential for the schools and universities. In data mining, there are various classification techniques with various levels of accuracy. This paper focuses to make comparative evaluation of four classifiers such as J48, Naive Bayesian, Bayesian Network and Decision Stump by using WEKA tool.  This study is to investigate and identify the best classification technique to analyze and predict the students’ performance of University of Jordan.

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Published
2020-04-30
How to Cite
Aye, N. (2020). A Comparative Analysis of Classification Algorithms on Students’ Performance. Transactions on Networks and Communications, 8(2), 20-34. https://doi.org/10.14738/tnc.82.8267