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Advances in Computer and Communication

DOI:http://dx.doi.org/10.26855/acc.2020.12.005

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Evaluation of Student Academic Performance Using Naïve Bayes Classifier

Khin Shin Thant*, Ei Theint Theint Thu, Myat Mon Khaing, Khin Lay Myint, Hlaing Htake Khaung Tin

Faculty of Information Science, University of Computer Studies, Hinthada, Myanmar.

*Corresponding author: Khin Shin Thant

Date: February 24,2021 Hits: 786

Abstract

The best way to achieve the highest quality in the higher education system is to increase the ability of students to improve their performance. Students’ ability to find information is called rules for identifying information. The Bayesian Classification represents a supervised learning method as well as a statistical method for classification. Data mining is used to find knowledge that may be useful in applying active learning in the technology perspective. There are two approaches which can be used to discover knowledge by data mining techniques such as prediction, classification, clustering, association rule and statistical methods. Among them, this paper predicts using data mining classification method. The data used in this paper is based on the student records that collected from department at the end of each semester. The main purpose of this paper is to assess students’ ability to study in quality education and to assess student performance through the Naïve Bayes classification. These findings will be very helpful to teach data mining subject in the coming year for students.

References

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Evaluation of Student Academic Performance Using Naïve Bayes Classifier

How to cite this paper: Khin Shin Thant, Ei Theint Theint Thu, Myat Mon Khaing, Khin Lay Myint, Hlaing Htake Khaung Tin. (2020) Evaluation of Student Academic Performance Using Naïve Bayes Classifier. Advances in Computer and Communication, 1(1), 46-52.

DOI: http://dx.doi.org/10.26855/acc.2020.12.005