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<!doctype html> <html lang="en"> <head> <meta charset="utf-8" /> <title>Evaluation of Student Academic Performance Using Naïve Bayes Classifier-Advances in Computer and Communication-Hill Publishing Group</title> <meta content="width=device-width, initial-scale=1" name="viewport" /> <meta content="Evaluation of Student Academic Performance Using Naïve Bayes Classifier-Advances in Computer and Communication-Hill Publishing Group" name="description"/> <meta content="Advances in Computer and Communication-Hill Publishing Group" name="keywords" /> <meta name="title" content="Evaluation of Student Academic Performance Using Naïve Bayes Classifier" /> <meta name="author" content="Khin Shin Thant" /> <meta name="author" content="Ei Theint Theint Thu" /> <meta name="author" content="Myat Mon Khaing" /> <meta name="author" content="Khin Lay Myint" /> <meta name="author" content="Hlaing Htake Khaung Tin" /> <meta name="description" content="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." /> <meta name="DOI" content="http://dx.doi.org/10.26855/acc.2020.12.005"/> <meta name="dc.title" content="Evaluation of Student Academic Performance Using Naïve Bayes Classifier" /> <meta name="dc.source" content="Advances in Computer and Communication" /> <meta name="dc.creator" content="Khin Shin Thant" /> <meta name="dc.creator" content="Ei Theint Theint Thu" /> <meta name="dc.creator" content="Myat Mon Khaing" /> <meta name="dc.creator" content="Khin Lay Myint" /> <meta name="dc.creator" content="Hlaing Htake Khaung Tin" /> <meta name="dc.type" content="Article"/> <meta name="dc.date" content="2021/2/24 0:00:00" /> <meta name="dc.identifier" content="DOI:http://dx.doi.org/10.26855/acc.2020.12.005" /> <meta name="dc.publisher" content="Hill Publishing Group Inc" /> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/" /> <meta name="dc.format" content="text/html" /> <meta name="dc.language" content="en" /> <meta name="dc.description" content="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." /> <meta name="dc.subject" content="Evaluation, Data Mining, Students’ Performance, Classification, Naïve Bayes" /> <meta name="dc.copyright" content="Advances in Computer and Communication" /> <meta name="dc.rightsAgent" content="acc@hillpublisher.com" /> <meta name="prism.publicationName" content="Advances in Computer and Communication" /> <meta name="prism.publicationDate" content="2021/2/24 0:00:00" /> <meta name="prism.volume" content="2" /> <meta name="prism.issue" content="1" /> <meta name="prism.rightsAgent" content="acc@hillpublisher.com" /> <meta name="prism.copyright" content="Advances in Computer and Communication" /> <meta name="prism.url" content="https://www.hillpublisher.com/ArticleDetails/506" /> <meta name="prism.doi" content="http://dx.doi.org/10.26855/acc.2020.12.005" /> <meta name="prism.section" content="Article" /> <meta name="prism:pdfurl" content="https://www.hillpublisher.com/UpFile/202103/20210304150010.pdf" /> <meta name="og:url" content="https://www.hillpublisher.com/ArticleDetails/506" /> <meta name="og:type" content="Article" /> <meta name="og:site_name" content="HillPublisher" /> <meta name="og:title" content="Evaluation of Student Academic Performance Using Naïve Bayes Classifier" /> <meta name="og:description" content="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." /> <meta name="citation_title" content="Evaluation of Student Academic Performance Using Naïve Bayes Classifier" /> <meta name="citation_description" content="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." /> <meta name="citation_author" content="Khin Shin Thant" /> <meta name="citation_author" content="Ei Theint Theint Thu" /> <meta name="citation_author" content="Myat Mon Khaing" /> <meta name="citation_author" content="Khin Lay Myint" /> <meta name="citation_author" content="Hlaing Htake Khaung Tin" /> <meta name="citation_publication_date" content="2021/2/24 0:00:00" /> <meta name="citation_journal_title" content="Advances in Computer and Communication" /> <meta name="citation_volume" content="2" /> <meta name="citation_issue" content="1" /> <meta name="citation_doi" content="http://dx.doi.org/10.26855/acc.2020.12.005" /> <meta name="citation_id" content="506" /> <meta name="citation_fulltext_html_url" content="https://www.hillpublisher.com/ArticleDetails/506" /> <meta name="citation_pdf_url" content="https://www.hillpublisher.com/UpFile/202103/20210304150010.pdf" /> <link href="/css/hillpub.css" type="text/css" rel="stylesheet"> <script type="text/ecmascript" src="/js/tab.js"></script> <script type="text/javascript" src="/js/jquery1.3.2.js"></script> <script type="application/ld+json"> { "@context": "https://schema.org", "@type": "Article", "name": "Evaluation of Student Academic Performance Using Naïve Bayes Classifier", "author": "Khin Shin Thant, Ei Theint Theint Thu, Myat Mon Khaing, Khin Lay Myint, Hlaing Htake Khaung Tin", "datePublished": "2021/2/24 0:00:00", "url": "https://www.hillpublisher.com/ArticleDetails/506", "identifier": "http://dx.doi.org/10.26855/acc.2020.12.005", "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. 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class="type_doi"><i>Article</i> <i style="color: #8e1530">Open Access</i> <span>http://dx.doi.org/10.26855/acc.2020.12.005</span></div> <h1 class="art_title">Evaluation of Student Academic Performance Using Naïve Bayes Classifier</h1> <div class="art-authors"> <p style="padding-bottom: 15px; margin-bottom: 0px; outline: none; line-height: 22px; font-size: 16px; color: rgb(0, 0, 0); font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei" !important;"><span style="font-family: Poppins; font-size: 14px;">Khin Shin Thant</span><sup><span style="font-family: Poppins; font-size: 14px;">*</span></sup><span style="font-family: Poppins; font-size: 14px;">, Ei Theint Theint Thu, Myat Mon Khaing, Khin Lay Myint, Hlaing Htake Khaung Tin</span></p><p style="padding-bottom: 15px; margin-bottom: 0px; outline: none; line-height: 22px; font-size: 16px; color: rgb(0, 0, 0); font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei" !important;"><span style="font-family: Poppins; font-size: 14px;">Faculty of Information Science, University of Computer Studies, Hinthada, Myanmar.</span></p><p style="padding-bottom: 15px; margin-bottom: 0px; outline: none; line-height: 22px; font-size: 16px; color: rgb(0, 0, 0); font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei" !important;"><strong><span style="font-family: Poppins; font-size: 14px;">*Corresponding author:</span></strong><span style="font-family: Poppins; font-size: 14px;"> Khin Shin Thant</span></p> </div> <div class="art-affiliations"> </div> <div class="art-pubhistory"> Published: February 24,2021 </div> <ul class="wzzhbox1"> <li id="left" class="preAfter" style="display:block;"> <a href="/ArticleDetails/436"> <span>Previous article in journal</span> </a> </li> <li id="right" class="next" style="display:block;"> <a href="/ArticleDetails/536"> <span>Next article in journal</span> </a> </li> </ul> <div class="abt"> <h2>Abstract</h2> <p><p>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.</p></p> </div> <div class="re" > <h2>References</h2> <p>[1]<span style="white-space: pre;"> </span>Hafez Mousa, Ashraf Maghari. (2017). “School Students’ Performance Prediction Using Data Mining Classifica-tion”, pp. 136-141, JARCCE, 2017.</p><p>[2]<span style="white-space: pre;"> </span>A. Dinesh Kumar, R. Pandi Selvam, V. Palanisamy. (2019). “Prediction of Student Performance using Hybrid Clas-sification”, IJRTE, pp 6566-6570, 2019.</p><p>[3]<span style="white-space: pre;"> </span>Y. Divyabharathi, P. Someswari. (2018). “A Framework for Student Academic Performance Using Naïve Bayes Classification”, JAET, pp. 1-4, 2018.</p><p>[4]<span style="white-space: pre;"> </span>Brijesh Kumar Baradwaj. (2011). “Mining Educational Data to Analyze Students’ Performance”, pp. 63-69, 2011.</p><p>[5]<span style="white-space: pre;"> </span>Ahmed S. J. Abu Hammad. (2018). “Mining Educational Data to Analyze Students’ Performance (A Case with University of Science and Technology Students)”. Pp. 56-65, 2018.</p><p>[6]<span style="white-space: pre;"> </span>Pooja Thakar, Anil Mehta, Ph. D, Manisha, Ph.D. (2015). “Performance Analysis and Prediction in Educational Data Mining: A Research Travelogue”. Pp. 60-68, 2015.</p><p>[7]<span style="white-space: pre;"> </span>Kalpesh P. Chaudhari, Riya A. Sharma, Shreya S. Jha, Rajeshwari J. Bari. (2017). “Student Performance Prediction System using Data Mining Approach”, IAJRCCE, pp. 833-839, 2017.</p> </div> <div class="ftex" id="fu"> <h2>How to cite this paper</h2> <p style="padding-bottom: 15px; margin-bottom: 0px; outline: none; line-height: 22px; font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei"; font-size: 16px; color: rgb(0, 0, 0); font-weight: bold;"><span style="font-family: Poppins; font-size: 14px;">Evaluation of Student Academic Performance Using Naïve Bayes Classifier</span></p><p style="padding-bottom: 15px; margin-bottom: 0px; outline: none; line-height: 22px; font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei"; font-size: 16px; color: rgb(0, 0, 0);"><b><span style="font-family: Poppins; font-size: 14px;">How to cite this paper:</span></b><span style="font-family: Poppins; font-size: 14px;"> 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. </span><i><span style="font-family: Poppins; font-size: 14px;">Advances in Computer and Communication</span></i><span style="font-family: Poppins; font-size: 14px;">, <b>1</b></span><span style="font-family: Poppins; font-size: 14px;">(</span><b><span style="font-family: Poppins; font-size: 14px;">1</span></b><span style="font-family: Poppins; font-size: 14px;">), 46-52.</span></p><p style="padding-bottom: 15px; margin-bottom: 0px; outline: none; line-height: 22px; font-family: Arial, sans-serif, "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei"; font-size: 16px; color: rgb(0, 0, 0);"><span style="font-family: Poppins; font-size: 14px;">DOI: http://dx.doi.org/10.26855/acc.2020.12.005</span></p> </div> </div>  </div>  </div>  <div class="footer"> <div class="foot_nav"><a href="/Index" title="Home">Home</a>　|　<a href="/Journals" title="Journals">Journals</a>　|　<a href="/Books" title="Books">Books</a>　|　<a href="/ForAuthors" title="For Authors and Reviewers">For Authors and Reviewers</a>　|　<a href="/OnlineSubmission" title="Online Submission">Online Submission</a>　|　<a href="ContactUs" title="Contact Us">Contact Us</a>　|　<a href="/AboutUs" title="About Us">About Us</a></div> <p>Copyright © 2023 Hill Publishing Group Inc. 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