{"id":8942,"date":"2024-10-01T03:09:25","date_gmt":"2024-10-01T02:09:25","guid":{"rendered":"https:\/\/icertpublication.com\/?page_id=8942"},"modified":"2026-04-03T09:00:04","modified_gmt":"2026-04-03T03:30:04","slug":"classifying-student-performance-an-in-depth-analysis-using-machine-learning-algorithms","status":"publish","type":"page","link":"https:\/\/icert.org.in\/index.php\/shodh-sari-2\/sodh-sari-vol-3-issue-4\/classifying-student-performance-an-in-depth-analysis-using-machine-learning-algorithms\/","title":{"rendered":"Classifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms"},"content":{"rendered":"\t\t
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Shodh Sari-An International Multidisciplinary Journal<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Vol-03, Issue-04 (Oct-Dec 2024)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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An International scholarly\/ academic journal, peer-reviewed\/ refereed journal, ISSN : 2959-1376<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Classifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms\n<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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 C, Suhasini<\/span>1<\/span><\/span><\/p>\n

1<\/span><\/span>Research Scholar, Statistician, JSS AHER<\/span><\/p>\n

 B, Madhu<\/span>2 <\/span><\/span><\/p>

2<\/span><\/span>Professor & Deputy Dean (Research), JSS AHER<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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DOI: <\/span>https:\/\/doi.org\/10.59231\/SARI7751<\/span><\/a><\/p>

Subject: Educational Data Mining \/ Computer Science<\/span><\/p>

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Received: June 05, 2024<\/span><\/p>

Accepted: Aug 20, 2024<\/span><\/p>

Published: Oct 01, 2024<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Thematic Classification: Student Performance Classification, Machine Learning Algorithms, Predictive Analytics in Education, In-depth Educational Analysis.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Abstract\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\u00a0<\/span>In recent years, analyzing and predicting student performance has been a significant challenge for educational institutions. Early analysis of student data can reveal their strengths and weaknesses, aiding in the improvement of examination outcomes. Machine learning offers a powerful tool for predicting student performance by utilizing demographic and academic data. We can forecast the results and determine which classification algorithm has the highest accuracy from algorithms such as support vector machines, J48 decision trees, naive bayes, and simple logistic regression. As a result, the accuracy level is addressed in the conclusion of data statistics, featuring classification models. By analyzing the classification algorithms, the logistic regression approach provides a more efficient way for educators to identify patterns and trends in the performance of students. This study enables targeted processes for students who would normally perform poorly on examinations.<\/span><\/p>

Keywords:<\/span> machine learning, student performance, algorithm, examination<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Impact statement <\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Title: \u201cClassifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms\u201d:<\/span><\/p>

This study compares various algorithms like\u00a0Support Vector Machine, J48 Decision Tree, Naive Bayes, and Logistic Regression\u00a0to predict student performance. It helps educational institutions improve examination outcomes by identifying students\u2019 strengths and weaknesses early on.\u00a0 The study highlights the effectiveness of machine learning in predicting student performance, aiding educators in identifying patterns and providing targeted support to improve educational outcomes.<\/span><\/p>

The comprehensive student data, including behavioural and psychological factors, to improve prediction accuracy using Real-Time Analysis to Implement real-time data analysis systems to provide immediate feedback and support to students and also formulate educational policies and intervention strategies based on predictive analytics to support at-risk students more effectively.<\/span><\/p>

The Digital Transformation Integrating digital tools and platforms to facilitate online learning, virtual labs, and digital libraries and creating innovation hubs within institutions. The Student Support Services is providing the comprehensive support services, including mental health resources, career counselling, and academic advising to help the student for improving their academic performance.\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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About The Author<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Suhasini C is currently employed as a Statistician at the Department of Bureau of Quality & Statistics, JSS Academy of Higher Education & Research. She completed both her undergraduate (B.Sc.) and postgraduate (M.Sc. in Statistics) degrees at the University of Mysore. With a decade of experience in teaching, Suhasini has served as a Lecturer, specializing statistics in time series analysis and data mining techniques. She has taught Statistics to both MSc and undergraduate students across various colleges in Mysore, Karnataka. Suhasini is also pursuing a Ph.D. in the Faculty of Life Sciences at JSS Academy of Higher Education and Research, Mysuru.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Cite this Article<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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APA 7th Style:<\/span> C, Suhasini, & B, Madhu. (2024). Classifying student performance: An in-depth analysis using machine learning algorithms. <\/span>Shodh Sari-An International Multidisciplinary Journal<\/span>, <\/span>3<\/span>(04), 129\u2013140. <\/span>https:\/\/doi.org\/10.59231\/SARI7751<\/span><\/a><\/p>

Chicago 17th Style:<\/span> C, Suhasini, and Madhu B. “Classifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms.” <\/span>Shodh Sari-An International Multidisciplinary Journal<\/span> 3, no. 4 (2024): 129\u2013140. <\/span>https:\/\/doi.org\/10.59231\/SARI7751<\/span><\/a>.<\/span><\/p>

MLA 9th Style:<\/span> C, Suhasini, and B, Madhu. “Classifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms.” <\/span>Shodh Sari-An International Multidisciplinary Journal<\/span>, vol. 3, no. 4, 2024, pp. 129-140, <\/span>https:\/\/doi.org\/10.59231\/SARI7751<\/span><\/a>.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Statements & Declarations\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Review Method:<\/span> This article underwent a double-blind peer-review process by two independent external experts in Data Science and Educational Statistics to evaluate the algorithmic accuracy and the validity of the predictive models used for student classification.<\/span><\/p>

Competing Interests:<\/span> The author Suhasini C and the author Madhu B declare that there are no financial, personal, or professional conflicts of interest that could have inappropriately influenced the research findings or the comparative analysis of the machine learning models.<\/span><\/p>

Funding:<\/span> This research was conducted as part of the authors’ academic and research activities at JSS Academy of Higher Education & Research (JSS AHER). No specific external grants or commercial funding were received for this work.<\/span><\/p>

Data Availability:<\/span> The analysis is based on academic performance datasets processed through various supervised learning classifiers. The manuscript contains detailed performance metrics including accuracy, precision, and recall for each algorithm. Summarized data are available in the paper, and specific datasets may be shared upon request for academic replication.<\/span><\/p>

License:<\/span> Classifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms<\/span> \u00a9 2024 by Suhasini C and Madhu B is licensed under <\/span>CC BY-NC-ND 4.0<\/span>. This work is published by the International Council for Education Research and Training (ICERT).<\/span><\/p>

Ethics Approval:<\/span> The study utilized anonymized institutional data. As it did not involve direct intervention with human subjects and maintained strict data privacy protocols, it was deemed exempt from formal ethical review by the Institutional Research Committee.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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References<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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      \n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tBack<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

      Shodh Sari-An International Multidisciplinary Journal Vol-03, Issue-04 (Oct-Dec 2024) An International scholarly\/ academic journal, peer-reviewed\/ refereed journal, ISSN : 2959-1376 Classifying Student Performance: An In-Depth Analysis Using Machine Learning Algorithms  C, Suhasini1 1Research Scholar, Statistician, JSS AHER  B, Madhu2  2Professor & Deputy Dean (Research), JSS AHER DOI: https:\/\/doi.org\/10.59231\/SARI7751 Subject: Educational Data Mining \/ Computer Science […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":8763,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"postBodyCss":"","postBodyMargin":[],"postBodyPadding":[],"postBodyBackground":{"backgroundType":"classic","gradient":""},"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-8942","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages\/8942","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/comments?post=8942"}],"version-history":[{"count":12,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages\/8942\/revisions"}],"predecessor-version":[{"id":31563,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages\/8942\/revisions\/31563"}],"up":[{"embeddable":true,"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/pages\/8763"}],"wp:attachment":[{"href":"https:\/\/icert.org.in\/index.php\/wp-json\/wp\/v2\/media?parent=8942"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}