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Digital Literacy: Comparative Review on Machine Learning Based Performance Assessment of Students | EAI Endorsed Transactions on Internet of Things
<!DOCTYPE html> <html lang="en-US" xml:lang="en-US"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Digital Literacy: Comparative Review on Machine Learning Based Performance Assessment of Students | EAI Endorsed Transactions on Internet of Things </title> <link rel="icon" href="https://publications.eai.eu/public/journals/6/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.3.0.18"> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="K. Shwetha"/> <meta name="DC.Creator.PersonalName" content="S. Shahar Banu"/> <meta name="DC.Date.created" scheme="ISO8601" content="2024-11-15"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2024-07-22"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-11-08"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-11-15"/> <meta name="DC.Description" xml:lang="en" content="The E-learning system paved an opportunity to make drastic changes in the educational system all over the world. Several institutions began to implement online learning to offer internet based courses contrary to the traditional classroom teaching. These online courses tends to provide several potential benefits such as flexibility and opportunities, to discover knowledge of the students. It also offers innovations in learning strategies of the students and resolve several complexities by accessing information from internet. Though e-learning based systems produces certain advantages, they also possess limitations of co-operative learning, active learning and performance mitigations. To address these issues, the present study focused on the different AI based techniques used in the prediction of student’s academic performance. The main objective of the study is to analyze the primary factors that affects the learning through online and analyze the performance using different intelligent approaches. A comparative study of the AI based techniques is performed to analyze the different methods involved in the assessment of academic performance. Further, the present issues and future works of the studies is deliberated to produce optimized analysis systems. This tends to support several researchers to overcome the disputes and provide effective e-learning assessment systems."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="6711"/> <meta name="DC.Identifier.DOI" content="10.4108/eetiot.6711"/> <meta name="DC.Identifier.URI" content="https://publications.eai.eu/index.php/IoT/article/view/6711"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2024 EAI Endorsed Transactions on Internet of Things"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/3.0/"/> <meta name="DC.Source" content="EAI Endorsed Transactions on Internet of Things"/> <meta name="DC.Source.ISSN" content="2414-1399"/> <meta name="DC.Source.Volume" content="11"/> <meta name="DC.Source.URI" content="https://publications.eai.eu/index.php/IoT"/> <meta name="DC.Subject" xml:lang="en" content="Distributed Learning Circumstances"/> <meta name="DC.Title" content="Digital Literacy: Comparative Review on Machine Learning Based Performance Assessment of Students"/> <meta name="DC.Type" content="Text.Serial.Journal"/> <meta name="DC.Type.articleType" content="Review article"/> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="EAI Endorsed Transactions on Internet of Things"/> <meta name="citation_journal_abbrev" content="EAI Endorsed Trans IoT"/> <meta name="citation_issn" content="2414-1399"/> <meta name="citation_author" content="K. 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Guizani, "IoT malicious traffic identification using wrapper-based feature selection mechanisms," Computers & Security, vol. 94, p. 101863, 2020."/> <meta name="og:site_name" content="EAI Endorsed Transactions on Internet of Things"/> <meta name="og:type" content="article"/> <meta name="og:title" content="Digital Literacy: Comparative Review on Machine Learning Based Performance Assessment of Students"/> <meta name="og:description" content=" The E-learning system paved an opportunity to make drastic changes in the educational system all over the world. Several institutions began to implement online learning to offer internet based courses contrary to the traditional classroom teaching. These online courses tends to provide several potential benefits such as flexibility and opportunities, to discover knowledge of the students. It also offers innovations in learning strategies of the students and resolve several complexities by accessing information from internet. Though e-learning based systems produces certain advantages, they also possess limitations of co-operative learning, active learning and performance mitigations. To address these issues, the present study focused on the different AI based techniques used in the prediction of student’s academic performance. The main objective of the study is to analyze the primary factors that affects the learning through online and analyze the performance using different intelligent approaches. A comparative study of the AI based techniques is performed to analyze the different methods involved in the assessment of academic performance. Further, the present issues and future works of the studies is deliberated to produce optimized analysis systems. This tends to support several researchers to overcome the disputes and provide effective e-learning assessment systems. 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Shwetha </span> <span class="affiliation"> B.S. Abdur Rahman Crescent Institute of Science & Technology <a href="https://ror.org/01fqhas03"><?xml version="1.0" encoding="UTF-8" standalone="no"?> <!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . 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Shahar Banu </span> <span class="affiliation"> B.S. Abdur Rahman Crescent Institute of Science & Technology <a href="https://ror.org/01fqhas03"><?xml version="1.0" encoding="UTF-8" standalone="no"?> <!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . 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Several institutions began to implement online learning to offer internet based courses contrary to the traditional classroom teaching. These online courses tends to provide several potential benefits such as flexibility and opportunities, to discover knowledge of the students. It also offers innovations in learning strategies of the students and resolve several complexities by accessing information from internet. Though e-learning based systems produces certain advantages, they also possess limitations of co-operative learning, active learning and performance mitigations. To address these issues, the present study focused on the different AI based techniques used in the prediction of student’s academic performance. The main objective of the study is to analyze the primary factors that affects the learning through online and analyze the performance using different intelligent approaches. A comparative study of the AI based techniques is performed to analyze the different methods involved in the assessment of academic performance. Further, the present issues and future works of the studies is deliberated to produce optimized analysis systems. This tends to support several researchers to overcome the disputes and provide effective e-learning assessment systems.</p> </section> <div class="item downloads_chart"> <h3 class="label"> Downloads </h3> <div class="value"> <canvas class="usageStatsGraph" data-object-type="Submission" data-object-id="6711"></canvas> <div class="usageStatsUnavailable" data-object-type="Submission" data-object-id="6711"> Download data is not yet available. </div> </div> </div> <br data-mce-bogus="1"> <!-- Plum Analytics --> <a href="https://plu.mx/plum/a/?doi=10.4108/eetiot.6711" class="plumx-summary" data-hide-when-empty="true" data-orientation="horizontal" ></a> <!-- /Plum Analytics --> <br data-mce-bogus="1"> <section class="item references"> <h2 class="label"> References </h2> <div class="value"> <p>[1] K. J. I. J. o. M. S. Sey and R. Technology, "Challenges of E-learning during COVID-19 Epidemic: A case study in one Cambodian high school," vol. 1, pp. 54-64, 2023. </p> <p>[2] S. Amin, S. 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