[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124875-en":3,"doc-seo-124875-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124875,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Roles of Machine Learning and Deep Learning for Supporting E-Learning Processes and Learning Outcomes through The Lens Of System's View of E-Learning Success Model","Machine learning and deep learning are core areas in artificial intelligence, yet their specific roles in supporting e-learning processes and learning outcomes receive limited attention. This study examines how ML and DL contribute to managing each of the six components of distance learning systems using the system’s view of the e-learning success model. It highlights intelligent learning environments enabling personalized adaptive development and discusses dropout mitigation by analyzing LMS data and learning patterns to identify likely obstacles early.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| AMCIS 2024 Proceedings | Americas Conference on Information Systems\u003Cbr>(AMCIS) |\n| --- | --- |\n| August 2024\u003Cbr>Roles of Machine Learning and Deep Learning for Supporting E-Learning Processes and Learning Outcomes through The Lens Of System's View of E-Learning Success Model\u003Cbr>Sean Eom\u003Cbr>southeast missouri state university, [sbeom@semo.edu](sbeom@semo.edu)\u003Cbr>Mohamed Amroune\u003Cbr>Larbi Tebessi University, [medamroune@gmail.com](medamroune@gmail.com)\u003Cbr>Abderrazak Khediri\u003Cbr>Larbi Tebessi University, [khediri.abderrazak@gmail.com](khediri.abderrazak@gmail.com)\u003Cbr>Mohammed Ridda Laour\u003Cbr>Larbi Tebessi University, [rlaouar.educ@gmail.com](rlaouar.educ@gmail.com)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/amcis2024](https://aisel.aisnet.org/amcis2024) |  |\n\nRecommended Citation  \nEom, Sean; Amroune, Mohamed; Khediri, Abderrazak; and Laour, Mohammed Ridda, \"Roles of Machine Learning and Deep Learning for Supporting E-Learning Processes and Learning Outcomes through The Lens Of System's View of E-Learning Success Model\" (2024) . AMCIS 2024 Proceedings. 15.  \n[https://aisel.aisnet.org/amcis2024/is_education/is_education/15](https://aisel.aisnet.org/amcis2024/is_education/is_education/15)  \nThis material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in AMCIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org).  \nRoles of Machine Learning and Deep Learning for Supporting E-Learning Processes and Learning Outcomes through The Lens Of System's View of E-Learning  \nSuccess Model  \nCompleted Research Full Paper  \nSean Eom  \nSoutheast Missouri State University [Sbeom@semo.edu](Sbeom@semo.edu)  \nAbderrazak Khediri  \nSaad Dahleb University  \n[khediri.abderrazak](khediri.abderrazak@gmail.com)[@gmail.com](khediri.abderrazak@gmail.com)  \nMohamed Amroune  \nLarbi Tebessi University [medamroune@gmail.com](medamroune@gmail.com)  \nMohammed Ridda Laour  \nA Larbi Tebessi University [rlaouar.educ@gmail.com](rlaouar.educ@gmail.com)  \nAbstract  \nMachine learning and deep learning are two core research domains in artificial intelligence. In recent years, the roles of artificial intelligence in education—the fundamental concepts of artificial intelligence, machine learning, and deep learning- have been extensively investigated. However, relatively scant attention has been paid to the roles of artificial intelligence(AI), especially machine learning (ML) and deep learning (DL), in supporting the e-learning process and outcomes. The primary objective of this study is to investigate the roles of two artificial intelligence subfields, ML and DL, in managing each of the six components of distance learning systems through the lens of the system's view of the e-learning success model.  \nAI and computer and information technologies have enabled an intelligent learning environment facilitating personalized adaptive learning development. Further, managing dropouts in online education has been an important issue. DL can predict potential obstacles and areas where students will likely struggle by analyzing historical learning management systems (LMS) data and learning patterns. With this insight, educators and platforms can provide targeted support and resources to help students overcome challenges before they lead to dropout.  \nKeywords  \nArtificial intelligence, machine learning, deep learning, distance learning systems, system’s view of the elearning success model.  \nIntroduction  \nMachine learning (ML) and deep learning (DL) are two core research domains in artificial intelligence. In recent years, the roles of AI in education (Chen et al., 2020; Chen et al., 2022; Holmes et al., 2019; Ouyang et al., 2022; Zhai et al., 202","cbCaichYTlZeIAA4","https://ap.wps.com/l/cbCaichYTlZeIAA4","pdf",410383,1,11,"English","en",105,"# Abstract\n# Introduction\n## Research Motivation and Problem\n## Objectives and Conceptual Framework\n## Lens of the E-Learning Success Model\n## Structure of the Conceptual Paper","[{\"question\":\"What is the primary objective of this study?\",\"answer\":\"To investigate how machine learning and deep learning support the e-learning process and outcomes by managing each component of distance learning systems through the system’s view of the e-learning success model.\"},{\"question\":\"How can deep learning help with dropout in online education?\",\"answer\":\"By analyzing historical learning management system data and learning patterns, deep learning can predict potential obstacles and areas where students are likely to struggle, enabling targeted support before dropout occurs.\"},{\"question\":\"What six components of distance learning systems are addressed?\",\"answer\":\"The model focuses on students, the instructor, learning management systems, self-regulated learning and interaction processes, and learning outcomes.\"}]","Roles of Machine Learning and Deep Learning for Supporting E-Learning Processes and Learning Outcomes through The Lens Of System's View of E-Learning Success Model | 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