[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127034-en":3,"doc-seo-127034-105":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},127034,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Information System Framework for Learners’ Dropout Prediction in Online Courses Using Machine Learning Algorithms - Research Paper","COVID-19 brought a temporary global slowdown, while IT support accelerated education, enabling online teaching and learning. Online courses expanded through MOOCs, yet learner retention and engagement remain challenging due to low learning pressure and permissive learning environments that increase dropouts. This study proposes an information system framework that predicts learners’ dropout risk using learning footprints and evaluation data. Comparative experiments across diverse datasets and features apply widely used machine learning algorithms, where ensemble techniques achieve superior performance.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ACIS 2023 Proceedings | Australasian (ACIS) |\n| --- | --- |\n| 12-2-2023\u003Cbr>Information System Framework for Learners’Dropout Prediction in Online Courses Using Machine Learning Algorithms\u003Cbr>Neelam Naik\u003Cbr>Assistant Professor, SVKM's Usha Pravin Gandhi College of Arts, Science and Commerce, India, [neelam.naik@upgcm.ac.in](neelam.naik@upgcm.ac.in)\u003Cbr>Harshali Patil\u003Cbr>Associate Professor, MET Institute of Computer Science, Mumbai, India, [harshalip_ics@met.edu](harshalip_ics@met.edu)\u003Cbr>Seema Purohit\u003Cbr>Professor Emeritus, B. K. Birla College of Arts, Science & Commerce (Autonomous), India, [supurohit@gmail.com](supurohit@gmail.com)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/acis2023](https://aisel.aisnet.org/acis2023) |  |\n\nRecommended Citation  \nNaik, Neelam; Patil, Harshali; and Purohit, Seema, \"Information System Framework for Learners’ Dropout Prediction in Online Courses Using Machine Learning Algorithms\" (2023) . ACIS 2023 Proceedings. 76.  \n[https://aisel.aisnet.org/acis2023/76](https://aisel.aisnet.org/acis2023/76)  \nThis material is brought to you by the Australasian (ACIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ACIS 2023 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).  \nInformation System Framework for Learners’ Dropout Prediction in Online Courses Using Machine Learning Algorithms  \nFull Research Paper  \nNeelam Naik  \nUsha Pravin Gandhi College of Arts, Science and Commerce  \nVile Parle West, Mumbai, Maharashtra, India [Email: neelam.naik@upgcm.ac.in](Email: neelam.naik@upgcm.ac.in)  \nHarshali Patil  \nMET Institute of Computer Science Bandra West, Mumbai, Maharashtra, India [Email: harshalip_ics@met.edu](Email: harshalip_ics@met.edu)  \nSeema Purohit  \nB. K. Birla College of Arts, Science and Commerce (Autonomous) Kalyan  \nMaharashtra, India  \nEmail: [supurohit@gmail.com](supurohit@gmail.com)  \nAbstract  \nThe COVID-19 pandemic resulted in a slight pause in day-to-day activities globally. To streamline the activities during the pandemic, the contribution of the Information Technology (IT) sector was pervasive. IT played a vital role in the education domain, which led to welcoming the online teachinglearning process. As a part of curricula, Massive Open Online Courses (MOOC) have been introduced by many universities during and even after the pandemic. Online learning popularity has increased many folds globally, but it faces the problem of retention and engagement of learners. The major problem in this system was related to the dropouts due to liberal learning environments and lack of learning pressure. The present study focuses on an information system framework for learners’ dropout prediction using footprints and evaluation data. Comparative analysis of different datasets with learners’diverse features is used for dropout prediction using well-known Machine Learning(ML) algorithms, from these ensemble techniques outperform.  \nKeywords: Information System, Dropout prediction, Machine Learning Algorithms, Hyperparameter tuning, Dataset Characteristics  \n1 Introduction  \nIn the financial year 2020, in higher education, 40 million students enrolled across India. The growth in enrolment by 2035 will become 92 million. India witnessed a gross enrolment ratio in higher education of 27.1% in 2021, as referred by Rathore (2023) and Kanwal (2022) . The worldwide online education market is expected to reach 77 billion dollars by 2028 and grow at a compounded 30.5% . Digital education is a blending of online education and traditional education systems. However traditional education activities are supported by digital equipment and computer-mediated actions for content creation and its delivery. In the near future, Augmented Reality (AR) is going to ","cbCaiogqB38a4L1z","https://ap.wps.com/l/cbCaiogqB38a4L1z","pdf",1134192,1,12,"English","en",105,"# Introduction\n## Online learning, MOOC adoption, and dropout challenge\n## Information systems background and motivation\n# Proposed framework and approach\n## Footprints and evaluation data for dropout prediction\n## Comparative analysis across datasets and learner features\n# Machine learning methodology\n## Model training and hyperparameter tuning\n## Ensemble techniques and performance comparison\n# Conclusion","[{\"question\":\"What problem does the study address in online learning environments?\",\"answer\":\"The study targets learner retention and engagement issues, focusing on dropouts caused by liberal learning environments and insufficient learning pressure.\"},{\"question\":\"How does the proposed approach predict learners’ dropout risk?\",\"answer\":\"It uses an information system framework that analyzes learning footprints and evaluation data to identify learners’ dropout behavior.\"},{\"question\":\"Which machine learning strategy is reported to perform best?\",\"answer\":\"The study reports that ensemble techniques outperform other approaches in dropout prediction across the tested datasets and learner feature sets.\"}]","Information System Framework for Learners’ Dropout Prediction in Online Courses Using Machine Learning Algorithms - 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