[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126952-en":3,"doc-seo-126952-105":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},126952,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","An analysis of diverse computational models for predicting student achievement on e-learning platforms","COVID-19 drove widespread adoption of online learning, creating large educational databases where evaluating student development becomes challenging. This review examines how to predict online course performance using data-driven machine learning approaches, focusing on effective feature selection, strategies to handle imbalanced data, and algorithms that forecast achievement. It also emphasizes hybrid techniques and optimization methods that improve prediction accuracy. Hybrid feature selection (e.g., L2 regularization and RFE) and ensemble models such as random forest, gradient boosting, and AdaBoost are highlighted for coping with complex educational datasets and improving outcomes.","An analysis of diverse computational models for predicting student achievement on e-learning platforms using machine  \nlearning  \nNaga Satya Koti Mani Kumar Tirumanadham1, Thaiyalnayaki Sekhar1, Sriram Muthal2  \n1Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Selaiyur, India 2Department of Information Technology, Bharath Institute of Higher Education and Research, Selaiyur, India  \n\n| Article history:\u003Cbr>Received Mar 30, 2024 Revised Jul 16, 2024 Accepted Aug 6, 2024 | Coronavirus disease 2019 (COVID-19) has led many colleges and students to use online learning. In educational databases with so much data, evaluating student development is difficult. E-learning is essential for egalitarian education since it uses technology and contemporary learning techniques. This review research found three ways for predicting online course performance: i) To choose the best features to raise student performance; ii) The most effective algorithms for transforming unbalanced data into balanced data; and iii) The best machine learning algorithms to predict online course performance. This study also offered insights into using hybrid techniques and optimization algorithms to educational data sets to improve student performance prediction. The utilization of data from independent e-learning products to enhance education today requires data processing to ensure quality. In addition to these techniques, our abstract highlights the effectiveness of hybrid feature selection methods like L2 regularization (Ridge) and recursive feature elimination (RFE) and ensemble learning models like random forest, gradient boosting, and AdaBoost. These approaches considerably improve prediction accuracy and tackle huge and sophisticated educational dataset challenges. Our work uses advanced machine-learning approaches to optimize e-learning settings and boost academic achievements in the shifting online education landscape caused by the COVID-19 pandemic.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>AdaBoost\u003Cbr>E-learning\u003Cbr>Ensemble learning models Gradient boosting L2 regularization (Ridge) Random forest\u003Cbr>Recursive feature elimination |  |\n\nCorresponding Author:  \nNaga Satya Koti Mani Kumar Tirumanadham  \nDepartment of Computer Science and Engineering, Bharath Institute of Higher Education and Research Selaiyur, Tamil Nadu, India  \n[Email: manikumar1248@gmail.com](Email: manikumar1248@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThere has been a total game-changer with more integration of e-learning platforms into the education landscape. The transition from traditional classrooms [1]–[3] to online learning environments has brought forth the basic change in the way learners are interacting with learning resources. E-learning [4], with a variety of instructional media, developed due to the use of technologies in education, has become a flexible and easily adjustable learning environment, beyond geographical boundaries, interesting to many kinds of learners. As the world educational community continues venturing into the potential realm of e-learning, the emphasis remains on the academic achievements of the students within the digital boundaries. In view of this, therefore, understanding and improving student performance in e-learning environments [5]–[8] is essential in ensuring the effectiveness and success of online educational activities. The aspect of student performance in the e-learning environment is multidimensional and involves the engagement of the  \nstudent, academic performance, and overall satisfaction of the student with the learning process. The present study looked into the complexity involved in student performance in an e-learning environment. The increasing reliance on digital platforms for education needs an in-depth analysis through the elements that affect student results and the efficiency of the present e-learning methods. T","cbCaioWkhOWXwGMT","https://ap.wps.com/l/cbCaioWkhOWXwGMT","pdf",434899,1,9,"English","en",105,"# Introduction\n## Student performance challenges in e-learning\n## Need for predictive approaches\n## Approaches for predicting online course success","[{\"question\":\"Why is predicting student achievement in e-learning considered difficult?\",\"answer\":\"Large educational databases make assessing student development difficult, especially when traditional assessment methods do not fit digital learning environments and data complexity increases.\"},{\"question\":\"What three main approaches are used to predict online course performance?\",\"answer\":\"The study summarizes: selecting the best features, using the most effective methods to transform imbalanced data into balanced data, and applying the best machine learning algorithms to predict performance.\"},{\"question\":\"Which techniques and models are highlighted for improving prediction accuracy?\",\"answer\":\"It highlights hybrid feature selection methods such as L2 regularization (Ridge) and recursive feature elimination (RFE), along with ensemble learning models including random forest, gradient boosting, and AdaBoost.\"}]","An analysis of diverse computational models for predicting student achievement on e-learning platforms | 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