[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291073-105":59,"doc-detail-291073-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","predicting-student-academic-outcomes-from-e-learning-interaction-data-using-hybrid-machine-learning-models","Predicting student academic outcomes from e-learning interaction data using hybrid machine learning models","","Rapid growth of digital learning platforms generates large volumes of student interaction data, enabling intelligent prediction of academic outcomes. This study presents a hybrid machine learning framework using an e-learning student reactions dataset that represents engagement patterns, behavioral responses, and interaction dynamics. Eight classifiers are assessed with an 80–20 train-test split and K-fold cross-validation, showing the RBF model achieves the highest accuracy (1.00) and strong generalization. The framework is mapped to FPGA/embedded systems and IoT-enabled smart classrooms for low-latency edge analytics.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-student-academic-outcomes-from-e-learning-interaction-data-using-hybrid-machine-learning-models/291073/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-student-academic-outcomes-from-e-learning-interaction-data-using-hybrid-machine-learning-models/291073.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What data source and concept does the study use for predicting student performance?","Question",{"text":113,"@type":114},"The study uses an e-learning student reactions dataset capturing engagement patterns, behavioral responses, and interaction dynamics derived from student interactions with online learning materials.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which evaluation strategies are used to test the classifiers?",{"text":118,"@type":114},"Models are evaluated using both an 80–20 train-test split and K-fold cross-validation to assess accuracy and generalization.",{"name":120,"@type":111,"acceptedAnswer":121},"Which hybrid model achieved the best results and what accuracy was reported?",{"text":122,"@type":114},"The RBF model achieved the highest accuracy of 1.00, indicating strong capability to model complex nonlinear behavior.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291073,1789643378,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Predicting student academic outcomes from e-learning interaction data using hybrid machine learning models  \nSajithunisa Hussain, Jayachandran Jeyachidra  \nDepartment of Computer Applications, Periyar Maniammai Institute of Science & Technology, Thanjavur, India  \nArticle history:  \nReceived Aug 21, 2024 Revised Mar 3, 2026 Accepted May 30, 2026  \nKeywords:  \nMachine learning Model  \nMulti-layer perceptron Pipeline  \nRandom forest Stacked classifier Support vector machine  \nCorresponding Author:  \nThe rapid growth of digital learning platforms has generated large volumes of student interaction data, providing opportunities for intelligent prediction of academic outcomes. Beyond educational analytics, such prediction tasks are relevant for reconfigurable systems, embedded platforms, very large scale integration (VLSI) accelerators, and internet of things (IoT)-enabled edge devices in smart learning environments. This study proposes a hybrid machine learning framework for predicting student performance using the e-learning student reactions dataset, which captures engagement patterns, behavioral responses, and interaction dynamics. Eight classifiers— eXtreme gradient boosting (XGBoost), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), radial basis function (RBF), and deep neural network (DNN)—are evaluated using both an 80–20 train–test split and K-fold cross-validation to assess accuracy and generalization. Results show the RBF model achieves the highest accuracy of 1.00, demonstrating its ability to capture complex, nonlinear behavior. From a systems perspective, the framework can be mapped onto field programmable gate arrays (FPGAs) or embedded devices, leveraging parallel computation for low-latency inference, and integrated with IoT-enabled smart classrooms for real-time edge analytics. These findings confirm that hybrid machine learning models not only improve student performance prediction but also serve as practical workloads for reconfigurable, embedded, and VLSI-based intelligent systems in digital education.  \nThis is an open access article under the CC BY-SA license.  \nJayachandran Jeyachidra  \nDepartment of Computer Applications, Periyar Maniammai Institute of Science & Technology Vallam-613403, Thanjavur, Tamil Nadu, India  \nEmail: [chithu_raj@pmu.edu](chithu_raj@pmu.edu)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe rapid digital transformation of education has fundamentally changed how students interact with learning content, instructors, and peers, leading to the widespread adoption of e-learning platforms across schools, universities, and professional training environments. Unlike traditional classroom settings, digital learning systems continuously record detailed traces of learner behavior, including time spent on instructional materials, responses to quizzes, participation in discussions, frequency of logins, and patterns of engagement with multimedia resources [1]–[3] . These large volumes of interaction data have created new opportunities for understanding how students learn, how they struggle, and what factors contribute to their academic success or failure. At the same time, the increasing diversity of learners in online environments, coupled with variations in motivation, prior knowledge, and learning styles, has made it more difficult for instructors to monitor individual progress and provide timely interventions [4]–[6]. As a result, there is a growing need for intelligent,  \ndata-driven approaches that can automatically analyze learner behavior and accurately predict student outcomes before final assessments are completed. In this context, educational data mining and learning analytics have emerged as important research areas that seek to transform raw e-learning data into actionable insights that can support personalized instruction, adaptive content delivery, and early warning systems for atrisk students. Predicting stud","cbCaie41jb8BB0xs","https://ap.wps.com/l/cbCaie41jb8BB0xs","pdf",459421,"English","# 1. INTRODUCTION\n## Digital transformation and learning traces\n## Need for data-driven prediction\n## Educational data mining and learning analytics\n## Limitations of traditional statistical methods\n## Role and challenges of machine learning models\n## Dataset characteristics and evaluation requirements","[{\"question\":\"What data source and concept does the study use for predicting student performance?\",\"answer\":\"The study uses an e-learning student reactions dataset capturing engagement patterns, behavioral responses, and interaction dynamics derived from student interactions with online learning materials.\"},{\"question\":\"Which evaluation strategies are used to test the classifiers?\",\"answer\":\"Models are evaluated using both an 80–20 train-test split and K-fold cross-validation to assess accuracy and generalization.\"},{\"question\":\"Which hybrid model achieved the best results and what accuracy was reported?\",\"answer\":\"The RBF model achieved the highest accuracy of 1.00, indicating strong capability to model complex nonlinear behavior.\"}]","Predicting student academic outcomes from e-learning interaction data using hybrid machine learning models | PDF",25]