[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118375-en":3,"doc-seo-118375-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118375,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Leveraging Machine Learning Techniques for Student’s Attention Detection - A Review","Online learning relies on sustained student engagement, yet attention can easily degrade due to multitasking and external distractions across varied home or remote environments. This review examines how machine learning methods support attention detection for effective teaching by analyzing data such as eye gaze, facial expressions, body movement, and EEG. It summarizes online learning-specific challenges, reviews limitations of existing approaches, and proposes recommendations to advance robust, scalable attention monitoring systems for improved learning outcomes.","Leveraging machine learning techniques for student’s attention  \ndetection: a review  \nEng Lye Lim1,3, Raja Kumar Murugesan2,4, Sumathi Balakrishnan2,3  \n1School of Diploma and Professional Studies, Taylor’s College, Subamg Jaya, Selangor, Malaysia 2School of Computer Science, Taylor’s University, Subang Jaya, Selangor Malaysia 3Digital Health and Innovations Impact Lab, Taylor’s University, Subang Jaya, Selangor, Malaysia 4Digital Economy and Business Transformation Impact Lab, Taylor’s University, Subang Jaya, Malaysia  \n\n| Article history:\u003Cbr>Received Jun 26, 2023 Revised Oct 17, 2023 Accepted Nov 6, 2023 | With the advances of the internet and today's innovation, it has become conceivable to conduct teaching and learning activities remotely through the online platform. Existing research says that student’s attention state and learning result are strongly correlated. However, despite its importance, this can be a challenging task, as students in general taking an online class maybe in a variety of different environments and may be multitasking or distracted by other factors. This review paper aims to address these challenges by exploring the opportunities offered by machine learning techniques in attention detection for effective online teaching and learning. By leveraging machine learning algorithms, which can analyze large volumes of data, including eye-tracking, facial expressions, and body movements, we can develop robust models for attention detection in online learning environments. This paper reviews the challenges specific to online learning, such as students' attention deficits and learning styles, and highlights the limitations of current attention detection methods. Furthermore, it provides recommendations to advance attention detection technology, emphasizing the potential of machine learning to enhance attention detection technology for effective online teaching and learning.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Attention detection Computer vision Electroencephalogram EEG Eye gaze tracking Machine learning\u003Cbr>Online learning Student attention |  |\n\nCorresponding Author:  \nRaja Kumar Murugesan  \nSchool of Computer Science, Taylor’s University Taylor's Road, Subang Jaya, Selangor, 47500, Malaysia Email: [rajakumar.murugesan@taylors.edu.my](rajakumar.murugesan@taylors.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn recent years, online learning has gained significant popularity, offering flexible and accessible educational opportunities to a diverse range of students. Experiencing from COVID-19 pandemic, which is a past today, many things have changed cross the industry including education in terms of operations and services. Though students have returned to their respective education institutions, online teaching and learning is still practiced at least to some extent. However, one of the significant disadvantages is the student’s attention that can be distracted by the external stimuli such as text messages and noises from the surrounding environment when attending online classes [1] . Research studies have indicated a positive association between attention level and academic performance, and poor attention may result in the students having difficulty following instructions, slow learning, and completing the tasks on time [2] . Hence, introducing a student’s attention monitoring system during online learning process is crucial for student’s learning success.  \nOne of the challenges faced by educators in online learning environments is the ability to monitor and assess students' attention levels [3] . Understanding students' attention patterns is crucial for effective instruction, personalized feedback, and identifying potential learning difficulties. In a traditional classroom, teachers monitor students' body language or facial expressions to gauge their attentiveness, which can lead to incorrect conclusions [4] . Therefore, there is a growing in","cbCaiqx1CdanvrdS","https://ap.wps.com/l/cbCaiqx1CdanvrdS","pdf",380553,1,11,"English","en",105,"# Introduction\n## Motivation and problem of monitoring student attention\n## Role of attention in academic performance\n## Objectives and paper organization\n# Student’s attention detection and prediction approaches","[{\"question\":\"Why is student attention monitoring important in online learning?\",\"answer\":\"Student attention is often disrupted by external stimuli and multitasking. Monitoring helps support learning success by enabling timely insight into attention patterns and potential learning difficulties.\"},{\"question\":\"What data sources are commonly used for attention detection?\",\"answer\":\"Attention detection leverages eye-gaze data, facial expressions, body movements, and electroencephalography (EEG) signals. Combining multiple sources can improve the robustness of detection models.\"},{\"question\":\"What challenges and limitations does the review highlight?\",\"answer\":\"The review discusses challenges specific to online environments and notes limitations of current attention detection methods. It also outlines areas needing improvement to achieve reliable and scalable performance.\"}]","Leveraging Machine Learning Techniques for Student’s Attention Detection - A Review | PDF",1785683331,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"leveraging-machine-learning-techniques-for-students-attention-detection-a-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/leveraging-machine-learning-techniques-for-students-attention-detection-a-review/118375/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is student attention monitoring important in online learning?","Question",{"text":76,"@type":77},"Student attention is often disrupted by external stimuli and multitasking. Monitoring helps support learning success by enabling timely insight into attention patterns and potential learning difficulties.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources are commonly used for attention detection?",{"text":81,"@type":77},"Attention detection leverages eye-gaze data, facial expressions, body movements, and electroencephalography (EEG) signals. Combining multiple sources can improve the robustness of detection models.",{"name":83,"@type":74,"acceptedAnswer":84},"What challenges and limitations does the review highlight?",{"text":85,"@type":77},"The review discusses challenges specific to online environments and notes limitations of current attention detection methods. 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