[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123253-en":3,"doc-seo-123253-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123253,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Research on Education Big Data for Students' Academic Performance Analysis based on Machine Learning","Education generates large-scale educational data from school academic affairs, financial, and library systems, creating opportunities and challenges for educational data mining. This work builds a machine learning approach using a Long Short-Term Memory Network (LSTM) to analyze educational big data and evaluate student performance from time-dependent learning behavior. LSTM is used to capture long-term trends in engagement and progress. Experiments compare multiple models and apply strict cross-validation to validate effectiveness, accuracy, and generalization, supporting personalized education and early intervention for underperforming students.","Research on Education Big Data for Student’s Academic Performance Analysis based on Machine Learning  \nChun wang  \nLaFetra College of Education, University of La Verne, CA, USA, [chun.wang@laverne.edu](chun.wang@laverne.edu)[ ](chun.wang@laverne.edu)Jiexiao Chen  \nSteinhardt School of Culture, Education, and Human Development, New York University, New York, [USAic11181@nyu.edu](USAic11181@nyu.edu)  \nZiyang Xie  \nDepartmen ofArt and Design, Hunan University of Humanities, Science and Technology, Hunan, [ChinaXieziyang197843@gmail.com](ChinaXieziyang197843@gmail.com)  \nJianke Zou*  \nHSBC Business School, Department of Management, Peking University, Peking, China, [Zoujianke@pku.org.cn](Zoujianke@pku.org.cn)  \nThe application of the Internet in the field of education is becoming more and more popular, and a large amount of educational data is generated in the process. How to effectively use these data has always been a key issue in the field of educational data mining. In this work, a machine learning model based on Long Short-Term Memory Network (LSTM) was used to conduct an indepth analysis of educational big data to evaluate student performance. The LSTM model efficiently processes time series data, allowing us to capture time-dependent and long-term trends in students' learning activities. This approach is particularly useful for analyzing student progress, engagement, and other behavioral patterns to support personalized education. In an experimental analysis, we verified the effectiveness of the deep learning method in predicting student performance by comparing the performance of different models. Strict cross-validation techniques are used to ensure the accuracy and generalization of experimental results.  \nCCS CONCEPTS • Applied computing ~ Education ~ Computer-assisted instruction • Computing methodologies ~Machine learning~ Machine learning approaches ~ Neural networks  \nAdditional Keywords and Phrases: Education Big Data, Performance Analysis, Machine Learning, Long Short-Term Memory Network.  \n1 INTRODUCTION  \nAccording to the definition of big data, big data refers to a data set that is too large to be analyzed and processed using conventional software. Educational big data refers to the data generated by students' daily learning and life activities from the school's academic affairs system, financial system, library system and other sources. The research on education big data mainly includes employment analysis, student specialty analysis, student development trajectory, online behavior analysis, and student portrait [1]. By analyzing this data, it is possible to make predictions about student behavior and potential risks, laying the foundation for providing personalized educational services.  \nMany higher education students face serious academic challenges, including weak self-control and a lack of motivation for active learning, which often require external supervision and intervention. In addition, some students are at risk of dropping out and having difficulty obtaining a degree due to the large number of failed subjects, and some students are under scrutiny for violations such as late arrivals, early departures, and absenteeism. Internet addiction, in particular, makes it difficult for some students to meet academic requirements [2]. These problems not only lead to increased psychological stress for some students, but also lead to self-harm behavior. In order to reduce these phenomena, schools and relevant authorities need to take measures to strengthen the supervision of students' learning, intervention in inappropriate behavior, and counseling for students with psychological problems.  \nIn the face of these challenges, human resources often appear to be insufficient. Therefore, it is crucial to use educational data mining to analyze and predict students' academic performance. With the increase of campus service management platforms, the accumulation of student data has shown massive growth, including consump","cbCaioWNRqJfc7sp","https://ap.wps.com/l/cbCaioWNRqJfc7sp","pdf",1250814,1,"English","en",105,"# Introduction\n## Educational big data and its application in performance prediction\n## Academic challenges and the need for intervention\n## Using educational data mining and deep learning for early prediction\n## Behavior patterns, online habits, and mental health correlations\n## Extracting higher-order features for policy and decision support","[{\"question\":\"What data sources are considered educational big data in this work?\",\"answer\":\"The work describes educational big data as data generated by students’ daily learning and life activities, collected from school academic affairs, financial, library, and other related systems.\"},{\"question\":\"Why is LSTM suitable for analyzing students’ academic performance?\",\"answer\":\"LSTM efficiently processes time series data, helping capture time-dependent and long-term trends in students’ learning activities, engagement, and behavioral patterns.\"},{\"question\":\"How is the model’s effectiveness validated?\",\"answer\":\"Effectiveness is validated through experimental comparisons with different models and strict cross-validation to ensure accuracy and generalization of the results.\"}]","Research on Education Big Data for Students' Academic Performance Analysis based on Machine Learning | PDF",1785815490,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"research-on-education-big-data-for-students-academic-performance-analysis-based-on-machine-learning","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/research-on-education-big-data-for-students-academic-performance-analysis-based-on-machine-learning/123253/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What data sources are considered educational big data in this work?","Question",{"text":74,"@type":75},"The work describes educational big data as data generated by students’ daily learning and life activities, collected from school academic affairs, financial, library, and other related systems.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Why is LSTM suitable for analyzing students’ academic performance?",{"text":79,"@type":75},"LSTM efficiently processes time series data, helping capture time-dependent and long-term trends in students’ learning activities, engagement, and behavioral patterns.",{"name":81,"@type":72,"acceptedAnswer":82},"How is the model’s effectiveness validated?",{"text":83,"@type":75},"Effectiveness is validated through experimental comparisons with different models and strict cross-validation to ensure accuracy and generalization of the results.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]