[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127379-en":3,"doc-seo-127379-105":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},127379,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning-Based Research on the Adaptability of Adolescents to Online Education","Rapid internet development has made adolescents’ adaptability to online learning a major educational focus, yet existing predictive modeling work needs refinement. Using data from the “Chinese Adolescent Online Education Survey” (2014–2016), this study applies five machine learning algorithms—logistic regression, KNN, random forest, XGBoost, and CatBoost—to identify key predictors of adaptability and select the most suitable model. Results show course duration, family financial status, and age as primary factors, with age markedly affecting adaptability.","Machine Learning-Based Research on the Adaptability of Adolescents to Online Education  \nMingwei,Wang1, Sitong Liu2  \n1College of Humanities&Social Sciences,HZAU,Wuhan,China  \n2GSE, University of Pennsylvania, Philadelphia, USA  \n[1](114512340123@qq.com)[14512340123@qq.com](114512340123@qq.com)  \nAbstract.With the rapid advancement of internet technology, the adaptability of adolescents to online learning has emerged as a focal point of interest within the educational sphere. However, the academic community's efforts to develop predictive models for adolescent online learning adaptability require further refinement and expansion. Utilizing data from the \"Chinese Adolescent Online Education Survey\" spanning the years 2014 to 2016, this study implements five machine learning algorithms—logistic regression, K-nearest neighbors, random forest, XGBoost, and CatBoost—to analyze the factors influencing adolescent online learning adaptability and to determine the model best suited for prediction. The research reveals that the duration of courses, the financial status of the family, and age are the primary factors affecting students' adaptability in online learning environments.Additionally, age significantly impacts students' adaptive capacities. Among the predictive models, the random forest, XGBoost, and CatBoost algorithms demonstrate superior forecasting capabilities, with the random forest model being particularly adept at capturing the characteristics of students' adaptability.  \nKeywords: Online Learning Adaptability,Machine Learning,Adolescent  \n1. Introduction  \nIn the digital era, the evolution of information technology has catalyzed significant innovation in educational paradigms, with online learning emerging as a pivotal segment within the field of education[1]. The 50th Statistical Report on Internet Development in China, published in 2022, indicates a substantial growth in the user base of online education in China, expanding from 110 million in 2015 to 377 million by 2022. Despite the evident advantages of online education, the industry confronts numerous challenges[2] .  \nAdaptability to online learning is defined as the learner's capacity to actively modify their approach in response to changes in personal and environmental conditions during the process of knowledge acquisition via the internet, thereby aligning their development with the learning context and achieving educational objectives[3] . It is one of the critical factors influencing the development of online education. Although current academic research extensively covers various aspects of adolescent online learning adaptability, including individual, familial, scholastic, and community dimensions, there is a notable absence of in-depth analysis on how these factors interrelate and collectively affect adolescents' acceptance of online education. Moreover, prevailing research often relies on descriptive statistics and correlation analysis to interpret the connections between these factors and online learning  \nadaptability, with a scarcity of studies constructing predictive models based on these elements. The limitations of this methodological approach have hindered our ability to deeply comprehend and anticipate the trends in adolescent adaptability to online education. Consequently, leveraging certain factors to predict the level of adaptability of adolescents to online education is of profound significance.  \nAccordingly, this study aims to explore and assess the factors influencing the adaptability of adolescent students to online learning and to construct corresponding predictive models, thereby providing a scientific basis and data support for educational practice and policy formulation.  \nThis research refines and organizes the broad concept of online learning adaptability and the dimensions that may affect adolescent online learning adaptability. By establishing a dataset that encompasses 13 features and one target column, an in-depth analysis of the","cbCaieAV1tbNIaDq","https://ap.wps.com/l/cbCaieAV1tbNIaDq","pdf",416585,1,"English","en",105,"# Introduction\n# Related work","[{\"question\":\"Which dataset and time period are used to study adolescents’ adaptability to online education?\",\"answer\":\"The study uses data from the “Chinese Adolescent Online Education Survey” covering 2014 to 2016.\"},{\"question\":\"What are the main factors affecting students’ adaptability to online learning?\",\"answer\":\"The research highlights course duration, family financial status, and age as the primary influencing factors.\"},{\"question\":\"Which machine learning models perform best for prediction?\",\"answer\":\"Random forest, XGBoost, and CatBoost show superior forecasting performance, with random forest particularly effective at capturing adaptability characteristics.\"}]","Machine Learning-Based Research on the Adaptability of Adolescents to Online Education | PDF",1785938588,20,{"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},"machine-learning-based-research-on-the-adaptability-of-adolescents-to-online-education","",{"@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/machine-learning-based-research-on-the-adaptability-of-adolescents-to-online-education/127379/",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","2026-08-05",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},"Which dataset and time period are used to study adolescents’ adaptability to online education?","Question",{"text":74,"@type":75},"The study uses data from the “Chinese Adolescent Online Education Survey” covering 2014 to 2016.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What are the main factors affecting students’ adaptability to online learning?",{"text":79,"@type":75},"The research highlights course duration, family financial status, and age as the primary influencing factors.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning models perform best for prediction?",{"text":83,"@type":75},"Random forest, XGBoost, and CatBoost show superior forecasting performance, with random forest particularly effective at capturing adaptability characteristics.","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"]