[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121995-en":3,"doc-seo-121995-105":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},121995,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Improvement of Student Interaction Analysis in Online Education Platforms through Interactive Mobile Technology and Machine Learning Integration","Online education platforms shaped by interactive mobile technology demand advanced, reliable analysis to improve how students interact and to optimize learning experiences in digital environments. Effective student interaction analytics must handle large-scale platform data while remaining accurate for anomaly detection and efficient for data processing. This study proposes a new approach that combines machine learning with real-time mobile data processing to detect irregular interactions, and introduces a congestion control mechanism that improves transmission stability and efficiency for mobile-based ecosystems. The result supports more engaging teaching and learning.","JIM International Journal of  \nInteractive Mobile Technologies  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJIM | eISSN: 1865-7923 | Vol. 18 No. 9 (2024) |   \n[https://doi.org/10.3991/ijim.v18i09.49291](https://doi.org/10.3991/ijim.v18i09.49291)  \nPAPER  \nImprovement of Student Interaction Analysis in Online Education Platforms through Interactive Mobile Technology and Machine Learning Integration  \nJinjin Wang(􀀍)  \nSchool of Preschool Education, Shaanxi Vocational & Technical College,  \nXi’an, China  \n[jinjinking2013@163.com](jinjinking2013@163.com)  \nABSTRACT  \nThe emergence of online education platforms, driven by interactive mobile technology, has significantly reshaped traditional educational paradigms and underscored the critical need for advanced analysis and improvement of student interactions. Effective analysis of student interaction is crucial for enhancing teaching quality and optimizing the learning experience in these digitally enriched environments. Traditional analysis frameworks often face challenges such as inaccuracies in anomaly detection and inefficiencies in data handling, particularly when handling extensive datasets typical of online platforms. This study introduces a novel approach to enhancing student interaction analysis systems by leveraging the synergy between machine learning and advanced interactive mobile technologies. Initially, the study proposes an advanced anomaly detection method tailored for identifying irregular student interactions. This method utilizes a blend of machine learning algorithms and the real-time data processing capabilities of mobile technology. Furthermore, to address the complexities of data transmission in mobile-based online education ecosystems, a state-ofthe-art congestion control algorithm has been developed. This algorithm optimizes data flow, significantly enhancing transmission stability and efficiency. The integration of interactive mobile technology with machine learning offers a robust and dynamic framework for analyzing student interactions, thereby facilitating a more engaging and effective online educational experience. This research contributes to the advancement of online education quality and efficiency by emphasizing the role of interactive mobile technology in shaping future learning environments.  \nKEYWORDS  \ninteractive mobile technology, online education platforms, student interaction analysis, machine learning integration, anomaly detection, congestion control, mobile data transmission optimization  \nWang, J. (2024) . Improvement of Student Interaction Analysis in Online Education Platforms through Interactive Mobile Technology and Machine Learning Integration. International Journal of Interactive Mobile Technologies (iJIM), 18(9), pp. 35–49. [https://doi.org/10.3991/ijim.v18i09.49291](https://doi.org/10.3991/ijim.v18i09.49291)[ ](https://doi.org/10.3991/ijim.v18i09.49291)[Article submitted 2024-02-13. Revision uploaded 2024-03-18. Final acceptance 2024-03-23.](Article submitted 2024-02-13. Revision uploaded 2024-03-18. Final acceptance 2024-03-23.)  \n© 2024 by the authors of this article. Published under CC-BY.  \niJIM | Vol. 18 No. 9 (2024) International Journal of Interactive Mobile Technologies (iJIM) 35  \nWang  \n1 INTRODUCTION  \nWith the proliferation and development of online education, student interaction analysis systems on platforms have emerged as pivotal tools for enhancing teaching quality and learning efficiency. Particularly in the realms of massive open online courses (MOOCs) and remote instruction, the optimization of such systems plays a critical role in understanding student behaviors, enhancing engagement, and improving course retention rates [1–4]. Against this backdrop, the integration of machine learning and computer networking technologies offers new opportunities for optimizing student interaction analysis systems on online education platforms. The formida","cbCaihfFE1JPtp6J","https://ap.wps.com/l/cbCaihfFE1JPtp6J","pdf",983308,1,15,"English","en",105,"# Introduction\n## Background and motivation\n## Current research challenges and limitations","[{\"question\":\"Why is student interaction analysis important in online education platforms?\",\"answer\":\"It helps enhance teaching quality and learning efficiency by understanding student behavior, improving engagement, and supporting course retention. It also enables timely insight into students’ progress and needs.\"},{\"question\":\"What problem do traditional anomaly detection methods face?\",\"answer\":\"They may not clearly distinguish normal fluctuations from real anomalies, causing false positives or false negatives. This limits the reliability of interaction irregularity detection.\"},{\"question\":\"How does the proposed approach improve system performance?\",\"answer\":\"It integrates machine learning with advanced interactive mobile technologies for real-time processing and improved anomaly detection. It also develops a congestion control algorithm to optimize mobile data transmission stability and efficiency.\"}]","Improvement of Student Interaction Analysis in Online Education Platforms through Interactive Mobile Technology and Machine Learning Integration | PDF",1785808194,38,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"improvement-of-student-interaction-analysis-in-online-education-platforms-through-interactive-mobile-technology-and-machine-learning-integration","",{"@graph":36,"@context":85},[37,54,68],{"@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/improvement-of-student-interaction-analysis-in-online-education-platforms-through-interactive-mobile-technology-and-machine-learning-integration/121995/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is student interaction analysis important in online education platforms?","Question",{"text":75,"@type":76},"It helps enhance teaching quality and learning efficiency by understanding student behavior, improving engagement, and supporting course retention. It also enables timely insight into students’ progress and needs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem do traditional anomaly detection methods face?",{"text":80,"@type":76},"They may not clearly distinguish normal fluctuations from real anomalies, causing false positives or false negatives. This limits the reliability of interaction irregularity detection.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach improve system performance?",{"text":84,"@type":76},"It integrates machine learning with advanced interactive mobile technologies for real-time processing and improved anomaly detection. 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