AI-Driven Personalized Learning Analytics Platform

AI-Driven Personalized Learning Analytics Platform | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-Driven Personalized Learning Analytics Platform KAMPA ABHINAY TEJ, GARAPATI SRIKRISHNA, PERAM JASWANTH REDDY, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8487363/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the rapidly evolving landscape of educational technology, traditional one-size-fits-all approaches are increasingly inadequate for addressing diverse student needs. This paper proposes an AI-driven platform for personalized learning analytics that collects, processes, and analyzes student data from multiple sources to provide customized learning paths and realtime insights. The system leverages advanced data analytics, machine learning, and streaming data technologies to monitor student engagement, identify learning patterns, and deliver personalized recommendations. By integrating data from learning management systems (LMS) and real-time student activity logs, the platform enables educators to make data-informed decisions and provide timely interventions. The system architecture is designed with scalability in mind, ensuring it can handle large datasets while maintaining compliance with data privacy regulations such as GDPR and ISO/IEC 27001. Through real-time analytics and personalized recommendations, the platform aims to improve student outcomes, enhance teaching strategies, and enable proactive interventions. This paper details the system’s architecture, methodologies, implementation challenges, and potential impact on educational institutions. Experimental results demonstrate significant improvements in student engagement, learning outcomes, and educator effectiveness when compared to traditional approaches. Artificial Intelligence and Machine Learning Information Retrieval and Management Artificial Intelligence Educational Data Mining Learning Analytics Personalized Learning Real-Time Analytics Data Privacy Adaptive Learning Student Performance Educational Technology Streaming Data Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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