Personalized Learning Recommendations Based on Feature Extraction and Attention Mechanisms

Personalized Learning Recommendations Based on Feature Extraction and Attention Mechanisms | 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 Personalized Learning Recommendations Based on Feature Extraction and Attention Mechanisms Hui Li, Shuai Wu, Shue Gu, Ronghui Wang, Yanyan Chen, Haining Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7223911/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract In recent years, the rise of online learning platforms has made personalized learning resource recommendation systems a key research focus. However, many existing algorithms fail to account for the evolving interests and needs of learners, limiting their effectiveness. To address this, we propose DynLearn-Adapt, a novel recommendation model that dynamically captures changes in student knowledge and behavior. First, the model uses convolutional neural networks to extract local learning features. It then employs a Transformer with a multi-head attention mechanism to model dependencies between skills, enhancing representational capacity. To track knowledge evolution and forgetting effects, DynLearn-Adapt integrates long- and short-term memory networks, enabling real-time updates to recommendations based on students? latest learning status. Additionally, positional encoding is introduced to better handle temporal information in learning sequences. Experiments conducted on two public datasets demonstrate that DynLearn-Adapt outperforms several baselines across multiple metrics, including accuracy, precision, recall, F1 score, and AUC, confirming its effectiveness and practical value. personalized recommendations dynamic tracking long and short-term memory attentional mechanisms feature extraction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 28 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 26 Jul, 2025 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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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7223911","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492200856,"identity":"130e72dd-4c20-4bf2-bab2-e26e63cb9669","order_by":0,"name":"Hui Li","email":"","orcid":"","institution":"Jiangsu Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""},{"id":492200857,"identity":"aaa35ef3-251b-4386-9404-662b4bb63ae3","order_by":1,"name":"Shuai Wu","email":"","orcid":"","institution":"Jiangsu Ocean 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