{"paper_id":"06df2119-64c4-4dfc-956b-ea8331f32625","body_text":"A Time-Sensitive Knowledge Tracing Method for Educational Data Analysis | 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PDF Article A Time-Sensitive Knowledge Tracing Method for Educational Data Analysis Li Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8702725/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 Knowledge tracing aims to dynamically model students’ knowledge states by analyzing their learning interaction behaviors and represents an important problem in educational data analysis and learning analytics. With the rapid growth of educational data, learning behaviors exhibit pronounced temporal characteristics and irregular time intervals. Effectively modeling the influence of learning order and temporal factors on knowledge retention remains a key challenge in knowledge tracing research. To address this challenge, this paper proposes a time-sensitive knowledge tracing method for educational data analysis, termed Ts-DKT. The proposed method is built upon the Transformer architecture and incorporates a hybrid positional encoding mechanism to enhance sensitivity to learning sequence order. In addition, a time-decay mechanism is introduced to model the forgetting process of knowledge over time, enabling a more accurate representation of the dynamic evolution of students’ knowledge states. Furthermore, a self-attention mechanism is employed to capture long-range dependencies within learning interaction sequences. Experimental results on multiple real-world educational datasets demonstrate that Ts-DKT consistently outperforms representative knowledge tracing models, including DKT, BKT, and AKT, across evaluation metrics such as AUC, accuracy, precision, and recall. These findings indicate that the proposed method achieves improved predictive performance and robustness in educational knowledge tracing tasks, providing valuable algorithmic insights for educational data analysis and learning analytics. Business and commerce/Information systems and information technology Physical sciences/Mathematics and computing Knowledge tracing Educational data analysis Self-attention mechanism Transformer model Time-decay mechanism Full Text Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8702725\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":586879704,\"identity\":\"b0767386-2803-4904-8e32-882db7cace75\",\"order_by\":0,\"name\":\"Li Lu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIie3RsQrCMBCA4ZNCdIhmkxTBvkLExcGHaRCcigguHSNCJt0tvkQf4UrAqT6Bi+ILBFw6CGpXERo3h3zz/cPdAXjeH+pBoNCK6ZCwDaKtHBICLVXs0/mY8aMssq1jEtDSyFAlY9MhLgmXa+zqIBZYWgMUItbHxkRhqMlCmF1ulhMYZYfYIRlpuhJ4ys2eQizOLonUXOaYXAwlrgmWQmYqAceEXlWh0rg+sngfmTfvwtozc3+IZ/3Km7XVNGKDhuQT/23c8zzP++4FcUBMXvVtIHkAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Nanjing University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Li\",\"middleName\":\"\",\"lastName\":\"Lu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-01-26 17:23:17\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8702725/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8702725/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":103504195,\"identity\":\"2b27ee72-d7ad-4a70-b35f-b019cbd3f312\",\"added_by\":\"auto\",\"created_at\":\"2026-02-26 13:18:19\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":820399,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Manuscript2.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8702725/v1_covered_55b61252-c300-4d09-9aa4-501742458829.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A Time-Sensitive Knowledge Tracing Method for Educational Data Analysis\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Knowledge tracing, Educational data analysis, Self-attention mechanism, Transformer model, Time-decay mechanism\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8702725/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8702725/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Knowledge tracing aims to dynamically model students’ knowledge states by analyzing their learning interaction behaviors and represents an important problem in educational data analysis and learning analytics. 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