QSDKT: Graph-Based Dynamic Knowledge Tracing through Question-Skill Similarity

QSDKT: Graph-Based Dynamic Knowledge Tracing through Question-Skill Similarity | 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 QSDKT: Graph-Based Dynamic Knowledge Tracing through Question-Skill Similarity Yue Li, Xianghong Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6856481/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 trace learners’ evolving knowledge states by predicting their future performance. In the teaching and learning processes, question difficulty is usually related to knowledge skills, and the effects of question difficulty and skill difficulty on students’ knowledge states are dynamic. In some recent studies, the effect of difficulty in questions has been considered, but dynamically assessing the difficulty relationship between questions and skills has not been explored and utilized effectively. In this paper, we propose a graph-based dynamic knowledge tracing model through question-skill similarity, named QSDKT. The model is used to address the challenge of effectively capturing learners’ dynamic knowledge states. First, we construct a relation graph and define difficulty levels based on the similarity between questions and skills. Second, we employ graph convolutional networks to capture the complex relationships between questions and skills. Finally, we design an adaptive sequential learning network to adjust question difficulty and track learners’ knowledge states dynamically. Experiments on three real-world datasets validate the effectiveness of QSDKT. The results show that QSDKT achieves higher accuracy in predicting the knowledge states of students, and the adaptive network’s dynamic adjustment of the difficulty of the questions further enhances the personalized learning of the model. Knowledge tracing Question difficulty Adaptive network Dynamic adjustment 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. 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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-6856481","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471742405,"identity":"b77d03e6-f9b0-46ab-a3cf-94ca04a273d0","order_by":0,"name":"Yue Li","email":"","orcid":"","institution":"College of Computer Science and Enginnering, Northwest Normal University, Lanzhou, 730070, China","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Li","suffix":""},{"id":471742406,"identity":"aba075fc-dd2b-48bc-9832-b314775b9e13","order_by":1,"name":"Xianghong Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPmYGBmaGCiiPhxgtbGAtZ0jSAsTMjG0kaWHnPSZdOO9O4naJBMYHb9sY5M0JO4wv2XjmtmeJO2ckMBvObWMw3NlAUAuP4WPebYdzN9xIYJPmbWNIMDhAWIvBYd45YC3sv4nVArSlAWILM7FajI15jh2u33DmYbPknHMShhsIaeHnP2MmzVNz2NjgePLBD2/KbOQJ2oIEGBuAhATx6kfBKBgFo2AU4AYAM3M4E83QiOQAAAAASUVORK5CYII=","orcid":"","institution":"College of Computer Science and Enginnering, Northwest Normal University, Lanzhou, 730070, China","correspondingAuthor":true,"prefix":"","firstName":"Xianghong","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-06-09 17:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6856481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6856481/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84846980,"identity":"78f4ca73-3e79-473b-a7e7-d81c7c367b88","added_by":"auto","created_at":"2025-06-18 03:21:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1246713,"visible":true,"origin":"","legend":"","description":"","filename":"QSDKTGraphBasedDynamicKnowledgeTracingthroughQuestionSkillSimilarity.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6856481/v1_covered_684ff6d8-b77f-4ccc-816d-a2933e90ec69.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eQSDKT: Graph-Based Dynamic Knowledge Tracing through Question-Skill Similarity\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"[email protected]","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, Question difficulty, Adaptive network, Dynamic adjustment","lastPublishedDoi":"10.21203/rs.3.rs-6856481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6856481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKnowledge tracing aims to trace learners’ evolving knowledge states by predicting their future performance. 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