DPKT: Difficulty-Aware Programming Knowledge Tracing with Large Language Models

DPKT: Difficulty-Aware Programming Knowledge Tracing with Large Language Models | 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 DPKT: Difficulty-Aware Programming Knowledge Tracing with Large Language Models Lina Yang, Xinjie Sun, Hui Li, Ran Xu, Xuqin Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5322525/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Knowledge Tracing ( KT ) assesses students’ mastery of specific knowledge concepts and predicts their problem-solving abilities by analyzing their interactions with intelligent tutoring systems. Although recent years have seen significant improvements in tracking accuracy with the introduction of deep learning and graph neural network techniques, existing research has not sufficiently focused on the impact of difficulty on knowledge state. The text understanding difficulty and knowledge concept difficulty of programming problems are crucial for students’ responses; thus, accurately assessing these two types of difficulty and applying them to knowledge state prediction is a key challenge. To address this challenge, we propose a Difficulty-aware Programming Knowledge Tracing with Large Language Models ( DPKT ) to extract the text understanding difficulty and knowledge concept difficulty of programming problems. Specifically, we analyze the relationship between knowledge concept difficulty and text understanding difficulty using an attention mechanism, allowing for dynamic updates to students’ s. This model combines an update gate mechanism with a graph attention network, significantly improving the assessment accuracy of programming problem difficulty and the spatiotemporal reflection capability of knowledge state. Experimental results demonstrate that this model performs excellently across various language datasets, validating its application value in programming education. This model provides an innovative solution for programming knowledge tracing and offers educators a powerful tool to promote personalized learning. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Feb, 2025 Reviews received at journal 24 Feb, 2025 Reviewers agreed at journal 18 Feb, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviews received at journal 08 Dec, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviewers invited by journal 13 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Editor invited by journal 08 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 24 Oct, 2024 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-5322525","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379727337,"identity":"5e8f37c0-ee09-4b8d-b81b-2c52ed9119b1","order_by":0,"name":"Lina Yang","email":"","orcid":"","institution":"Liupanshui Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Yang","suffix":""},{"id":379727338,"identity":"570c5031-bddb-4613-8fee-1a01a75c0ad9","order_by":1,"name":"Xinjie Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACZgYGgwQQg725ASqUQKwWnoPEaoEDiUQitRgc5z1Q8KCiLnG75MO2hz/+HGbgZ88xYPi5A7cWyWa+BIOEM4cTd85ObDfmbTvMINnzxoCx9wxuLfzMPAYGiW0HEjfcTmyTZmw4zGBwI8eAmbENtxY2sJZ/dYkbbh5skwQ5zJ6QFogtDcyJG24wtknwsAFtkSCgRbIZqCXh2GHjDWfAfknnkTjzrOBgLx4tBufPmBn+qKmT3XD88DFgiFnL8bcnb3zwE48WkHcMYAwgbuYBsQ7g1QCM/wdIWuoIKB4Fo2AUjIKRCADJ9lIZYD8fnQAAAABJRU5ErkJggg==","orcid":"","institution":"Liupanshui Normal University","correspondingAuthor":true,"prefix":"","firstName":"Xinjie","middleName":"","lastName":"Sun","suffix":""},{"id":379727339,"identity":"54535f82-306c-4c7d-9ce3-ec09af4eaa89","order_by":2,"name":"Hui Li","email":"","orcid":"","institution":"Liupanshui Normal University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""},{"id":379727340,"identity":"cd53489b-ebf8-40c1-844b-febc2c8e44f2","order_by":3,"name":"Ran Xu","email":"","orcid":"","institution":"Liupanshui Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Xu","suffix":""},{"id":379727341,"identity":"31cc3528-b64c-4af6-a5c0-1ca595316ccd","order_by":4,"name":"Xuqin Wei","email":"","orcid":"","institution":"Liupanshui Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xuqin","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-10-24 04:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5322525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5322525/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-96540-3","type":"published","date":"2025-04-03T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80081925,"identity":"40b41556-de0e-4bad-bf91-ccacbefa5b43","added_by":"auto","created_at":"2025-04-07 16:01:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":912370,"visible":true,"origin":"","legend":"","description":"","filename":"DPKTDifficultyAwareProgrammingKnowledgeTracingwithLargeLanguageModels.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5322525/v1_covered_1a38aa36-bfaf-4e6c-85fd-f0ea625679cd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DPKT: Difficulty-Aware Programming Knowledge Tracing with Large Language Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5322525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5322525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Knowledge Tracing ( KT ) assesses students’ mastery of specific knowledge concepts and predicts their problem-solving abilities by analyzing their interactions with intelligent tutoring systems. 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