{"paper_id":"1a4b4cc9-52a2-45df-8255-c163ae3227de","body_text":"Modeling students’ Chinese language learningpathways by introducing a behavior-driven semanticgraph construction mechanism | 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 Modeling students’ Chinese language learningpathways by introducing a behavior-driven semanticgraph construction mechanism Ming Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9258679/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 This study introduces an innovative approach to modeling students’ Chinese language learning pathways through abehavior-driven semantic graph construction mechanism. Traditional language learning assessment and modeling methodsoften fail to capture the nuanced and dynamic nature of language acquisition, as they overlook the significance of behavioralinteractions and contextual relationships in shaping learning outcomes. The proposed Behavior-Driven Semantic GraphModel (BDSGM) bridges this gap by integrating learners’ behavioral data such as engagement patterns, response timing, andtask completion sequences with semantic information derived from language content. Through this integration, the modelconstructs a continuously evolving graph that represents the learner’s cognitive and behavioral trajectory, enabling adaptiveinterpretation of learning progress. Complementing this model, the Behavior-Driven Semantic Graph Strategy leverages theinsights generated by BDSGM to intelligently adjust instructional pathways, recommending targeted learning materials andpersonalized practice exercises. This dual-framework approach not only facilitates a deeper understanding of individuallearning behaviors but also enhances pedagogical precision and adaptability. By aligning semantic relationships with behavioralpatterns, the methodology provides a robust foundation for data-driven personalization in language education. Ultimately, thisresearch contributes to the advancement of intelligent educational systems that promote individualized, efficient, and engagingChinese language learning experiences.Keywords: Chinese language learning, semantic graph, behavior-driven model, learning pathways, personalized education Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Social science/Science technology and society Chinese language learning semantic graph behavior-driven model learning pathways personalized education Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor invited by journal 01 Apr, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 29 Mar, 2026 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-9258679\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":621519533,\"identity\":\"6223d19e-feeb-423d-8d0a-63d5348ae686\",\"order_by\":0,\"name\":\"Ming Li\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Nanjing Normal University of Special Education\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Ming\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-29 12:23:17\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9258679/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9258679/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":107122534,\"identity\":\"36dce602-5325-4d43-97bb-c671fbbb15dc\",\"added_by\":\"auto\",\"created_at\":\"2026-04-17 04:55:53\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1179107,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ScientificReports.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9258679/v1_covered_c4ddaa85-f610-414b-b466-c616128244ed.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Modeling students’ Chinese language learningpathways by introducing a behavior-driven semanticgraph construction mechanism\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"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\":\"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\":\"Chinese language learning, semantic graph, behavior-driven model, learning pathways, personalized education\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9258679/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9258679/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"This study introduces an innovative approach to modeling students’ Chinese language learning pathways through abehavior-driven semantic graph construction mechanism. 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