Causal Attention Graph Knowledge Tracing

Causal Attention Graph Knowledge Tracing | 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PDF Article Causal Attention Graph Knowledge Tracing Mengran Tian, Zhihao Wang, Xiaohui Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7073568/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 Graph-based Knowledge Tracing (GKT) is a prominent variant of Graph Neural Networks (GNNs) utilized in the field of knowledge tracing. It effectively redefines knowledge tracing as a time-series node-level classification problem within GNNs by modeling coursework as a structured graph. However, it is hard to distinguish causal and non-causal relationships for GNNs, because most GNNs adhere to the ‘learning to attend’ paradigm, which may result in broad and non-selective handling of the relationship between features and goals. This leads to the poor generalization of GNN classifiers. To enhance the generalization and robustness of model classification, we propose a knowledge-tracking model called Causal Attention Graph-based Knowledge Tracing (CAGKT). The method constructs a causal structural model to account for causal relationships between variables and eliminates confounding factors by cutting off backdoor paths, and utilizes causal features for final prediction. Subsequently, the Temporal Convolutional Network model is used to extract more comprehensive concept features of the student at each moment. Then the attention module aggregates the features of neighboring nodes to update the graph features. Finally, the graph features are decoupled into causal and shortcut features, and students' answers are predicted based on causal features. Comparison and ablation experiments on open datasets show that the proposed method can improve student achievement prediction and outperform existing models in terms of performance. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files output.bbl 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. 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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-7073568","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525155926,"identity":"c16da7b6-017b-426e-b532-c1a09f0d225d","order_by":0,"name":"Mengran Tian","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Mengran","middleName":"","lastName":"Tian","suffix":""},{"id":525155927,"identity":"309849a7-32a1-402f-9514-fe62f360df48","order_by":1,"name":"Zhihao Wang","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"Wang","suffix":""},{"id":525155928,"identity":"dd7ea13c-3706-4e93-b08a-e2dc51f7fe06","order_by":2,"name":"Xiaohui Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDACCSBmbAASBxgYHyRU1JCmhdngwZljpGlhk3zYwkxYh/zs5oePeXcw2PPd7jGrSGxgY+Bv707Aq4VxzjFjw5lnGBJn3jljdiNxhwyDxJmzG/BqYZZIMJP42MaQYHAjB6jlDBuDgUQufi1sEunfJBLbGOxBWgoS25gJa+GRyAHbwrgBqIWBKC0SEjnFhjPbgH65kVYskXDmGA9Bv8jPSN/4mBfoML4byRs//qiokeNv78WvBQr+I1xKjPJRMApGwSgYBQQAAPz+R/LllJmMAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Normal University","correspondingAuthor":true,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-07-08 10:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7073568/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7073568/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92994974,"identity":"e7f2b21a-bfed-4dde-9344-2035097c2ed0","added_by":"auto","created_at":"2025-10-08 02:58:01","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5433,"visible":true,"origin":"","legend":"","description":"","filename":"14d39475be5749d29c877144d399a17a.json","url":"https://assets-eu.researchsquare.com/files/rs-7073568/v1/390a2d40b8fcaaef86e78e6b.json"},{"id":95224055,"identity":"4eae943a-952f-4562-aef3-5bf3eb864bea","added_by":"auto","created_at":"2025-11-05 16:23:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":406785,"visible":true,"origin":"","legend":"","description":"","filename":"CausalAttentionGraphKnowledgeTracing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7073568/v1_covered_6ffd34ac-3407-47ed-9d7b-094b318ddbfe.pdf"},{"id":92994973,"identity":"c48185c4-ccfb-4a29-80f5-25cfb01a2558","added_by":"auto","created_at":"2025-10-08 02:58:01","extension":"bbl","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4951,"visible":true,"origin":"","legend":"","description":"","filename":"output.bbl","url":"https://assets-eu.researchsquare.com/files/rs-7073568/v1/e95a074ba9898db5713c3d45.bbl"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Attention Graph Knowledge Tracing","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":"[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":"","lastPublishedDoi":"10.21203/rs.3.rs-7073568/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7073568/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Graph-based Knowledge Tracing (GKT) is a prominent variant of Graph Neural Networks (GNNs) utilized in the field of knowledge tracing. It effectively redefines knowledge tracing as a time-series node-level classification problem within GNNs by modeling coursework as a structured graph. However, it is hard to distinguish causal and non-causal relationships for GNNs, because most GNNs adhere to the ‘learning to attend’ paradigm, which may result in broad and non-selective handling of the relationship between features and goals. This leads to the poor generalization of GNN classifiers. To enhance the generalization and robustness of model classification, we propose a knowledge-tracking model called Causal Attention Graph-based Knowledge Tracing (CAGKT). The method constructs a causal structural model to account for causal relationships between variables and eliminates confounding factors by cutting off backdoor paths, and utilizes causal features for final prediction. Subsequently, the Temporal Convolutional Network model is used to extract more comprehensive concept features of the student at each moment. 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