A Multi-Dimensional Hybrid Stochastic Model for Early and Interpretable Blockage Detection in Programming Education

A Multi-Dimensional Hybrid Stochastic Model for Early and Interpretable Blockage Detection in Programming Education | 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 A Multi-Dimensional Hybrid Stochastic Model for Early and Interpretable Blockage Detection in Programming Education Grota Abdelkader, Erritali Mohammed, Etcheverry Patrick, Nodenot Thierry This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9225464/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Detecting student difficulties in programming education is a critical challenge for instructors, a task further complicated by the rise of AI coding assistants that can obscure the learning process. While fine-grained interaction logs from development environments offer rich behavioral data, existing analytical models often focus on a single dimension, limiting their predictive power and interpretability. This paper proposes a multi-dimensional hybrid stochastic model that integrates three complementary analytical layers to provide a more holistic understanding of student behavior. The model combines: (1) a Markov Chain to capture the behavioral dimension of workflow transitions; (2) a Hidden Markov Model (HMM) to infer the latent cognitive dimension, identifying states such as progression, hesitation, and blockage; and (3) a Recurrent Neural Network (RNN) with an attention mechanism to model the sequential dimension by capturing long-range temporal dependencies. We validated our approach on a dataset of interaction traces collected from 70 first-year computer science students. The hybrid model achieved a macro-averaged F1-score of 88.1% across four distinct cognitive states (Progressing, Hesitating, Blocked, Confused), significantly outperforming all baseline and ablation models as confirmed by a Friedman test followed by a Nemenyi post-hoc analysis (p < .001). For the critical task of blockage detection, the model obtained a precision of 92.5% and a recall of 90.0%. By providing both high-level cognitive state labels and low-level attention-based explanations, our framework bridges the gap between predictive accuracy and pedagogical interpretability. The findings demonstrate that this integrated, multi-dimensional approach offers a robust and effective foundation for developing real-time, evidence-based support systems for programming education. Educational Data Mining Learning Analytics Student Blockage Detection Programming Education Hybrid Models Hidden Markov Models Recurrent Neural Networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor invited by journal 30 Mar, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 25 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. 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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-9225464","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621733709,"identity":"316a4517-ba03-41c5-aa38-c04f58eab824","order_by":0,"name":"Grota Abdelkader","email":"data:image/png;base64,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","orcid":"","institution":"Faculty of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Grota","middleName":"","lastName":"Abdelkader","suffix":""},{"id":621733710,"identity":"76a84d8f-20a7-4b57-b907-fc8f9fdc6935","order_by":1,"name":"Erritali Mohammed","email":"","orcid":"","institution":"University of Pau and the Adour Region","correspondingAuthor":false,"prefix":"","firstName":"Erritali","middleName":"","lastName":"Mohammed","suffix":""},{"id":621733711,"identity":"64565d35-8141-4b72-a150-d8b6dabd4c10","order_by":2,"name":"Etcheverry Patrick","email":"","orcid":"","institution":"University of Pau and the Adour Region","correspondingAuthor":false,"prefix":"","firstName":"Etcheverry","middleName":"","lastName":"Patrick","suffix":""},{"id":621733712,"identity":"3380684f-92a3-4393-ba8e-d8a286863f4a","order_by":3,"name":"Nodenot Thierry","email":"","orcid":"","institution":"University of Pau and the Adour Region","correspondingAuthor":false,"prefix":"","firstName":"Nodenot","middleName":"","lastName":"Thierry","suffix":""}],"badges":[],"createdAt":"2026-03-25 16:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9225464/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9225464/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106960263,"identity":"33473159-b8c4-4811-ae60-ca6ccf79cb57","added_by":"auto","created_at":"2026-04-15 09:19:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2329626,"visible":true,"origin":"","legend":"","description":"","filename":"DiscoverInformatics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9225464/v1_covered_0593d378-396b-4f30-9abd-c7da615c03b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multi-Dimensional Hybrid Stochastic Model for Early and Interpretable Blockage Detection in Programming Education","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":"[email protected]","identity":"discover-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Informatics](https://link.springer.com/journal/44564)","snPcode":"44564","submissionUrl":"","title":"Discover Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Educational Data Mining, Learning Analytics, Student Blockage Detection, Programming Education, Hybrid Models, Hidden Markov Models, Recurrent Neural Networks","lastPublishedDoi":"10.21203/rs.3.rs-9225464/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9225464/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\nDetecting student difficulties in programming education is a critical challenge for instructors, a task further complicated by the rise of AI coding assistants that can obscure the learning process. While fine-grained interaction logs from development environments offer rich behavioral data, existing analytical models often focus on a single dimension, limiting their predictive power and interpretability. This paper proposes a multi-dimensional hybrid stochastic model that integrates three complementary analytical layers to provide a more holistic understanding of student behavior. The model combines: (1) a Markov Chain to capture the behavioral dimension of workflow transitions; (2) a Hidden Markov Model (HMM) to infer the latent cognitive dimension, identifying states such as progression, hesitation, and blockage; and (3) a Recurrent Neural Network (RNN) with an attention mechanism to model the sequential dimension by capturing long-range temporal dependencies. We validated our approach on a dataset of interaction traces collected from 70 first-year computer science students. 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