Student Performance and LMS Activity in Primary Schools: A Bayesian Additive Regression Trees Approach with Random Effects

Student Performance and LMS Activity in Primary Schools: A Bayesian Additive Regression Trees Approach with Random Effects | 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 Student Performance and LMS Activity in Primary Schools: A Bayesian Additive Regression Trees Approach with Random Effects Natalia da Silva, Bruno Tancredi, Ignacio Alvarez-Castro This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7357300/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Apr, 2026 Read the published version in Quality & Quantity → Version 1 posted You are reading this latest preprint version Abstract Using data collected on almost all students aged 9 to 12 in Uruguay, we apply Bayesian Additive Regression Trees (BART) with random effects to study the association between student performance, Learning Management System (LMS) activity, and socioeconomic status. Performance data were combined with LMS activity pattern data.BART was chosen because of its strong predictive performance in high-dimensional problems. Additionally, it allows the inclusion of school-level random effects and, as a Bayesian method, provides an internal measure of uncertainty for its predictions.Results suggest that the model can be used for the early identification of at-risk students and to highlight schools that are either particularly successful or in need of intervention. LMS activity is characterized by several predictor variables, making it difficult to assess its overall effect on student performance. For example, some methods widely used in machine learning applications—such as variable importance measures and profile plots (e.g., Partial Dependence Plots)—are not suitable, as they are designed to evaluate only one or a few predictor variables. We address this limitation using a synthetic student profile approach to assess the effect of LMS activity on academic performance. An interesting finding is that high levels of LMS usage have greater positive effects on performance, particularly among students from low socioeconomic backgrounds. Education data fusion applied statistics data visualization data science machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Apr, 2026 Read the published version in Quality & Quantity → 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-7357300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521805068,"identity":"ad7c40ba-d494-4c47-9cf9-9d84ceb47926","order_by":0,"name":"Natalia da Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie2QvWrDMBCAzxisDiJdlaHpK1wJ9IdAnkVGUC0OZAykEE+eDH2VvkEVBPVi8OrRxdAuHQRdMpTSS0KHguO2Wyn6kMTppA/dCcDj+as0SIuh0QCMWLrLhT336Ux+KhJgzM2PFNgr2yDOv1MuWLV2cj6FQVE8mjib6pzZtYPFJE5Fgl3KVa5CIVHBsEyQFDXL+bUSUOqDChoFpIQUJNRLGc7uBUcRZPawUrXhRuKKgueGlJXm4vh1E7z3KLWK6BVLgUQjF1ZywSMRpH1Ke34pseDD+mWrFGfUC2Ue9DjjT/PuwuK2dm/L0aDSrXO4POXMUuZmcnLL1F33N+/hX7eSZgRHnYX1wppfKx6Px/Mf+QBwjWWQhdkUDAAAAABJRU5ErkJggg==","orcid":"","institution":"University of the Republic","correspondingAuthor":true,"prefix":"","firstName":"Natalia","middleName":"da","lastName":"Silva","suffix":""},{"id":521805069,"identity":"3613850e-c5bd-4abf-a8e2-0b6cf521924e","order_by":1,"name":"Bruno Tancredi","email":"","orcid":"","institution":"University of the Republic","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"","lastName":"Tancredi","suffix":""},{"id":521805070,"identity":"a5991937-2968-4bc8-828f-a73c2b189a48","order_by":2,"name":"Ignacio Alvarez-Castro","email":"","orcid":"","institution":"University of the Republic","correspondingAuthor":false,"prefix":"","firstName":"Ignacio","middleName":"","lastName":"Alvarez-Castro","suffix":""}],"badges":[],"createdAt":"2025-08-12 15:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7357300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7357300/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11135-026-02739-5","type":"published","date":"2026-04-16T15:58:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92459779,"identity":"5f8724a7-1ab0-4278-8e25-4e06bc9839ea","added_by":"auto","created_at":"2025-09-30 03:20:42","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5227,"visible":true,"origin":"","legend":"","description":"","filename":"cb747ce5dd6d418ab943a4656f5fd46b.json","url":"https://assets-eu.researchsquare.com/files/rs-7357300/v1/06ed722d9bd9d19d9f894d29.json"},{"id":107350815,"identity":"4b8de324-56a6-40a4-8fda-6c51dc312fc4","added_by":"auto","created_at":"2026-04-20 16:05:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":961897,"visible":true,"origin":"","legend":"","description":"","filename":"paperQQceibal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7357300/v1_covered_6871c03c-bed5-45aa-95a9-075ad5bea254.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Student Performance and LMS Activity in Primary Schools: A Bayesian Additive Regression Trees Approach with Random Effects","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Education, data fusion, applied statistics, data visualization, data science, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7357300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7357300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Using data collected on almost all students aged 9 to 12 in Uruguay, we apply Bayesian Additive Regression Trees (BART) with random effects to study the association between student performance, Learning Management System (LMS) activity, and socioeconomic status. 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