A Modular Approach to Cyclical Self-Regulated Learning Modeling with Machine Learning

A Modular Approach to Cyclical Self-Regulated Learning Modeling with Machine Learning | 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 Modular Approach to Cyclical Self-Regulated Learning Modeling with Machine Learning Andrew Schwabe, Özgür Akgün, Ella Haig This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8109467/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 Self-regulated learning (SRL) is essential for academic success, encompassing cog-nitive, metacognitive, motivational, and emotional processes that enable studentsto plan, perform, and reflect on their learning. This paper reviews foundationalSRL theory, highlighting models such as Zimmerman’s cyclical framework, whichorganizes activities into forethought, performance, and self-reflection phases,alongside contributions from Boekaerts, Winne and Hadwin, Pintrich, Efklides,and Hadwin et al. It examines interventions, which have proven effective in tra-ditional settings for enhancing SRL but are resource-intensive and challengingto scale. Measurement approaches are discussed, including offline methods, likesurveys, and online techniques using trace and multimodal data, though the lat-ter often provide biased, incomplete representations of SRL by focusing solelyon digital interactions and neglecting offline activities or theoretical mappings.In this paper, we explore methods to use machine learning to model cyclicalSRL, review challenges of applying modern techniques to represent SRL the-ory models, test SRL-inspired feature engineering from trace data, and proposea modular machine learning approach that partitions data by macro-phases tocapture causal insights and cyclical reinforcement otherwise lost in monolithicmodels (a single unified approach), enabling scalable intervention simulation forpersonalized e-learning. Self Regulated Learning eLearning Machine Learning Artificial Intelligence Full Text Additional Declarations No competing interests reported. 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. 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-8109467","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":546057055,"identity":"81d9904f-2561-4ded-ac62-7a0b542570a6","order_by":0,"name":"Andrew Schwabe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie2RsWrDQAyGVQ6S5dJb5aUd8gIOhUImv4pNwJNbyOYhmMviNS8Q+hiZZQSermR18ZIunTy4L9DEIUOGkHOzZbhvEkIf0o8AHI67RFDRpqcSz12yKYOI0Zym/6vIMY3yWxS1MkDeR5bB8LOo05QDGPJOSHNdweqNaL5hBPk+mxrDkZaxL2Rluat6DMnbEAaUvHrLvA4BEhCyvW48b41Po3WGoJpO+auDrrArPiWdogUCHrfo+kHjcYvlsEkVh4wlexqbl6ku91GOP36xtsR/2jL/totMgUomX3oRB0rNvndNaYl/wQB6HulwOByOfg4ss1MYHzM54AAAAABJRU5ErkJggg==","orcid":"","institution":"University of St. Andrews","correspondingAuthor":true,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Schwabe","suffix":""},{"id":546057056,"identity":"93d75582-3ada-4f81-9b40-bc71115e73e6","order_by":1,"name":"Özgür Akgün","email":"","orcid":"","institution":"University of St. Andrews","correspondingAuthor":false,"prefix":"","firstName":"Özgür","middleName":"","lastName":"Akgün","suffix":""},{"id":546057057,"identity":"d803f439-4aa8-4620-a6e3-92956dda34c4","order_by":2,"name":"Ella Haig","email":"","orcid":"","institution":"University of Portsmouth","correspondingAuthor":false,"prefix":"","firstName":"Ella","middleName":"","lastName":"Haig","suffix":""}],"badges":[],"createdAt":"2025-11-14 00:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8109467/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8109467/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96459927,"identity":"8aae7719-20e4-4897-b910-5809596a76a9","added_by":"auto","created_at":"2025-11-21 10:15:21","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5530,"visible":true,"origin":"","legend":"","description":"","filename":"681ec820cc244a579238bdf76ff97109.json","url":"https://assets-eu.researchsquare.com/files/rs-8109467/v1/63c2d4738ea79a702641608f.json"},{"id":104962774,"identity":"de756cd8-ca32-43be-a55c-3b3cd547a176","added_by":"auto","created_at":"2026-03-19 09:14:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2529120,"visible":true,"origin":"","legend":"","description":"","filename":"AIEdJournal2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8109467/v1_covered_88ba1244-e7bc-49f1-b1a5-a5a6eea949d6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA Modular Approach to Cyclical Self-Regulated Learning Modeling with Machine Learning\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Self Regulated Learning, eLearning, Machine Learning, Artificial Intelligence","lastPublishedDoi":"10.21203/rs.3.rs-8109467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8109467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Self-regulated learning (SRL) is essential for academic success, encompassing cog-nitive, metacognitive, motivational, and emotional processes that enable studentsto plan, perform, and reflect on their learning. 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