A Data-Driven Cognitive Feature–Based Model for English Text Readability Assessment to Support College English Instruction

A Data-Driven Cognitive Feature–Based Model for English Text Readability Assessment to Support College English Instruction | 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 A Data-Driven Cognitive Feature–Based Model for English Text Readability Assessment to Support College English Instruction Jing Zhao, Congrong Zou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8598697/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Text readability assessment is essential for effective college English teaching and instructional material selection. Traditional readability models mainly rely on surface linguistic features and often fail to reflect the cognitive processes involved in reading comprehension. To address this limitation, this study proposes a cognitive feature–based approach for English text readability assessment, aiming to support data-driven English teaching evaluation. The proposed method incorporates cognitive features related to lexical rarity, logical complexity, and comprehension difficulty, and integrates them with conventional linguistic features. Multiple machine learning models are employed and evaluated on four benchmark datasets, including CEFR, CLEC, OneStopEnglish, and RACE. Experimental results show that models combining cognitive and linguistic features consistently outperform those using linguistic features alone across multiple evaluation metrics. The findings indicate that cognitive features provide complementary information for readability assessment and enhance the discriminability of readability levels. This study offers practical implications for college English teaching by enabling more accurate matching between reading materials and learners’ proficiency levels, thereby supporting personalized and data-driven instructional decision-making. Humanities/Language and linguistics Social science/Language and linguistics Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology English Teaching Text Readability Assessment Cognitive Features Educational Data Mining Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviews received at journal 29 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers invited by journal 25 Feb, 2026 Editor invited by journal 22 Jan, 2026 Editor assigned by journal 20 Jan, 2026 Submission checks completed at journal 20 Jan, 2026 First submitted to journal 14 Jan, 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. 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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-8598697","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":598699365,"identity":"f62a35b1-3347-4936-afa4-5ff892d9cabd","order_by":0,"name":"Jing Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACNvnDBx98/CfBw8/efPBBQkUNYS18EmzJhjPYbGQke44lGzw4c4ywFjkJHjVpHrY0G4MbOWaSD1uYiXCYdA+DNA/PYR6QlorEBjYG/vbuBPxaZM4eMJwjcZhH8syzshuJO2QYJM6c3YBfC0NeQsIbg8M8fMeTt91IPMPGYCCRS0hLjsEBnoTDPAwHEswKEtuYidAikWPYyHMgjUfgRIoZA3FaeI4lM85ssOEBBbJEwpljPAT9It/efPzHxwYJe1BUfvxRUSPH396LXwsG4CFN+SgYBaNgFIwCrAAASMFMLkCvCOgAAAAASUVORK5CYII=","orcid":"","institution":"Hubei Normal University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhao","suffix":""},{"id":598699366,"identity":"f3f383ec-1c64-48a1-8336-07f5f9736dc0","order_by":1,"name":"Congrong Zou","email":"","orcid":"","institution":"Hubei Normal University","correspondingAuthor":false,"prefix":"","firstName":"Congrong","middleName":"","lastName":"Zou","suffix":""}],"badges":[],"createdAt":"2026-01-14 07:24:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8598697/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8598697/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103775298,"identity":"4823dea7-4824-4d80-8960-b5af426f9074","added_by":"auto","created_at":"2026-03-02 18:25:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":767002,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8598697/v1_covered_d0f88d7a-8bbe-4c40-9eac-d72236f11a8c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Data-Driven Cognitive Feature–Based Model for English Text Readability Assessment to Support College English Instruction","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":"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":"English Teaching, Text Readability Assessment, Cognitive Features, Educational Data Mining, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-8598697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8598697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Text readability assessment is essential for effective college English teaching and instructional material selection. 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