A Machine Learning Approach to Identifying Core Competencies Fostered in College English Flipped Classrooms

A Machine Learning Approach to Identifying Core Competencies Fostered in College English Flipped Classrooms | 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 Machine Learning Approach to Identifying Core Competencies Fostered in College English Flipped Classrooms li liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9189742/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Based on the context of college English flipped classrooms, this study systematically analyzes the key features influencing presentation evaluations and their educational implications by utilizing student peer-assessment data from multi-topic, multi-group classroom presentations. Gradient Boosted Decision Trees (GBDT) classification models and Random Forest regression models were employed for this purpose. In the experimental design, each presentation topic was independently completed by three student groups. Peer assessment categorized the presentations into three grades: A (excellent), B (good), and C (qualified), employing an upper-limit scoring mechanism (A ≤ 100, B ≤ 90, C ≤ 80). Through a combination of horizontal comparison (classification within the same topic) and vertical comparison (score prediction within the same level), the study identifies key influencing features across different levels and score ranges. The findings reveal that the Logical Coherence Index consistently demonstrates the highest feature importance in both models, suggesting that logical organization serves as the foundational core of high-quality academic English presentations. The Content Quality Index and Presentation Effectiveness Index significantly differentiate between A and B grades. Meanwhile, the Oral Delivery Index and Overall Coordination Index positively contribute to total score improvement. Further grade-specific analysis shows that Grade A presentations are strongly associated with innovation and interdisciplinary integration capabilities; Grade B presentations rely on logical rigor and data analysis quality; and Grade C presentations are primarily influenced by presentation skills and audience engagement. From a data-driven perspective, this study proposes structured and tiered instructional recommendations for developing students’ English academic presentation skills: building on logical training as the foundation, implementing differentiated teaching as the pathway, and aiming for overall coordination, to enhance students’ academic expression and comprehensive pragmatic competences. Flipped classroom Gradient boosting decision tree Random forest Student ability cultivation Data analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 May, 2026 Reviewers invited by journal 01 Apr, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 22 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. We do this by developing innovative software and high quality services for the global research community. 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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-9189742","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616348611,"identity":"f3842403-cea7-469e-93a2-10fea6fc2cf5","order_by":0,"name":"li liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYFACHoYDH37YyPEzMx9+QKwWxoMze9KMJdvZ0gyI1cJ8mIftcOKG8zwKEkRpkI/IPXCAh+dw4ubDPAwGDDU20QS1GJ45l3BAwiLdeNth3gMPGI6l5TYQ1NLeY3DAgMdadtthvgQDxobDRGhp5jE4kMDGzLgZyJAgSos8O9CWA2zOihuYidViwHPG4GAjMJAlDgMDOYEYv8jPyDH+/AcUlf2HDz/4UGNDhC0HkHkJhJSDbSFo6CgYBaNgFIwCAN14QmqtrfsQAAAAAElFTkSuQmCC","orcid":"","institution":"Xuzhou University of Technology","correspondingAuthor":true,"prefix":"","firstName":"li","middleName":"","lastName":"liu","suffix":""}],"badges":[],"createdAt":"2026-03-22 07:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9189742/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9189742/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106403329,"identity":"c92d554b-d2d5-41ee-986a-8787de34c83a","added_by":"auto","created_at":"2026-04-08 09:14:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1079106,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9189742/v1_covered_1a7c5ad8-27d8-4437-8497-869139d39ba4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Machine Learning Approach to Identifying Core Competencies Fostered in College English Flipped Classrooms","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":"asian-pacific-journal-of-second-and-foreign-language-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jsfl","sideBox":"Learn more about [Asian-Pacific Journal of Second and Foreign Language Education](http://sfleducation.springeropen.com)","snPcode":"40862","submissionUrl":"https://submission.nature.com/new-submission/40862/3","title":"Asian-Pacific Journal of Second and Foreign Language Education","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Flipped classroom, Gradient boosting decision tree, Random forest, Student ability cultivation, Data analysis","lastPublishedDoi":"10.21203/rs.3.rs-9189742/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9189742/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on the context of college English flipped classrooms, this study systematically analyzes the key features influencing presentation evaluations and their educational implications by utilizing student peer-assessment data from multi-topic, multi-group classroom presentations. Gradient Boosted Decision Trees (GBDT) classification models and Random Forest regression models were employed for this purpose. In the experimental design, each presentation topic was independently completed by three student groups. Peer assessment categorized the presentations into three grades: A (excellent), B (good), and C (qualified), employing an upper-limit scoring mechanism (A\u0026thinsp;\u0026le;\u0026thinsp;100, B\u0026thinsp;\u0026le;\u0026thinsp;90, C\u0026thinsp;\u0026le;\u0026thinsp;80). Through a combination of horizontal comparison (classification within the same topic) and vertical comparison (score prediction within the same level), the study identifies key influencing features across different levels and score ranges. The findings reveal that the Logical Coherence Index consistently demonstrates the highest feature importance in both models, suggesting that logical organization serves as the foundational core of high-quality academic English presentations. 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