Application of Support Vector Machine in CoreCompetency Index Analysis of Higher VocationalCulinary Education

Application of Support Vector Machine in CoreCompetency Index Analysis of Higher VocationalCulinary 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 Article Application of Support Vector Machine in CoreCompetency Index Analysis of Higher VocationalCulinary Education Xu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7599337/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 In the realm of higher vocational education, accurately assessing students’ core competencies is pivotal for curriculum development and pedagogical effectiveness. Traditional evaluation methods often rely on subjective judgments and standardizedtesting, which may not comprehensively capture the multifaceted abilities required in specialized fields. This study introduces aninnovative approach by integrating Support Vector Machine (SVM) algorithms to analyze and predict core competency indiceswithin higher vocational culinary education. Leveraging the Gastronomic Learning Architecture (GLA), a structured frameworkthat encapsulates both theoretical knowledge and practical skills, we systematically quantify various competency dimensions.The SVM model is trained on a dataset comprising performance metrics, contextual variables, and evaluative feedback, enablingit to discern complex patterns indicative of student proficiency. Experimental results demonstrate that our SVM-based analysisachieves a predictive accuracy surpassing traditional assessment techniques, offering a more nuanced and objective evaluationof student competencies. This methodological advancement not only enhances the precision of competency assessmentsbut also aligns with the broader application of machine learning in educational contexts, as emphasized in the Applicationsof Machine Learning to Food Science research topic. By bridging the gap between computational science and vocationaleducation, this approach paves the way for data-driven strategies in curriculum optimization and personalized learning pathways. Physical sciences/Engineering Physical sciences/Mathematics and computing Support Vector Machine Core Competency Analysis Higher Vocational Education Gastronomic Learning Architecture Machine Learning Applications 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. 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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-7599337","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":525738086,"identity":"19bff5d7-1d5b-4ea7-8735-d8cf0fbaad33","order_by":0,"name":"Xu 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