Predicting Student Satisfaction in Career Choices Using Machine Learning: A Case Study

Predicting Student Satisfaction in Career Choices Using Machine Learning: A Case Study | 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 Predicting Student Satisfaction in Career Choices Using Machine Learning: A Case Study Mohamed Oubraime, Ahmed M’Hamdi, Abderrahim Sabour This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5841034/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Discover Education → Version 1 posted You are reading this latest preprint version Abstract Student satisfaction is crucial for making successful educational and career choices. This study explores how artificial intelligence, specifically the Support Vector Machine (SVM) model, can help predict student satisfaction with their university choices by considering socio-economic, environmental, and cultural factors. We analyzed data from 125 students at a technology university through three main steps: first, we assessed the reliability of a satisfaction measurement tool; second, we identified key factors influencing satisfaction through statistical analysis; and third, we applied an SVM model to predict satisfaction, using 70% of the data for training and 30% for testing. Our results revealed significant positive correlations between student background, social support, prior knowledge, and their preference for group work with their overall satisfaction. The SVM model was able to predict satisfaction with an accuracy of 89%. These findings highlight the value of AI in educational guidance and suggest that the SVM model can effectively predict student satisfaction, as well as other factors critical to academic and career success. School Counseling Artificial Intelligence and Machine Learning Educational Psychology School and Career Guidance Career Satisfaction Machine Learning (ML) Support Vector Machine (SVM) Full Text Additional Declarations The authors declare no competing interests. All participants involved in this study provided informed consent to participate. The study was conducted in accordance with the ethical standards of the Declaration of Helsinki (1964) and its later amendments. Supplementary Files declarations.pdf Ethical Declarations Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Discover Education → 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-5841034","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402953335,"identity":"34712700-6aff-4f46-a34b-e146b022dd3d","order_by":0,"name":"Mohamed Oubraime","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0000-0571-1723","institution":"Ibn Zohr Univeersity","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Oubraime","suffix":""},{"id":402953336,"identity":"2d753f8e-4656-4183-868f-db9b2f02bea9","order_by":1,"name":"Ahmed M’Hamdi","email":"","orcid":"","institution":"Higher School of Education and Training, Ibn Zohr University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"M’Hamdi","suffix":""},{"id":402953337,"identity":"5dd2efe3-e3d9-443d-bb67-84049e710cee","order_by":2,"name":"Abderrahim Sabour","email":"","orcid":"https://orcid.org/0000-0002-8136-779X","institution":"High School of Technology, Ibn Zohr University,","correspondingAuthor":false,"prefix":"","firstName":"Abderrahim","middleName":"","lastName":"Sabour","suffix":""}],"badges":[],"createdAt":"2025-01-16 10:31:35","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5841034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5841034/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s44217-026-01337-9","type":"published","date":"2026-03-14T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104818987,"identity":"40b7b9a8-efd6-4967-bb0b-073235fa3fe1","added_by":"auto","created_at":"2026-03-17 14:01:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":544620,"visible":true,"origin":"","legend":"","description":"","filename":"MainDocumentJ.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5841034/v1_covered_b601fd89-3ac9-4d01-9b95-62d93ccea613.pdf"},{"id":74011475,"identity":"9f7a1591-9fab-4812-80e6-5399deacc9b6","added_by":"auto","created_at":"2025-01-17 02:44:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":284868,"visible":true,"origin":"","legend":"\u003cp\u003eEthical Declarations\u003c/p\u003e","description":"","filename":"declarations.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5841034/v1/6b5e7da9559ebe0a8eefbc61.pdf"}],"financialInterests":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eAll participants involved in this study provided informed consent to participate. The study was conducted in accordance with the ethical standards of the Declaration of Helsinki (1964) and its later amendments.\u003c/p\u003e","formattedTitle":"\u003cp\u003ePredicting Student Satisfaction in Career Choices Using Machine Learning: A Case Study\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Université Ibn Zohr","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":"School and Career Guidance, Career Satisfaction, Machine Learning (ML), Support Vector Machine (SVM)","lastPublishedDoi":"10.21203/rs.3.rs-5841034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5841034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudent satisfaction is crucial for making successful educational and career choices. This study explores how artificial intelligence, specifically the Support Vector Machine (SVM) model, can help predict student satisfaction with their university choices by considering socio-economic, environmental, and cultural factors. We analyzed data from 125 students at a technology university through three main steps: first, we assessed the reliability of a satisfaction measurement tool; second, we identified key factors influencing satisfaction through statistical analysis; and third, we applied an SVM model to predict satisfaction, using 70% of the data for training and 30% for testing. Our results revealed significant positive correlations between student background, social support, prior knowledge, and their preference for group work with their overall satisfaction. The SVM model was able to predict satisfaction with an accuracy of 89%. These findings highlight the value of AI in educational guidance and suggest that the SVM model can effectively predict student satisfaction, as well as other factors critical to academic and career success.\u003c/p\u003e","manuscriptTitle":"Predicting Student Satisfaction in Career Choices Using Machine Learning: A Case Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-17 02:44:19","doi":"10.21203/rs.3.rs-5841034/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"1da01ea0-d3b1-42bc-9738-949dee9b19da","owner":[],"postedDate":"January 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42960462,"name":"School Counseling"},{"id":42960463,"name":"Artificial Intelligence and Machine Learning"},{"id":42960464,"name":"Educational Psychology"}],"tags":[],"updatedAt":"2026-03-17T14:01:22+00:00","versionOfRecord":{"articleIdentity":"rs-5841034","link":"https://doi.org/10.1007/s44217-026-01337-9","journal":{"identity":"discover-education","isVorOnly":false,"title":"Discover Education"},"publishedOn":"2026-03-14 00:00:00","publishedOnDateReadable":"March 14th, 2026"},"versionCreatedAt":"2025-01-17 02:44:19","video":"","vorDoi":"10.1007/s44217-026-01337-9","vorDoiUrl":"https://doi.org/10.1007/s44217-026-01337-9","workflowStages":[]},"version":"v1","identity":"rs-5841034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5841034","identity":"rs-5841034","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}