Empowering College Students in Selecting Ideal Advisors: A Text-Based Recommendation Model

Empowering College Students in Selecting Ideal Advisors: A Text-Based Recommendation Model | 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 Empowering College Students in Selecting Ideal Advisors: A Text-Based Recommendation Model Ling Jian, Jiaxin Zhou, Haiping Zhao, Yue Yin, Li Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3833209/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 College students may encounter various challenges in their academic journey, necessitating assistance from their educators. Current research on technology to improve education quality mainly focuses on academic early warning, course recommendation, and employment prediction, overlooking the difficulties students encounter in finding suitable advisors. In view of that, this paper proposes an AdVisor RecommenDation (AVRD) model based on text information. AVRD first employs Chinese BERT and unsupervised SimCSE to train the corpus of teachers’ records. The time decay factor is then introduced as the weight of the text record vector, and the representation vector of teachers is obtained after the weighted average. Finally, the similarity between the vectors of teachers’ and students’ demands is calculated, and the list of teachers is recommended to students according to the designed pooling and matching criteria. The questionnaire data from 170 college students are collected to evaluate the proposed model. Experimental results demonstrate the effectiveness of AVRD, which can help 70% of students find the ideal advisor that matches their needs. In addition, ablation studies show that each part of the proposed AVRD model plays an important role in the model. advisor recommendation recommender system SimCSE text representation questionnaire Full Text 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-3833209","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330115402,"identity":"bab82461-2b66-4dfb-a4e2-30d5b128f5ca","order_by":0,"name":"Ling Jian","email":"","orcid":"","institution":"China University of Petroleum East China","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Jian","suffix":""},{"id":330115403,"identity":"60a30e6a-288e-4bbf-96bf-3db975801df7","order_by":1,"name":"Jiaxin Zhou","email":"","orcid":"","institution":"China University of Petroleum East China","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Zhou","suffix":""},{"id":330115404,"identity":"ce26519d-7828-4f0d-82bd-49e11d5a0336","order_by":2,"name":"Haiping Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACAwbGBoYEEEsCiD/ABInWwjiDOC0wANTCzEOMFnP2w20PHu6oZZCf3fzwsW3bHXkG9uZtEgw1d3BqsexJbDdIPHOcgXHOMWPj3LZnhg08x8okGI49w+2wA4ltEoltxxiYJRLMpHPbDicwSOSYSTA2HMat5fxDiBY2ifRv0pYgLfJvCGi5AbalhoEHaLg0I9gWHkJaHgL90naAR0Iip9iw59wzwzaetGKLhGP4HJb+7OHPtjo5+RnpGx/8KLsjz89+eOONDzW4tQABGxAfhsYIwwEwFxK5+LXUwTgH8KsdBaNgFIyCEQkAc/VUHpjgXvoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8864-2790","institution":"China University of Petroleum East China","correspondingAuthor":true,"prefix":"","firstName":"Haiping","middleName":"","lastName":"Zhao","suffix":""},{"id":330115405,"identity":"0bf6c161-f2db-49f2-87de-ee98c5b4e07d","order_by":3,"name":"Yue Yin","email":"","orcid":"","institution":"New York University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Yin","suffix":""},{"id":330115406,"identity":"0749d7dc-d780-4f55-a684-70f6e0faee24","order_by":4,"name":"Li Li","email":"","orcid":"","institution":"China University of Petroleum East China","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-01-04 01:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3833209/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3833209/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62844078,"identity":"5b6576ba-fcef-45de-8e37-e1335c43730f","added_by":"auto","created_at":"2024-08-20 07:07:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1349632,"visible":true,"origin":"","legend":"","description":"","filename":"SoftComputing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3833209/v1_covered_4acd2d16-53ad-4ed0-889b-a55b42b458a2.pdf"}],"financialInterests":"","formattedTitle":"Empowering College Students in Selecting Ideal Advisors: A Text-Based Recommendation Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"advisor recommendation, recommender system, SimCSE, text representation, questionnaire","lastPublishedDoi":"10.21203/rs.3.rs-3833209/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3833209/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"College students may encounter various challenges in their academic journey, necessitating assistance from their educators. Current research on technology to improve education quality mainly focuses on academic early warning, course recommendation, and employment prediction, overlooking the difficulties students encounter in finding suitable advisors. In view of that, this paper proposes an AdVisor RecommenDation (AVRD) model based on text information. AVRD first employs Chinese BERT and unsupervised SimCSE to train the corpus of teachers’ records. The time decay factor is then introduced as the weight of the text record vector, and the representation vector of teachers is obtained after the weighted average. Finally, the similarity between the vectors of teachers’ and students’ demands is calculated, and the list of teachers is recommended to students according to the designed pooling and matching criteria. The questionnaire data from 170 college students are collected to evaluate the proposed model. 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