Multi-strategy Enhanced Artificial Rabbits Optimization for Prediction of Grades in Tourism Service Communication Courses

Multi-strategy Enhanced Artificial Rabbits Optimization for Prediction of Grades in Tourism Service Communication Courses | 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 Multi-strategy Enhanced Artificial Rabbits Optimization for Prediction of Grades in Tourism Service Communication Courses Zhuyin Jia, Xiaodan Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4590300/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Predicting students' grades through their classroom behavior is a longstanding concern in education. Recently, artificial intelligence has shown remarkable potential in this area. In this paper, the Artificial Rabbits Optimization Algorithm (ARO) is chosen to enhance the predictor's capabilities. ARO is a recently proposed and popular metaheuristic algorithm known for its simple and straightforward structure. However, like other metaheuristic algorithms, ARO often falls into local optima and, as iterations increase, the convergence speed slows down, leading to lower convergence accuracy. To address this issue, we introduce a Multi-Strategy Enhanced Artificial Rabbits Optimization Algorithm (MEARO). In MEARO, we first employ a Nonlinear exploration and exploitation transition factor (NL) to improve the balance between exploration and exploitation in ARO. we employ a Stochastic Dynamic Centroid Backward Learning approach (SOBL) to improve both the quality and diversity of the population. This ensures a broader optimization of the search area and boosts the chances of locating the global optimum. Lastly, we incorporate a Dynamic Changing Step Length Development strategy to enhance the randomness and development capability of ARO. To confirm the efficiency of MEARO, we compared its performance with eight other sophisticated algorithms using the CEC2017 benchmark. Our findings indicate that MEARO outperforms the other algorithms we tested. Furthermore, we optimized two critical parameters of the Kernel Extreme Learning Machine (KELM) using the MEARO algorithm, boosting its classification performance. Moreover, experimental results on the collected student performance dataset show that the KELM model optimized by MEARO outperforms other benchmarked models in terms of various metrics. Finally, we also find that interest in the course, frequency of classroom discussion, and access to extra knowledge and information related to the course are significant factors affecting performance. Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Artificial Rabbits Optimization Intelligent education Reinforced exploitation strategy Stochastic centroid dynamic opposition-based learning Kernel extreme learning machine Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Sep, 2024 Reviews received at journal 17 Sep, 2024 Reviews received at journal 16 Sep, 2024 Reviewers agreed at journal 06 Sep, 2024 Reviewers agreed at journal 06 Sep, 2024 Reviewers invited by journal 06 Sep, 2024 Editor assigned by journal 06 Sep, 2024 Editor invited by journal 04 Jul, 2024 Submission checks completed at journal 02 Jul, 2024 First submitted to journal 16 Jun, 2024 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. 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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-4590300","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":330594802,"identity":"e06542bd-ee3e-4347-9395-86f4f76d2e2d","order_by":0,"name":"Zhuyin Jia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACxgYGBgkGBmY5NvbmA6RpMebjOZZAvE0gLYnzJHIUiFPO3H/G8MbHNuv0NoYcBoYfFduIcdgZY8sZZ9Jz2xjOHmDsOXObCC2NPWbSPBWHc9sY+xKYGduI0dLMYyb9x+BwOhszjwGRWtqAWhgqDiewsRGtpYet2LLnTLphGw9bwkGi/GLYf3jjjZ9t1vLy8x8ffPCjghgtDRwGcM4BwuqBQJ6B/QFRCkfBKBgFo2AEAwCURzjeTk1KjwAAAABJRU5ErkJggg==","orcid":"","institution":"Wenzhou Central Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhuyin","middleName":"","lastName":"Jia","suffix":""},{"id":330594803,"identity":"f76addb7-8680-4cd4-93a8-10ec9fb56e9d","order_by":1,"name":"Xiaodan Qu","email":"","orcid":"","institution":"Zhejiang Industry \u0026 Trade Vocational College","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaodan","middleName":"","lastName":"Qu","suffix":""}],"badges":[],"createdAt":"2024-06-16 15:33:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4590300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4590300/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-84931-x","type":"published","date":"2025-07-04T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86179867,"identity":"293c394d-31ea-4fbf-86b2-6812a955fc21","added_by":"auto","created_at":"2025-07-07 16:20:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1352846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript20240702.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4590300/v1_covered_60c6ed58-0229-4e3a-88f8-0cf9c2725134.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-strategy Enhanced Artificial Rabbits Optimization for Prediction of Grades in Tourism Service Communication Courses","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Artificial Rabbits Optimization, Intelligent education, Reinforced exploitation strategy, Stochastic centroid dynamic opposition-based learning, Kernel extreme learning machine","lastPublishedDoi":"10.21203/rs.3.rs-4590300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4590300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicting students' grades through their classroom behavior is a longstanding concern in education. Recently, artificial intelligence has shown remarkable potential in this area. In this paper, the Artificial Rabbits Optimization Algorithm (ARO) is chosen to enhance the predictor's capabilities. ARO is a recently proposed and popular metaheuristic algorithm known for its simple and straightforward structure. However, like other metaheuristic algorithms, ARO often falls into local optima and, as iterations increase, the convergence speed slows down, leading to lower convergence accuracy. To address this issue, we introduce a Multi-Strategy Enhanced Artificial Rabbits Optimization Algorithm (MEARO). In MEARO, we first employ a Nonlinear exploration and exploitation transition factor (NL) to improve the balance between exploration and exploitation in ARO. we employ a Stochastic Dynamic Centroid Backward Learning approach (SOBL) to improve both the quality and diversity of the population. This ensures a broader optimization of the search area and boosts the chances of locating the global optimum. Lastly, we incorporate a Dynamic Changing Step Length Development strategy to enhance the randomness and development capability of ARO. To confirm the efficiency of MEARO, we compared its performance with eight other sophisticated algorithms using the CEC2017 benchmark. Our findings indicate that MEARO outperforms the other algorithms we tested. Furthermore, we optimized two critical parameters of the Kernel Extreme Learning Machine (KELM) using the MEARO algorithm, boosting its classification performance. Moreover, experimental results on the collected student performance dataset show that the KELM model optimized by MEARO outperforms other benchmarked models in terms of various metrics. Finally, we also find that interest in the course, frequency of classroom discussion, and access to extra knowledge and information related to the course are significant factors affecting performance.\u003c/p\u003e","manuscriptTitle":"Multi-strategy Enhanced Artificial Rabbits Optimization for Prediction of Grades in Tourism Service Communication Courses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 04:44:54","doi":"10.21203/rs.3.rs-4590300/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-18T08:36:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T21:35:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-16T09:42:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1306856300304455791055689892304209395","date":"2024-09-06T22:28:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231012595275044231874416003674461131780","date":"2024-09-06T22:26:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-06T22:22:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-06T22:17:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-04T06:01:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-03T03:52:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-16T15:32:19+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"d712299c-97c7-4f46-87d8-60c22b851e5f","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34996073,"name":"Physical sciences/Mathematics and computing"},{"id":34996074,"name":"Physical sciences/Mathematics and computing/Applied mathematics"},{"id":34996075,"name":"Physical sciences/Mathematics and computing/Computational science"},{"id":34996076,"name":"Physical sciences/Mathematics and computing/Computer science"},{"id":34996077,"name":"Physical sciences/Mathematics and computing/Information technology"},{"id":34996078,"name":"Physical sciences/Mathematics and computing/Scientific data"}],"tags":[],"updatedAt":"2025-07-07T16:12:00+00:00","versionOfRecord":{"articleIdentity":"rs-4590300","link":"https://doi.org/10.1038/s41598-024-84931-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-04 15:57:13","publishedOnDateReadable":"July 4th, 2025"},"versionCreatedAt":"2024-07-25 04:44:54","video":"","vorDoi":"10.1038/s41598-024-84931-x","vorDoiUrl":"https://doi.org/10.1038/s41598-024-84931-x","workflowStages":[]},"version":"v1","identity":"rs-4590300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4590300","identity":"rs-4590300","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}