Beyond Individual Attributes: Network Structure and Learning Perception as Drivers of Collective Intelligence at Scale

Beyond Individual Attributes: Network Structure and Learning Perception as Drivers of Collective Intelligence at Scale | 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 Beyond Individual Attributes: Network Structure and Learning Perception as Drivers of Collective Intelligence at Scale Xiaojie Niu, Jingjing Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9174225/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 Collective intelligence, the emergent capacity of groups to perform cognitive tasks beyond the abilities of individual members, has been extensively studied in small-group laboratory settings but remains underexplored in large-scale naturalistic educational environments. This study investigates factors influencing collective intelligence among 5,347 learners across 25 class-level groups within a blended university course. Using a crowdsourced question bank activity as the collective task, we operationalized collective intelligence through four behavioral dimensions aligned with McGrath's group task circumplex theory. These dimensions included generation, negotiation, execution, and assessment. A two-stage structural equation modeling approach was first employed to validate the collective intelligence measurement model, and standardized composite scores were computed using empirically derived factor loadings. Five categories of antecedents were then examined through correlation analyses and structural equation modeling, including demographic composition, social interaction network characteristics, course organizational features, learning perception, and social presence. Results indicated that social interaction network characteristics were the strongest predictor of collective intelligence with a standardized path coefficient of 0.564, followed by learning perception with 0.282. Social presence exhibited a non-significant negative association with collective intelligence, suggesting that heightened interpersonal awareness may introduce cognitive interference in task-oriented collective activities. Demographic composition and course organizational arrangements did not significantly predict collective intelligence after accounting for other predictors. These findings highlight the primacy of group-level interaction processes over individual-level attributes in shaping collective intelligence and offer practical guidance for designing blended learning environments that support emergent collective cognition at scale. collective intelligence social interaction networks learning perception blended learning structural equation modeling crowdsourcing in education 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. 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-9174225","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619962023,"identity":"26e540e3-84b5-404d-9885-60539730915d","order_by":0,"name":"Xiaojie Niu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACZiCWMAAzGB8wsIGEEojXwmxAnBYkwCZBlBaD48wPH1gU2DCYs/OYVf4oO8zAz55jwPBzB24tks1sxgYSBmkMls08Zrd5zh1mkOx5Y8DYewa3Fn5mBjMJCYPDDAaHgVoY24CMGzkGzIxteNzPzP4NqOU/WEvhT6AWe0Ja+Jl5QLYcAGth4AXZIkFAi2QzTzHQL8k8BofZiqV5zqXzSJx5VnCwF48Wg/PHNz6W+GMnZ3D+8MaPP8qs5fjbkzc++IlHCwgwSzAw8MA4YMYB/BoYGBg/EFIxCkbBKBgFIxsAAET2QiHrPp+KAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Xiaojie","middleName":"","lastName":"Niu","suffix":""},{"id":619962024,"identity":"780659fc-15d3-4127-86e6-79e0fae7636a","order_by":1,"name":"Jingjing Zhang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-20 03:09:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9174225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9174225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107060884,"identity":"ed10a1d9-b2d3-4f00-b8fe-d5a3257a01ff","added_by":"auto","created_at":"2026-04-16 10:11:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4898060,"visible":true,"origin":"","legend":"","description":"","filename":"CIManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9174225/v1_covered_f7412c28-86db-4e36-b463-199ab75a8881.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond Individual Attributes: Network Structure and Learning Perception as Drivers of Collective Intelligence at Scale","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":"collective intelligence, social interaction networks, learning perception, blended learning, structural equation modeling, crowdsourcing in education","lastPublishedDoi":"10.21203/rs.3.rs-9174225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9174225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Collective intelligence, the emergent capacity of groups to perform cognitive tasks beyond the abilities of individual members, has been extensively studied in small-group laboratory settings but remains underexplored in large-scale naturalistic educational environments. 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