Application of Deep Learning Algorithms in Improving Teaching Quality Assessment of Applied Psychology Micro-Major Programs

Application of Deep Learning Algorithms in Improving Teaching Quality Assessment of Applied Psychology Micro-Major Programs | 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 Deep Learning Algorithms in Improving Teaching Quality Assessment of Applied Psychology Micro-Major Programs Yang Meng, Shuzhu Tang, Jun Niu, Yizhuo Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8109845/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Applied psychology micro-major programs, as emerging interdisciplinary education models, face challenges in teaching quality assessment including complex evaluation dimensions and heterogeneous learning behavior data. Addressing the problem that existing assessment models struggle to capture deep temporal dependencies and key influencing factors in students' learning processes, this study constructs a deep learning assessment framework integrating Bidirectional Long Short-Term Memory networks and multi-head attention mechanisms. The framework integrates multi-source data including learning behavior sequences and psychological assessment data, utilizes BiLSTM networks to extract bidirectional temporal features, and combines multi-head attention mechanisms to achieve adaptive weight allocation and feature fusion of assessment elements, establishing a high-precision teaching quality prediction model. Experimental verification based on teaching data from 180 students across two consecutive semesters in an applied psychology micro-major program at a university shows that the proposed model achieves an assessment accuracy of 97.23%, an improvement of 11.6 percentage points over traditional machine learning methods and 4.8 percentage points over single deep learning models, with an inference time of 1.8 milliseconds. The model can effectively identify core factors affecting teaching quality and provide interpretable assessment evidence, offering precise data support for applied psychology micro-major teaching reform. Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Applied psychology micro-major Teaching quality assessment Bidirectional Long Short-Term Memory network Multi-head attention mechanism Temporal feature extraction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 07 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Editor invited by journal 21 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 20 Nov, 2025 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-8109845","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":603865229,"identity":"78c9f3c6-9c92-4a89-8115-7502f508181b","order_by":0,"name":"Yang Meng","email":"","orcid":"","institution":"Changchun Humanities and Sciences College","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Meng","suffix":""},{"id":603865230,"identity":"c7215509-0391-4308-a677-b581f70b9b5d","order_by":1,"name":"Shuzhu Tang","email":"","orcid":"","institution":"Changchun Humanities and Sciences College","correspondingAuthor":false,"prefix":"","firstName":"Shuzhu","middleName":"","lastName":"Tang","suffix":""},{"id":603865231,"identity":"68840912-9848-4786-b352-0432c2fc62ef","order_by":2,"name":"Jun Niu","email":"","orcid":"","institution":"Changchun Humanities and Sciences College","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Niu","suffix":""},{"id":603865232,"identity":"fd62fac2-8412-4064-b8e8-5006670e839f","order_by":3,"name":"Yizhuo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3PsQrCMBCA4QuBdAnU8aQv0RKIDgVfJSLUxUG3Thoo6FJwrW8h+AKFoi55gI4FQdeOHVXcHNqOgvmn47hvOACb7Tcjt6ZB6r7HCsDvQ6gg6dgZaiBa9STMAxa7ft6X+OW88pYcPXFNR5WKQwFOcT62E6VENkYhjQm0MpEEHkVlG5GlyhXnOJPlItDTbRECctlBpjrnDDenrD+ZkeRFqI8fIjvJxNwpOaRI0VxW2esXwbp+Ge7mj6Zu1tTdJce6jsNg7xSXVgID9bVgrefv3LzzxGaz2f69J0LISwT2+XsxAAAAAElFTkSuQmCC","orcid":"","institution":"Changchun Humanities and Sciences College","correspondingAuthor":true,"prefix":"","firstName":"Yizhuo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-11-14 02:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8109845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8109845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780531,"identity":"f59eacd6-eec7-4e2c-9cb0-5b7e22692842","added_by":"auto","created_at":"2026-03-17 07:53:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":828037,"visible":true,"origin":"","legend":"","description":"","filename":"1107.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8109845/v1_covered_18139a5e-9cfe-43e6-b885-56bf59e0435c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of Deep Learning Algorithms in Improving Teaching Quality Assessment of Applied Psychology Micro-Major Programs","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Applied psychology micro-major, Teaching quality assessment, Bidirectional Long Short-Term Memory network, Multi-head attention mechanism, Temporal feature extraction","lastPublishedDoi":"10.21203/rs.3.rs-8109845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8109845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eApplied psychology micro-major programs, as emerging interdisciplinary education models, face challenges in teaching quality assessment including complex evaluation dimensions and heterogeneous learning behavior data. 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