Enhancing Academic Performance Evaluation of Research Topic Selection through Explainable Artificial Intelligence and Educational Data Analytics  

Enhancing Academic Performance Evaluation of Research Topic Selection through Explainable Artificial Intelligence and Educational Data Analytics | 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 Enhancing Academic Performance Evaluation of Research Topic Selection through Explainable Artificial Intelligence and Educational Data Analytics Essa E. Almazroei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8419224/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 This research work presents a framework that leverages Explainable Artificial Intelligence (XAI) and Educational Data Analytics (EDA) to evaluate student performance in the various departments. A dataset comprising student-authored research articles, including titles, abstracts, citations, and online engagement metrics, was analysed using Logistic Regression (LR) for topic classification. The model achieved an overall accuracy of 93% with 7% miss rate, with detailed class metrics as follows: class 0 (Physics) having precision 1.00, recall 0.22, f1-score 0.36, support 37, class 1 (Engineering) having 0.90, 0.89, 0.89, 200, class 2 (Computational Science) having 0.93, 1.00, 0.96, 591, and class 3 (Pure Mathematics) having 0.00, 0.00, 0.00, 9. Macro-average precision, recall, and f1-score were 0.71, 0.52, and 0.55, while weighted averages were 0.92, 0.93, and 0.91. To make it more interpretable, SHAP and LIME analyses were performed, which revealed the textual features and tendencies of scholarly activity that produce the greatest impact on the results of classification. The results indicate that Physics students are always involved in submitting and publishing manuscripts, and this reflects on their academic performance throughout the academic calendar. Such an approach provides a great predictive power and practical information, providing the educator with a data-based tool that is transparent and allows tracking and improving student academic achievement. Physical sciences/Engineering Physical sciences/Mathematics and computing Academic Performance Research Topic Selection Explainable AI Education Data Analytics 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. 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-8419224","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":575737362,"identity":"43ac0d5d-954e-4812-8b53-23d44ee93c0c","order_by":0,"name":"Essa E. 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","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":"Academic Performance, Research Topic Selection, Explainable AI, Education Data 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A dataset comprising student-authored research articles, including titles, abstracts, citations, and online engagement metrics, was analysed using Logistic Regression (LR) for topic classification. The model achieved an overall accuracy of 93% with 7% miss rate, with detailed class metrics as follows: class 0 (Physics) having precision 1.00, recall 0.22, f1-score 0.36, support 37, class 1 (Engineering) having 0.90, 0.89, 0.89, 200, class 2 (Computational Science) having 0.93, 1.00, 0.96, 591, and class 3 (Pure Mathematics) having 0.00, 0.00, 0.00, 9. Macro-average precision, recall, and f1-score were 0.71, 0.52, and 0.55, while weighted averages were 0.92, 0.93, and 0.91. To make it more interpretable, SHAP and LIME analyses were performed, which revealed the textual features and tendencies of scholarly activity that produce the greatest impact on the results of classification. The results indicate that Physics students are always involved in submitting and publishing manuscripts, and this reflects on their academic performance throughout the academic calendar. Such an approach provides a great predictive power and practical information, providing the educator with a data-based tool that is transparent and allows tracking and improving student academic achievement.\u003c/p\u003e","manuscriptTitle":"Enhancing Academic Performance Evaluation of Research Topic Selection through Explainable Artificial Intelligence and Educational Data Analytics ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:25:48","doi":"10.21203/rs.3.rs-8419224/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":"35d4ed42-6a12-4202-8762-bb27b0423f8a","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61263273,"name":"Physical sciences/Engineering"},{"id":61263274,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-29T19:51:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:25:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8419224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8419224","identity":"rs-8419224","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}