Evaluating the effects of adjunct VR-haptic simulator practice in preclinical restorative training and predicting student performance using machine learning

Evaluating the effects of adjunct VR-haptic simulator practice in preclinical restorative training and predicting student performance using machine learning | 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 Evaluating the effects of adjunct VR-haptic simulator practice in preclinical restorative training and predicting student performance using machine learning Bilge Ersöz, Ali Degirmenci, Numan Aydin, Elif Aybala Oktay, Serpil Karaoğlanoğlu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8478166/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2026 Read the published version in BMC Medical Education → Version 1 posted 10 You are reading this latest preprint version Abstract Background Although undergraduate preclinical training is commonly carried out on phantom mannequins, virtual reality (VR) devices have started to be used as supplementary tools. This study aimed to investigate the impact of adjunct VR-haptic simulator practice on undergraduate dental education in restorative practice and to explore whether machine-learning models can predict faculty-assigned letter grades. Methods A total of 100 undergraduate students participated in the study, with 50 receiving 8 hours of phantom training only, while the other 50 received 8 hours of phantom training plus an additional 4 hours of VR-haptic simulator training. All students prepared a Class I cavity, which was graded independently by two experienced lecturers. (100-point scale). Inter-rater agreement was quantified using the intraclass correlation coefficient (ICC; two-way random-effects, absolute agreement, average measures; ICC(2,2) = 0.85, (95% CI: 0.72-0.86)). The effect of dental simulator use on cavity performance was analyzed. Twenty-six features were extracted to predict students' letter grades using four different machine learning methods (Naive Bayes, decision tree, random forest (RF), and support vector machines). In addition, the result of the best-performing method was explained using SHAP to describe feature-prediction associations (not causality). Results The best scores were obtained with the RF (Accuracy=0.8273, Macro-F1-score=0.8205, Macro average precision=0.8304, Macro average recall=0.8309). The SHAP explainability results indicate that the most deterministic features are the occlusal outline, simulator usage, and point angle. These findings suggest that simulator usage significantly impacts student performance, contributing to improved grades. Conclusion In preclinical restorative training, adjunct VR-haptic simulator exposure was found to be associated with faculty-assigned performance grades on a Class I cavity preparation task. Machine-learning models can be employed to predict the letter grades assigned by faculty members. cavity preparation dental education explainable artificial intelligence machine learning virtual reality Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 05 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviews received at journal 23 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 16 Jan, 2026 Editor assigned by journal 15 Jan, 2026 Editor invited by journal 13 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 12 Jan, 2026 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-8478166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575540117,"identity":"d450b560-0d12-4f0b-8bf7-de9df7f81f99","order_by":0,"name":"Bilge 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cavity preparation, dental education, explainable artificial intelligence, machine learning, virtual reality","lastPublishedDoi":"10.21203/rs.3.rs-8478166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8478166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAlthough undergraduate preclinical training is commonly carried out on phantom mannequins, virtual reality (VR) devices have started to be used as supplementary tools. This study aimed to investigate the impact of adjunct VR-haptic simulator practice on undergraduate dental education in restorative practice and to explore whether machine-learning models can predict faculty-assigned letter grades. \u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 100 undergraduate students participated in the study, with 50 receiving 8 hours of phantom training only, while the other 50 received 8 hours of phantom training plus an additional 4 hours of VR-haptic simulator training. All students prepared a Class I cavity, which was graded independently by two experienced lecturers. (100-point scale). Inter-rater agreement was quantified using the intraclass correlation coefficient (ICC; two-way random-effects, absolute agreement, average measures; ICC(2,2) = 0.85, (95% CI: 0.72-0.86)). The effect of dental simulator use on cavity performance was analyzed. Twenty-six features were extracted to predict students' letter grades using four different machine learning methods (Naive Bayes, decision tree, random forest (RF), and support vector machines). In addition, the result of the best-performing method was explained using SHAP to describe feature-prediction associations (not causality). \u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe best scores were obtained with the RF (Accuracy=0.8273, Macro-F1-score=0.8205, Macro average precision=0.8304, Macro average recall=0.8309). The SHAP explainability results indicate that the most deterministic features are the occlusal outline, simulator usage, and point angle. These findings suggest that simulator usage significantly impacts student performance, contributing to improved grades.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn preclinical restorative training, adjunct VR-haptic simulator exposure was found to be associated with faculty-assigned performance grades on a Class I cavity preparation task. Machine-learning models can be employed to predict the letter grades assigned by faculty members.\u003c/p\u003e","manuscriptTitle":"Evaluating the effects of adjunct VR-haptic simulator practice in preclinical restorative training and predicting student performance using machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 02:44:31","doi":"10.21203/rs.3.rs-8478166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-05T07:17:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T22:07:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-23T15:45:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127761623755822622894021581555936309450","date":"2026-02-16T14:40:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240046392096274841225186094006939990725","date":"2026-02-12T15:04:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-16T09:39:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-15T09:20:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-13T06:29:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-12T19:52:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-01-12T19:50:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"770ac7db-c662-496d-9d3a-41cbc18c831a","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:33:10+00:00","versionOfRecord":{"articleIdentity":"rs-8478166","link":"https://doi.org/10.1186/s12909-026-09072-3","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2026-03-27 16:08:53","publishedOnDateReadable":"March 27th, 2026"},"versionCreatedAt":"2026-01-20 02:44:31","video":"","vorDoi":"10.1186/s12909-026-09072-3","vorDoiUrl":"https://doi.org/10.1186/s12909-026-09072-3","workflowStages":[]},"version":"v1","identity":"rs-8478166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8478166","identity":"rs-8478166","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}