Evaluating remote education learning tools: a hybrid decision-support model using rough approximate operators of Fermatean fuzzy rough soft sets

Evaluating remote education learning tools: a hybrid decision-support model using rough approximate operators of Fermatean fuzzy rough soft sets | 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 remote education learning tools: a hybrid decision-support model using rough approximate operators of Fermatean fuzzy rough soft sets Muhammad Abdullah, Khuram Ali Khan, Atiqe Ur Rahman, Asma Ibrahim Aleidi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8810148/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract The sudden shift to remote learning has made choosing the best digital tools a crucial challenge for educational institutions, especially in developing areas like Punjab, Pakistan, where infrastructural and resource limitations add extra complexity and uncertainty. Existing multi-attribute decision-making (MADM) models often miss capturing the two-dimensional nature of this uncertainty; including membership and non-membership, while also managing parameterization and data roughness. This study aims to fill this gap by proposing a new combined MADM framework called Fermatean fuzzy rough soft sets (FFRSS) for assessing and selecting remote learning tools. A case study from Punjab, Pakistan, is included, evaluating six popular remote learning tools based on thirteen pedagogically and technically relevant criteria. The criteria weights are calculated using a new fermatean fuzzy entropy method, and the ranking of options is done through the fermatean fuzzy rough soft ring sum product method. Applying the FFRSS model to the case study produces a clear ranking of the remote learning tools. Google Classroom ranks as the most suitable platform, followed by Microsoft Teams and Zoom, with custom Learning Management Systems (LMS) and social media platforms ranking lower. Sensitivity analysis confirms the model's robustness against changes in weights, and comparisons with current methods demonstrate its better handling of high uncertainty. This study presents a tri-integrated model, FFRSS, offering a more adaptable approach to managing complexities in decision-making, especially when choosing educational technology. Remote education Software packages Fermatean fuzzy rough sets Approximation operators Multi-attribute decision-making Information processing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 15 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 06 Feb, 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. 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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-8810148","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596214556,"identity":"993e9b02-a362-4e75-ae7f-27cf752b1039","order_by":0,"name":"Muhammad Abdullah","email":"","orcid":"","institution":"University of Sargodha","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Abdullah","suffix":""},{"id":596214557,"identity":"8f0f8146-b131-46a8-bf0c-457eb9210593","order_by":1,"name":"Khuram Ali Khan","email":"","orcid":"","institution":"University of 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