Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Poly, Anisha; Nizar Banu, P.K.; Althuniyan, Najla; Azar, Ahmad Taher; Kamal, Nashwa Ahmad Title A name given to the resource Fuzzy Logic Approach to Cold-Start Challenges in Deaf and Hard of Hearing Recommender Systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Engineering, Technology and Applied Science Research;Volume;15;Issue;3;pp.23449-23460 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.48084/etasr.10825" target="_blank" rel="noreferrer noopener">https://doi.org/10.48084/etasr.10825</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007917815?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007917815?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Poly A., Department of Computer Science, CHRIST (Deemed to be University), India; Nizar Banu P.K., Department of Computer Science, CHRIST (Deemed to be University), India; Althuniyan N., College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia, Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia; Azar A.T., College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia, Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia; Kamal N.A., Faculty of Engineering, Cairo University, Giza, Egypt Description An account of the resource An adaptive e-learning environment faces significant challenges in offering personalized learning resources for Deaf and Hard-Hearing (DHH) learners. These learners exhibit diverse preferences in learning and communication, influenced by their characteristics related to deafness, highlighting the need for personalized educational content. A well-defined learning model is essential to map the characteristics of learners to suitable learning resources, enabling effective recommendations within an e-learning system. This study explores the development of a comprehensive DHH learner model, focusing on the presence of multiple learning preferences based on the VARK (Visual, Aural, Read/Write, and Kinesthetic) learning style model and the effectiveness of fuzzy clustering in capturing the diverse but overlapping preferences. Fuzzy-C-Means (FCM) successfully identified six different but overlapping clusters, indicating that most learners exhibit multimodal learning preferences rather than relying solely on a visual learning style. Cluster centroid analysis reveals that the visual learning style is the most preferred, while aural learning is the least favored among DHH learners. By calculating the overall learning style score based on the fuzzy membership value across all clusters on all four dimensions of VARK, learners' learning style preferences were validated against self-reported data. The evaluation involved a survey of 130 higher secondary DHH students from Kerala, India, yielding promising results (precision: 0.90, recall: 0.84, F1-score: 0.84) on the model's efficiency in identifying the dominant learning style. These findings emphasize the need for adaptive content delivery strategies that integrate text, visual, and interactive elements to enhance the engagement of DHH learners. However, the limited sample size, due to the unavailability of publicly accessible datasets, and the limited number of students in higher secondary education, further highlights the need for accessible and standardized DHH data to advance this research domain. by the authors. Subject The topic of the resource adaptive e-learning; cold-start; DHH learner model; fuzzy clustering; learning style; recommendation systems Publisher An entity responsible for making the resource available Dr D. Pylarinos Relation A related resource ISSN: 22414487; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online