Fuzzy Logic Approach to Cold-Start Challenges in Deaf and Hard of Hearing Recommender Systems

Authors

  • Anisha Poly Department of Computer Science, CHRIST (Deemed to be University), India
  • P. K. Nizar Banu Department of Computer Science, CHRIST (Deemed to be University), India
  • Najla Althuniyan College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia
  • Ahmad Taher Azar College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia | Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia
  • Nashwa Ahmad Kamal Faculty of Engineering, Cairo University, Giza, Egypt
Volume: 15 | Issue: 3 | Pages: 23449-23460 | June 2025 | https://doi.org/10.48084/etasr.10825

Received: 6 March 2025 | Revised: 9 April 2025 and 14 April 2025 | Accepted: 19 April 2025 | Online: 4 June 2025


Corresponding author: Ahmad Taher Azar

Abstract

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.

Keywords:

DHH learner model, recommendation systems, cold-start, learning style, fuzzy clustering, adaptive e-learning

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[1]
A. Poly, P. K. N. Banu, N. Althuniyan, A. T. Azar, and N. A. Kamal, “Fuzzy Logic Approach to Cold-Start Challenges in Deaf and Hard of Hearing Recommender Systems”, Eng. Technol. Appl. Sci. Res., vol. 15, no. 3, pp. 23449–23460, Jun. 2025.

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Copyright (c) 2025 Anisha Poly, P. K. Nizar Banu, Najla Althuniyan, Ahmad Taher Azar, Nashwa Ahmad Kamal

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