E-Learning Model to Identify the Learning Styles of Hearing-Impaired Students

Author:

Luangrungruang Tidarat,Kokaew UrachartORCID

Abstract

Deaf students apparently experience hardship in conventional learning; however, despite their inability to hear, nothing can stop them from reading. Although they perform impressively in memorizing the information, their literacy and reading capability still appear to be weak since they lack the chance to revise by listening and practicing repetitively. Currently, the teaching media for deaf students are quite rare and inadequate, forcing them to face difficulties in integrating new knowledge, even though most of the contents are in a form of written, printed, downloaded, or even accessible via an e-learning platform. However, it is crucial to bear in mind that each learner is different. There is evidence showing that some learners prefer particular methods of learning, also known as learning preferences or learning styles. Thus, the present study reports the sequence of learning styles obtained by using a modified VRK + TSL model that categorized students based on their learning styles. We also propose four different ways of teaching using content-adaptive learning styles, namely visual, reading/writing, kinesthetic, and Thai sign language. Based on personal preferences and the principle of universal design under synthesized learning, an e-learning model was developed to identify deaf learners’ learning styles. The objective is to provide e-learning to identify the learning styles of hearing-impaired students and to respond with up-to-date e-learning materials that can be used anywhere and at any time. These materials must support the education of deaf students. As a result, learners have increased efficiency and increased learning outcomes.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

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1. Technology Usage For Education: Polytechnic Deaf or Hard-of-Hearing Student;2024 IEEE 6th Symposium on Computers & Informatics (ISCI);2024-08-10

2. Unsupervised Learning for Understanding Diversity: Applying Feature Engineering and Cluster Analysis to Deaf and Hard of Hearing Data;2024 International Conference on Control, Automation and Diagnosis (ICCAD);2024-05-15

3. Discovering the Micro-Clusters From a Group of DHH Learners;Advances in Educational Technologies and Instructional Design;2024-04-26

4. Integration of Information and Communication Technologies in Education for the Deaf;Advances in Educational Technologies and Instructional Design;2023-12-29

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