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Advancing Inclusive E-learning: Relationship Between Motivational Factors and Dyslexic Characteristics.

Wang, R. and Hook, A., 2024. Advancing Inclusive E-learning: Relationship Between Motivational Factors and Dyslexic Characteristics. In: Bramwell-Dicks, A., Evans, A., Winckler, M., Petrie, H. and Abdelnour-Nocera, J., eds. Design for Equality and Justice. INTERACT 2023. Cham: Springer, 283-287.

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DOI: 10.1007/978-3-031-61698-3_29

Abstract

Ensuring e-learning is inclusive for all is a global endeavor to ensure educational quality and equity. Little research has explored the relationship between learners’ dyslexic characteristics and motivational factors that affect continued intention to interact with an e-learning system. Identifying the motivational factors that are mostly related to dyslexia can inspire future research and practice for implementing motivational strategies and assistive services targeting these specific needs in e-learning environments. The present study investigated the relationship between severity of dyslexic characteristics and levels of motivational factors for engaging with e-learning. Preliminary results indicated significant relationships between severity of dyslexia and seven motivational factors, including a moderate, significant, negative correlation with Perceived Ease of Use, Confirmed Fit, Utilization, and Perceived Control, and a small, but significant, negative correlation with Feedback, Learning Experience, and Reading Experience. Discussions highlighted a personalized solution to dyslexia-related motivational needs in e-learning, considering the possibility of real-time data collection and computing enabled by e-learning environments. To advance e-learning for those with dyslexic traits, the seven dyslexia-related motivational factors found in this study can be prioritized to be monitored to detect dyslexic learners’ motivational needs and deliver personalized design, assistance, or strategies.

Item Type:Book Section
ISBN:9783031616976
Series Name:Lecture Notes in Computer Science
Volume:14536
ISSN:0302-9743
Group:Faculty of Science & Technology
ID Code:40617
Deposited By: Symplectic RT2
Deposited On:17 Dec 2024 08:40
Last Modified:17 Dec 2024 08:40

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