A Conceptual System Architecture for Motivation-enhanced Learning for Students with Dyslexia

Wang, Ruijie; Chen, Liming; Solheim, Ivar

doi:10.1145/3141151.3141158

Cited by 3 publications

(2 citation statements)

References 25 publications

(43 reference statements)

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“…For example, Gooch et al have examined the use of gamification to motivate children with dyslexia, showing that gamification can enhance dyslexic children's learning motivation [1]. However, their study fails to address more aspects of motivation for dyslexic students in a big picture; additionally, educational games may be boring for the adults with dyslexia, though they may work well on dyslexic children [47,48], and also e-learning systems are more widely available than educational games. Therefore, we believe that research on e-learning systems based on users' motivation model to provide personalized motivational support has a wider range of users, which is in fact applicable to all generic users certainly including dyslexic users.…”

Section: Motivational Modeling In E-learning Systems and Technology Amentioning

confidence: 99%

Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems

Wang

Chen

Solheim

2020

ACM Trans. Interact. Intell. Syst.

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E-Learning systems can support real-time monitoring of learners' learning desires and effects, thus offering opportunities for enhanced personalized learning. Recognition of the determinants of dyslexic users' motivation to use e-learning systems is important to help developers improve the design of e-learning systems and educators direct their efforts to relevant factors to enhance dyslexic students' motivation. Existing research has rarely attempted to model dyslexic users' motivation in e-learning context from a comprehensive perspective. The present work has conceived a hybrid approach, namely, combining the strengths of qualitative and quantitative analysis methods, to motivation modeling. It examines a variety of factors that affect dyslexic students' motivation to engage in e-learning systems from psychological, behavioral, and technical perspectives, and establishes their interrelationships. Specifically, the study collects data from a multi-item Likert-style questionnaire to measure relevant factors for conceptual motivation modeling. It then applies both covariance-based (CB-SEM) and variance-based structural equation modeling (PLS-SEM) approaches to determine the quantitative mapping between dyslexic students' continued use intention and motivational factors, followed by discussions about theoretical findings and design instructions according to our motivation model. Our research has led to a novel motivation model with new constructs of Learning Experience, Reading Experience, Perceived Control, and Perceived Privacy. From both the CB-SEM and PLS-SEM analyses, results on the total effects have indicated consistently that Visual Attractiveness, Reading Experience, and Feedback have the strongest effects on continued use intention.

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Section: Motivational Modeling In E-learning Systems and Technology Amentioning

confidence: 99%

Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems

Wang

Chen

Solheim

2020

ACM Trans. Interact. Intell. Syst.

Self Cite

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“…In recent years, more and more researchers have attempted to develop an understanding of how technology can enhance the effectiveness of teaching and learning interventions (Wang et al, 2017). E-learning has evolved into a key tool that can facilitate and enhance the progression of student's knowledge, skills and understanding (Nuttal, 2014).…”

Section: Introductionmentioning

confidence: 99%

Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type

Alsobhi

Alyoubi

2019

DTA

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Purpose Through harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type. Design/methodology/approach The DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases. Findings An experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes. Originality/value The DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.

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Sustained Feelings of Success and Agency: Keys to Literacy Motivation among Adolescents with Learning Disabilities

Louick¹,

Scanlon

2019

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A Conceptual System Architecture for Motivation-enhanced Learning for Students with Dyslexia

Cited by 3 publications

References 25 publications

Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems

Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems

Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type

Sustained Feelings of Success and Agency: Keys to Literacy Motivation among Adolescents with Learning Disabilities

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