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

Ruijie Wang, Liming (Luke) Chen, Ivar Solheim

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

Increased user motivation from interaction process leads to improved interaction, resulting in increased motivation again, which forms a positive self-propagating cycle. Therefore, a system will be more effective if the user is more motivated. Especially for students with dyslexia, it is common for them to experience more learning difficulties that affect their learning motivation. That's why we need to employ techniques to enhance user motivation in the interaction process. In this research, we will present a system architecture for motivation-enhanced learning and the detailed process of the construction of our motivation model using ontological approach for students with dyslexia. The proposed framework of the personalised learning system incorporates our motivation model and corresponding personalisation mechanism aiming to improve learning motivation and performance of students with dyslexia. Additionally, we also provide examples of inference rules and a use scenario for illustration of personalisation to be employed in our system.
Original languageEnglish
Pages13-19
Number of pages7
DOIs
Publication statusPublished - 2017
Eventthe 2017 International Conference - Toronto, ON, Canada
Duration: 10 Sep 201712 Sep 2017

Conference

Conferencethe 2017 International Conference
Period10/09/1712/09/17

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