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

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.,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.,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.,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.

[1]  Nawaz Khan,et al.  Toward Linking Dyslexia Types and Symptoms to the Available Assistive Technologies , 2014, 2014 IEEE 14th International Conference on Advanced Learning Technologies.

[2]  David A. Sanders,et al.  Inferring Learning Style From the Way Students Interact With a Computer User Interface and the WWW , 2010, IEEE Transactions on Education.

[3]  Rabih Bashroush,et al.  A Semantic Rule-based Approach Supported by Process Mining for Personalised Adaptive Learning , 2014, EUSPN/ICTH.

[4]  M. Lytras,et al.  Sustainable Higher Education and Technology-Enhanced Learning (TEL) , 2018, Sustainability.

[5]  Marcus Nyström,et al.  The Effect of Illustration on Improving Text Comprehension in Dyslexic Adults , 2016, Dyslexia.

[6]  Ren Yi,et al.  Exploring an on-line course applicability assessment to assist learners in course selection and learning effectiveness improving in e-learning , 2017 .

[7]  Liming Chen,et al.  A Conceptual System Architecture for Motivation-enhanced Learning for Students with Dyslexia , 2017 .

[8]  Nawaz Khan,et al.  Personalised Learning Materials Based on Dyslexia Types: Ontological Approach , 2015, KES.

[9]  O. P. Vyas,et al.  An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage , 2015, Knowl. Based Syst..

[10]  Mahmoud Abd Ellatif,et al.  A proposed paradigm for smart learning environment based on semantic web , 2017, Comput. Hum. Behav..

[11]  Nawaz Khan,et al.  Dyslexia adaptive e-learning system based on multi-layer architecture , 2015, 2015 Science and Information Conference (SAI).

[12]  Nigel A. Beacham,et al.  An investigation into the effects that digital media can have on the learning outcomes of individuals who have dyslexia , 2006, Comput. Educ..

[13]  Keeley A. Crockett,et al.  On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees , 2017, Int. J. Hum. Comput. Stud..

[14]  Petra J Lewis,et al.  Brain Friendly Teaching-Reducing Learner's Cognitive Load. , 2016, Academic radiology.