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

Aisha Yaquob Alsobhi (Department of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)
Khaled Hamed Alyoubi (Department of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)

Data Technologies and Applications

ISSN: 2514-9288

Publication date: 1 April 2019

Abstract

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.

Keywords

Citation

Alsobhi, A. and Alyoubi, K. (2019), "Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type", Data Technologies and Applications, Vol. 53 No. 2, pp. 189-200. https://doi.org/10.1108/DTA-10-2018-0092

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Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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