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

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.

Adaptive e-learning systems through learning styles: A review of the literature

“…According to the conducted literature review, the main reason behind the preferences of most researchers for the Felder Silverman model concerning the rest lies mainly in the fact that a questionnaire has been developed that is very effective and easy to use for adaptive (Abech et al, 2016), (Ali et al, 2019), (Almeida et al, 2019), (Alsobhi & Alyoubi, 2019), (Anantharaman et al, 2019), (Andaloussi et al, 2019), (Apoki et al, 2020), (Araújo et al, 2020), (Arsovic & Stefanovic, 2020a), (Alshammari et al, 2015), (Alshammari & Qtaish, 2019), , (Boussakuk, 2020), (Budiyanto et al, 2017), (Christudas et al, 2018), (Dwivedi et al, 2018), (El Guabassi et al, 2018), (Elkot, 2019), (Fasihuddin et al, 2016), (Hurtado et al, 2018), (Karagiannis & Satratzemi, 2020), (Kolekar et al, 2018), (Kolekar et al, 2019), (Kurilovas, 2019), (Lakkah et al, 2017), , (Sunil & Doja, 2019), (Supangat & Bin Saringat, 2020), (Sweta & Lal, 2017), (Tarus et al, 2017), (Tortorella & Graf, 2017), (Vaidya & Joshi, 2018) VARK 4 (10%) (Aeiad & Meziane, 2019), (Bradáč & Smolka, 2020), (Hassan & Qureshi, 2018), (Nalintippayawong et al, 2017) Honey and Mumford 3 (7%) (Baharudin et al, 2017), (Drissi & Amirat, 2016),…”

“…However, apart from the prediction techniques selected, these studies do not share much in their approaches. (Abech et al, 2016), (Ali et al, 2019), (Aljojo et al, 2016), (Almeida et al, 2019), (Alsobhi & Alyoubi, 2019), (Apoki et al, 2020), (Araújo et al, 2020), (Arsovic & Stefanovic, 2020a), (Alshammari & Qtaish, 2019), (Baharudin et al, 2017), , (Boussakuk, 2020), (Bradáč & Smolka, 2020), (Budiyanto et al, 2017), (Dwivedi et al, 2018), (Drissi & Amirat, 2016), (El Guabassi et al, 2018), (Elkot, 2019), (Fasihuddin et al, 2016), (Hafidi & Bensebaa, 2015), (Hassan & Qureshi, 2018), (Hurtado et al, 2018), (Lakkah et al, 2017), (Nalintippayawong et al, 2017), (Papanikolaou, 2015), , (Siddique et al, 2019), (Supangat & Bin Saringat, 2020), (Tarus et al, 2017), (Tortorella & Graf, 2017), (Vaidya & Joshi, 2018) Implicit or automatic modeling (Dynamic) (Sunil & Doja, 2019), (Kolekar et al, 2019) It becomes pretty clear that there is a need to move into implicit techniques, providing greater flexibility and allowing the learner to change and evolve along with her skills. However, even the studies that have been made that way have differing outlooks on how this automation should be achieved, resulting in multiple approaches with no standard anchors.…”

Identifying gaps in using artificial intelligence to support students with intellectual disabilities from education and health perspectives

“…Yaquob and Hamed (2019), meanwhile, introduced an adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). The proposed system correlated each type of dyslexia with the preferred learning techniques associated with it.…”

Modelling Support Systems for Selecting Professions for Applicants in the Content of Personalization of Education

“…The article [9] sets out the criteria for the international assessment of IT knowledge using artificial intelligence in education. [20] proposes the use of artificial intelligence based adaptive learning hypermedia systems in e-learning.Projecting the "educational process" phase of the personal education trajectory using fuzzy set theory helps students become more promising graduates [12].Obviously successful use of genetic algorithms, neural networks, and matrices in addressing the teacher-learner relationship and their interaction issues in the virtual learning environment [21,23]. [23] shows an example of the use of software such as CATIA V5 and Fluidsim CAD systems in the teaching technique of laboratory and technical subjects.…”

“…The article [9] sets out the criteria for the international assessment of IT knowledge using artificial intelligence in education. [20] proposes the use of artificial intelligence based adaptive learning hypermedia systems in e-learning.…”