Content adaptation for students with learning difficulties: design and case study

Abstract

Purpose – This article aims to propose an adaptation algorithm that combines the analytical hierarchy process (AHP), a rule-based system, and a k-means clustering algorithm. Informatic tools are very useful to enhance the learning process in the classroom. The large variety of these tools require advanced decision-making techniques to select parameters, such as student profiles and preferences, to adjust content and information display, according to specific characteristics and necessities of students. They are part of the Kamachiy–Idukay (KI), a platform to offer adaptative educational services to students with learning difficulties or disabilities. Design and Methodology – The design and implementation of the adaptation algorithm comprises the following phases: utilization of the AHP to determine the most important student parameters, parameter to take into account in the adaptation process, such as preferences, learning styles, performance in language, attention and memory aspects and disabilities; designing the first part of the adaptation algorithm, based on a rule-based system; designing the second part of the adaptation algorithm, based on k-means clustering; integration of the adaptation algorithm to KI; and validation of the approach in a primary school in Bogotá (Colombia). Approach – The main approach is the application of computational techniques, namely, rule-based systems and k-means clustering, plus an AHP prioritization at design time to yield a system to support the teaching–learning process for students with disabilities or learning difficulties. Findings – The algorithm found several groups of students with specific learning difficulties that required adapted activities. The algorithm also prioritized activities according to learning style and preferences. The results of the application of this system in a real classroom yielded positive results. Limitations of the research – The algorithm performs adaptation for students with mild disabilities or learning difficulties (language, attention and memory). The algorithm does not address severe disabilities that could greatly affect cognitive abilities. Contributions – The main contribution of this paper is an adaptation algorithm with the following distinctive characteristics, namely, designed utilizing the AHP, which ensures a proper prioritization of the student characteristics in the adaptation process, and utilizes a rule-based system to identify different adaptation scenarios and k-means clustering to group students with similar adaptation requirements.

Educational Robotics Intervention in the Motivation of Students

“…Con la metodología propuesta se pretende conseguir efectos formativos en los estudiantes evidenciado en el aumento de la autoestima por medio del éxito, así como el mantenimiento de la motivación durante el proceso de enseñanza y aprendizaje al utilizar robótica educativa en el aula de forma constante. De igual forma se recomienda tener en cuenta características de adaptación [80], [81], [82], [83] en plataformas robóticas, así como en las actividades complementarias a la robótica educativa.…”

Evaluation of content adaptation

“…One of them is adaptation to provide services that convey the information to students, according to their particular characteristics and necessities (Bailey et al, 2002). Content adaptation utilizes parameters, such as user preferences (Bailey et al, 2002;Minsky, 1974), goals, knowledge, among other data stored in a user model (Bremgartner and de Magalhaes Netto, 2011), focusing in enriching services and displaying only the information that is accord to those characteristics (Lancheros-Cuesta et al, 2014).…”