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

Content adaptation for students with learning difficulties: design and case study 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Information Systems Emerald Publishing

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

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Publisher
Emerald Publishing
Copyright
Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
ISSN
1744-0084
DOI
10.1108/IJWIS-12-2013-0040
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Web Information SystemsEmerald Publishing

Published: Jun 10, 2014

Keywords: Information systems; AHP; Content and display adaptation; Decision‐making algorithms; K‐means clustering

References