Abstract
This paper investigates the pedagogical basis of Adaptive Educational Hypermedia Systems (AEHS) that incorporate Learning Styles in order to accommodate user's learning style preferences and needs. Therefore, AES adapt the learning content, its presentation and navigation to the user's learning style preferences. We collect thirty three (33) Adaptive and Intelligent Web-based Educational Systems (AIWBES) that incorporate learning styles and discuss twenty of them, namely the AEHS, as the remaining are Intelligent Tutorng Systems. The main achievement of this work is the investigation of AEHS' pedagogical basis in terms of adaptation rules. We conclude that these systems follow similar patterns in their adaptation logic.
Highlights
The Internet and World Wide Web (WWW) offer new possibilities for innovative instruction, as the development of hypermedia educational systems facilitates learning and utilizes learning as an active process, in which users can explore alternative learning paths
The research questions are: What is the pedagogical basis of Adaptive Educational Hypermedia Systems (AEHS)? What are the adaptation rules that following these systems in order to adapt the educational material to user's learning style preferences? In particular, the search was performed using keywords, such as adaptive educational stystems, intelligent systems, personalization and adaptative systems, learning styles, learning styles and adaptive systems, pedagogical basis at adaptive systems, adaptation rules, adaptation rules and adaptive systems, categorization of adaptive systems, etc
''INSPIRE'' (INtelligent System for Personalized Instruction in a Remote Environment) is an Adaptive Educational Hypermedia System that personalizes the presentation of educational material in accordance with users' learning style, based on the Honey and Mumford's Learning Style Model
Summary
The Internet and World Wide Web (WWW) offer new possibilities for innovative instruction, as the development of hypermedia educational systems facilitates learning and utilizes learning as an active process, in which users can explore alternative learning paths. AEHS cater to needs of each individual user, adapts to learning goals/tasks, level of knowledge, background, experience, traits, context of work, prerequisite knowledge and interests [3]. There are only a few studies that provide overview of such systems, which main source of adaptation are learning styles, such as [8], [9], [10], [11], [7], [12], [6], [13], etc. In our study we categorize the undermentioned systems by learning style models and adaptation rules. Popescu et al [4] propose a model, ULSM (Unified Learning Style Model), a characteristic-based modeling approach for characterizing the students from their learning preferences. The present work investigates AES which incorporate learning styles in order to adapt the learning content, its presentation and navigation to the user's learning style preferences. As the adaptation to the learning style has been considered an important factor towards personalized instruction and adaptation rules are required for adaptation, we consider that is purposeful to examine the adaptation rules of these systems
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