Adaptive Educational Hypermedia Systems

The purpose of this study is to analyze various studies on adaptive educational hypermedia environments, regarded as a new field of research and application, in a comprehensive way. To this end, all postgraduate thesis studies conducted in Turkey, articles in the journals whose scope covers educational sciences and which published in Turkey, and articles with Turkey address in the international journals indexed in SSCI were reviewed and content analysis was employed. The study will present current situation of this field and also, inform those who are interested in adaptive educational hypermedia environments concerning deficiencies in the field in the light of similar studies conducted abroad.

The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.

Identification of learning styles supports Adaptive Educational Hypermedia Systems compiling and presenting tutorials custom in cognitive characteristics of each individual learner. This work addresses the issue: identifying the learning style of students, following the Kolb’s learning cycle. To this purpose, we propose a three-layers Fuzzy Cognitive Map (FCM) in conjunction with a dynamic Hebbian rule for learning styles recognition. The form of FCMs is designed by humans who determine its weighted interconnections among concepts. But the human factor may not be as reliable as it should be. Thus, a FCM model of the system allowing the adjustment of its weights using additional learners’ characteristics such as the Learning Ability Factors. In this article, two consecutively interconnected FCM (in the form of a three layer FCM) are presented.  The schema’s efficiency has been tested and compared to known results after a fine-tuning of the weights of the causal interconnections among concepts. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs. The online recognition of learning styles by using three-layer Fuzzy Cognitive Map improves the accuracy of recognition obtained using Bayesian Networks that uses quantitative measurements of learning style taken from statistical samples. This improvement is due to the fuzzy nature of qualitative characterizations (such as learning styles), and the presence of intermediate level nodes representing Learning Ability Factors. Such factors are easily recognizable characteristics of a learner to improve adjustment of weights in edges with one end in the middle-level nodes.  This leads to the establishment of a more reliable model, as shown by the results given by the application to a test group of students.

This paper describes the use of adaptation patterns in the task of formulating standards for adaptive educational hypermedia (AEH) systems that is currently under investigation by the EU ADAPT project. Within this project, design dimensions for high granularity patterns have been established. In this paper we focus on detailing lower granularity adaptive patterns based upon learning styles. Several patterns from existing AEH system case studies are identified and classified according to an extended learning style" onion" model. This ...

The paper focuses on the instructional and interface de-sign adopted in the Adaptive Educational Hypermedia System INSPIRE-INtelligent System for Personalized Instruction in a Remote Environment. INSPIRE dynami-cally generates lessons accommodating diversity in learners' knowledge background, preferences and pro-gress, and supports end-learner modifiability. The design of the interface supports the notion of the system as a coach on the learning process. The way the interface is embodied in the instructional strategy adopted in IN-SPIRE is described. An experimental study of the system is presented, which assesses students' attitudes towards the proposed design.

Majority of the online educational systems provide same content to all the learners without considering their personal needs, goals and educational background. This makes the learning process more difficult and boring. Adaptive hypermedia can enhance the quality of these systems through personalisation or customisation. Adaptive hypermedia based educational system (AHES) builds learners’ profile as a learner model and then presents the content to them according to their preferences and previous educational record. Development of
AHES is a lengthy and difficult process because it involves implementation of adaptation along with development of communication tools and digital library etc. To make this process easy, different authoring tools or frameworks are
available like AHA!, InterBook, NetCoach and COFALE. In this paper, their analysis and comparison are described through which selection of a framework to develop better quality AHES becomes easy.

Existing adaptive educational hypermedia systems focus on preparing adaptive educational environments for students and educators meeting their needs. In an educational adaptive hypermedia (AH) application, important problems faced by application developers (educators) are to create and update the domain model accurately. This study presents a new reference model and its concept for web-based adaptive educational hypermedia called SAHM (supervised adaptive hypermedia model). The new model redefines the storage layer of existing AH reference models and helps to solve the problems encountered in AH applications. With the help of this proposed model, application developers might develop a domain model of AH applications easily, effectively, and successfully. It is expected that the new SAHM would provide a new direction in designing new and more adaptive systems and new applications. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 60–74, 2013

The use of adaptations, along with the social affordances of collaboration and networking, carries a great potential for improving e-learning experiences. However, the review of the previous work indicates current e-learning systems have only marginally explored the integration of social features and adaptation techniques. The overall aim of this research, therefore, is to address this gap by evaluating a system

Abstract In this paper, we analyse web log data from user trials of the WHURLE-LS adaptive educational hypermedia (AEH) system from a behavioural perspective. This system allows users to switch from one presentational mode to another (visual, verbal or neutral), and this paper investigates users' choice of mode as they interacted with the system. We present the main findings of the browsing behaviours within the framework of rational choice theory, and discuss why and when switching might have occurred.

According to literature, a lot of research work on adaptive e-learning was carried out and, insofar, many systems in this application domain were developed in the last decades. One solution approach within the scope of adaptive educational hypermedia is the project AdeLE (Adaptive e-Learning with Eye-Tracking), started at the end of the year 2003 and with the scientific focus on

In this paper we describe the outcomes of experimentation with technical school students (of the same degree as senior high school students) on Physics teaching. Our first goal was to find out if the application of the Kolb's learning cycle on Physics teaching improves the performances of students. The unit of the chapter of electromagnetism we presented was the following: