Adaptive Educational Hypermedia Systems

Adaptive learning resources selection and sequencing is recognized as among the most interesting research questions in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning resources in AEHS, the definition of adaptation rules contained in the Adaptation Model, is required. Although, some efforts have been reported in literature aiming to support the Adaptation Model design by providing AEHS designers direct guidance or semi-automatic mechanisms for making the design process less demanding, still it requires significant effort to overcome the problems of inconsistency, confluence and insufficiency, introduced by the use of rules. Due to the problems of inconsistency and insufficiency of the defined rule sets in the Adaptation Model, conceptual "holes" can be generated in the produced learning resource sequences (or learning paths). In this paper, we address the design problem of the Adaptation Model in AEHS proposing an alternative sequencing method that, instead of generating the learning path by populating a concept sequence with available learning resources based on pre-defined adaptation rules, it first generates all possible learning paths that match the learning goal in hand, and then, adaptively selects the desired one, based on the use of a decision model that estimates the suitability of learning resources for a targeted learner. In our simulations we compare the learning paths generated by the proposed methodology with ideal ones produced by a simulated perfect rule-based AEHS. The simulation results provide evidence that the proposed methodology can generate almost accurate learning paths avoiding the need for defining complex rule sets in the Adaptation Model of AEHS.

Özet─ İletilebilen ve erişilebilen bilgi hacmindeki radikal artışla birlikte, tüm kullanıcılar için aynı sayfa içeriğini ve aynı bağlantıları sunan hiper ortamların kullanıcıların ihtiyaçlarına cevap verememesi, "uyarlanabilir öğrenme ortamlarının" tasarlanması ve geliştirilmesi ihtiyacını ortaya çıkarmıştır. Uyarlanabilir eğitsel hiper ortam kavramını bütüncül bir bakış açısıyla ele almayı amaçlayan bu çalışmada, uyarlanabilir öğrenme ortamı kavramı tanımlanarak, bu ortamlara gereksinim duyulma nedenleri üzerinde durulmuştur. Ardından uyarlanabilir öğrenme ortamlarının genel yapısı açıklanarak, bu ortamların oluşturulmasında kullanılan yöntem ve teknikler ele alınmıştır. Son olarak uyarlanabilir öğrenme ortamlarının değerlendirilme sürecinden bahsedilerek, literatürde var olan çalışmalardan yola çıkarak bu ortamların eğitim sürecine katkıları tartışılmıştır.

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.

Adaptive hypermedia is a relatively new direction in research at the crossroads of hypermedia and user modeling. Adaptive hypermedia systems build a model of the goals, preferences and knowledge of each individual user and use this model throughout the interaction with the user, in order to adapt to the needs of that user. Educational hypermedia was one of the first application areas for adaptive hypermedia and is currently one of the most popular and well-investigated. The goal of this presentation is to explain the nature and the mechanism of adaptation in educational adaptive hypermedia and to provide several examples of using adaptive hypermedia in educational and training applications of different natures and complexity.

It has been considered that when creating a learning module in an immersive educational environment the learning design process is time consuming particularly dictating the rules responsible for sequencing the module’s lessons. This challenge becomes more complicated when the sequence needs to be made more adaptive and personalised. Therefore, we introduce our proposed prototype system called iPerSeq –an intelligent Personalised Sequencing system-. This prototype relies on machine learning to intelligently personalise and adapt the learning path for individual students based on their previous contributions and behaviours during learning. This work in progress paper describes the current research being undertaken to investigate this issue and how it can be deployed in 3D Virtual Worlds. In addition, we explain the system’s conceptual model.

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.

The traditional learning with live interactions between teacher and students has achieved many successes but nowadays it raises the demand of personalized learning when computer and internet are booming. Learning is mostly associated with activities involving computers and interactive networks simultaneously and users require that learning material/activities should be provided to them in suitable manner. This is origin of adaptive learning domain. For this reason, the adaptive learning system (ALS) must have ability to change its action to provide learning content and pedagogic environment/method for every student in accordance with her/his individual characteristics. Adaptive systems are researched and developed for a long time; there are many kinds of them. So it is very difficult for researchers to analyze them. In this study report, I collect scientific resources to bring out an overview of adaptive learning systems along with their features. Main reference is the master thesis “User Modeling and User Profiling in Adaptive E-learning Systems” of author Christoph Fröschl. I express my deep gratitude to the author Christoph Fröschl for providing her/his great research.

one of the most important matters in e-learning systems is the adjustment of training with the level of knowledge, personality, behavior and abilities that names learner's profile. This profile is built by numerous factors and methods. We will focus in this paper in how the level of knowledge of learner is determined and how the methods used could be efficient to help learners to overcome their difficulties by specifying the exact origin of the cognitive errors they made. Thereafter, we will propose a novel approach of diagnostic and remediation taking into account the Proximal Development Zone (PDZ) of the learner to better help and support him in his training. To do that we propose an ontology of evaluation based on competencies approach and using the 2/3 rule adopted by de-Ketele and Xavier Roegiers to specify the origin of the errors made as a cognitive errors. In this case, our proposal supporting model focuses on learners' cognitive level by proposing diagnosis activities which confirm that such resource of such competence is not acquired and therefore select the most adequate remediation. In this specification – the source of difficulties meets by learners-that lies the originality of our approach.

The learning process in Adaptive Educational Hypermedia (AEH) environments is complex and may be influenced by aspects of the student, including prior knowledge, learning styles, experience and preferences. Current AEH environments, however, are limited to processing only a small number of student characteristics. This paper discusses the development of an AEH system which includes a student model that can simultaneously take into account multiple student characteristics. The student model will be developed to use stereotypes, overlays and perturbation techniques.

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.

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

Over the years Adaptive Hypermedia systems have evolved through three generations [4]: the pre-web generation, the web generation, and the new adaptive web generation. The latter –the current generation of AH– lumps together the need to explore actual tendencies of the ...

This paper focuses on a new adaptive digital storytelling environment that has been created in order to help teachers to investigate and treat students’ misconceptions about various learning subjects. The environment is comprised of an intelligent concept mapping tool that can be used for the diagnosis of students’ misconceptions about a specific learning concept, and their treatment, using appropriate digital stories. In fact, teachers, can use this concept mapping tool to create a detailed digital concept map of the learning concept in question. Teachers can also design a closed-ended interactive questionnaire that could be attached to each of its nodes. This questionnaire could be used for the evaluation of students’ knowledge about every sub-concept included in the aforementioned concept map. In this way, the misconceptions of each student and the class as a whole could be displayed. Thus, the system provides the possibility for adaptive personalized feedback for each student and for the whole class. The environment also provides a digital story tool for the creation of rich multimedia digital stories combining various multimedia files (text, sound, photos & video). Teachers can use this digital story tool to create appropriate stories for the treatment of students’ misconceptions which were diagnosed through the questionnaire. Subsequently, these stories could be attached to the nodes of the aforementioned concept map and the system can automatically assign the appropriate stories to each student to help her/him overcome his/her misconceptions. An example of the use of our adaptive digital storytelling environment will be demonstrated.

The domain of traditional hypermedia is revolutionized by the arrival of the concept of adaptation. Currently the domain of Adaptive Hypermedia Systems (AHS) is constantly growing. A major goal of current research is to provide a personalized educational experience that meets the needs specific to each learner (knowledge level, goals, motivation etc...). In this article we have studied the possibility of implementing traditional features of adaptive hypermedia in an open environment, and discussed the standards for describing learning objects and architectural models based on the use of ontologies as a prerequisite for such an adaptation.

This research paper presents the positive effect of incorporating individuals' working memory (WM) span as a personalization factor in terms of improving users' academic performance in the context of adaptive educational hypermedia. The psychological construct of WM is robustly related to information processing and learning, while there is a wide differentiation of WM span among individuals. Hence, in an effort to examine the role of cognitive and affective factors in adaptive hypermedia along with psychometric user profiling considerations, WM has a central role in the authors' effort to develop a user information processing model. Encouraged by previous findings, a larger scale study has been conducted with the participation of 230 university students in order to elucidate if it is possible through personalization to increase the performance of learners with lower levels of WM span. According to the results, users with low WM performed better in the personalized condition, which involved segmentation of the web content and aesthetical annotation, while users with medium/high WM span were slightly negatively affected by the same techniques. Therefore, it can by supported it is possible to specifically address the problem of low WM span with significant results.

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.

This paper discusses the need to provide adaptive navigational support for students accessing large numbers of interactive examples in Web-enhanced education. Here we introduce the system NavEx (Navigation to Examples), which is able to provide the adaptive annotation of programming examples without the need for manual indexing. NavEx uses innovative algorithms for the prerequisite-outcome indexing of learning materials and is an efficient knowledge-based approach for adaptive annotation. The system serves as an adaptive value-added service residing between an learning portal and an advanced re-usable content.

This paper discusses the need to provide adaptive navigational support for students accessing large numbers of interactive examples in Web-enhanced education. Here we introduce the system NavEx (Navigation to Examples), which is able to provide the adaptive annotation of programming examples without the need for manual indexing. NavEx uses innovative algorithms for the prerequisite-outcome indexing of learning materials and is an efficient knowledge-based approach for adaptive annotation. The system serves as an adaptive value-added service residing between an learning portal and an advanced re-usable content.

Despite the fact that adaptive hypermedia techniques have proven their ability to provide user guidance and orientation in hyperspace, we do not currently see the widespread adoption of these techniques. A couple of reasons may explain this phenomenon. One of them is the current lack of re-usability and interoperability between adaptive techniques/systems, which -to some degree -originates in the so-called "open corpus problem" found in adaptive hypermedia. In this article, we analyze this problem in a popular arena: adaptive hypermedia systems with an emphasis on education. The origins and effects of the open corpus problem are discussed, and recent approaches are demonstrated that have -in one way or the other -developed as strategies for solving the open corpus problem. We summarize these findings and discuss how solution strategies can be successfully employed in the future, enabling adaptive hypermedia techniques within open, dynamic information spaces, such as the Semantic Web. We restrict ourselves to adaptive navigation support because we consider the first problem to solve in an open corpus setting is to adapt access to documents to the user's needs. Contentlevel adaptation (i.e., adapting the content of one particular document) is a possible future step which may later be addressed by, for example, considering information chunks or variations of documents instead of documents-as-entities, and by applying techniques from navigational-level adaptation onto these chunks / variations / etc.

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 ...

In this article we present and discuss the main ideas that supported the development of a course for multimedia project development with art students focusing on the use of new media formats and collaborative learning environments. The course followed a project-based teaching and learning model and made use of several applications for students to produce sequential semantic hypervideos. This model was developed within a study that followed a design-based research approach in two classes from Faculdade de Belas-Artes da Universidade de Lisboa. In this article we focus on some results following examples of students’ work and discuss how some of their activities might have contributed to an increase of their digital literacy.

Web-based Intelligent Design and Tutoring System (WINDS) is a European project1 with the objective to implement a learning environment integrating an intelligent tutoring system, a computer instruction management system and a set of cooperative tools. This environment will be used to build a large knowledge base supporting Architecture and Civil Engineering Design Courses and to experiment a comprehensive Virtual University

This paper presents an approach to resolve the problem of authoring and interchanging educational material, based on web services. Here we describe the ultimate goal, of reusing and interchanging freely adaptive elearning material, shortly sketch previous solutions, showing their benefits but also limitations and then introduce our webservice based approach. Finally, we discuss the gains brought by our proposed solution.

Currently there are just a few adaptive hypermedia authoring tools oriented to education. We develop one of them -the Adaptive Learning Environment 1 (ALE) that has been already used by several universities in the area of design and architecture. In this paper we present an approach how to support the authoring process in the case of adaptive (educational) hypermedia. We describe the representation of content materials, of the knowledge about the domain as well as about the student, interconnection of the knowledge with the materials, we mention the support of template based authoring and finally explain what adaptation strategies we implement.

In this paper we present an intelligent way of organizing learning material in an adaptive educational hypermedia system. We describe the use of instructional metadata which facilitates both the detection of student learning style and the application of various adaptation techniques. The advantage of our approach is that it is independent of a particular learning style model. Furthermore, the author has to supply only the annotated learning content (the static description) while the adaptation logic (the dynamic description) is provided by the system. The approach is implemented in an adaptive educational system called WELSA and illustrated with a course module in the area of Artificial Intelligence.

The penetration of Adaptive Educational Hypermedia (AEH) within the educational community has been very poor so far. In part this is due to the complexity of the authoring process. But it also influenced by the often short 'lifetime' of the AEH software itself. We see AEH system interoperability as a route to address these problems. This paper builds on previous limited interoperability work, between the AEH systems MOT and WHURLE, by using open web standards. We describe a Web Service using Web Services Description Language and SOAP that is capable of converting MOT materials to WHURLE. Moreover this Web Service is flexible and easily extensible, and we hope that this will mark the first step in developing an interoperable middleware for AEH conversions.

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.

This research paper presents the positive effect of incorporating individuals’ working memory (WM) span as a personalization factor in terms of improving users’ academic performance in the context of adaptive educational hypermedia. The psychological construct of WM is robustly related to information processing and learning, while there is a wide differentiation of WM span among individuals. Hence, in an effort to examine the role of cognitive and affective factors in adaptive hypermedia along with psychometric user profiling considerations, WM has a central role in the authors’ effort to develop a user information processing model. Encouraged by previous findings, a larger scale study has been conducted with the participation of 230 university students in order to elucidate if it is possible through personalization to increase the performance of learners with lower levels of WM span. According to the results, users with low WM performed better in the personalized condition, which involved segmentation of the web content and aesthetical annotation, while users with medium/high WM span were slightly negatively affected by the same techniques. Therefore, it can by supported it is possible to specifically address the problem of low WM span with significant results.

Research in educational adaptive hypermedia systems has been concerned with the generation of personalized courses, in this work we focus on a task of these kind of systems: the semi-automatic sequencing of didactic resources. Sequencing defines the order in which topics (and didactic resources) in a course will be presented to learners, considering for this, their previous knowledge and particular objectives. This task is based on subjective information, for example the learner knowledge, preferences, learning style, and even assessment results are perceived differently depending the context. In this paper we define an architecture for sequencing of didactic materials using fuzzy attributes and rules.

The design of an adaptive or adaptable educational hypermedia (AEH) is a complex process. Beside the design activities of "conventional" educational hypermedia, it involves a number of other tasks -the design of the learner model, of the user model detection mechanisms, of the adaptation capabilities (to dynamically customize the application according to the learner's model), of the user control over customization. Patterns can significantly help mastering the complexity of AEH design. As a preliminary steps towards the definition of a pattern language for AEH, we have identified and structured the typical problems a designer may face during each AEH design task. From the problem analysis and classification we have defined a taxonomy for AEH patterns, which can be used as a conceptual framework for discovering patterns and for structuring a pattern language in the AEH field. The paper reports our approach, discusses our taxonomy, and presents examples of AEH design patterns.

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.

For recently years the research of adaptation of computer education has been an important topic. Although Adaptive Educational Hypermedia Systems (AEHS) are different disciplines with IMS Learning Design (IMS LD), they have the same goal is to create the best possible environment for a learner to perform his/her learning activities in. How IMS LD addresses many requirements for computer based adaptation and personalized e-Learning is one of the main concerns for researcher in this field. This paper represents an approach to learning design for adaptive learning system for adaptation of learning activities. Constructing set of rules for learning activities adaptation represented in first order logic, and mapping them into IMS LD specification. In addition, an adaptive course of computer science domain in online context is implemented using IMS LD design.

Adaptive learning resources selection and sequencing is recognized as among the most interesting research questions in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning resources in AEHS, the definition of adaptation rules contained in the Adaptation Model, is required. Although, some efforts have been reported in literature aiming to support the Adaptation Model design by providing AEHS designers direct guidance or semi-automatic mechanisms for making the design process less demanding, still it requires significant effort to overcome the problems of inconsistency, confluence and insufficiency, introduced by the use of rules. Due to the problems of inconsistency and insufficiency of the defined rule sets in the Adaptation Model, conceptual "holes" can be generated in the produced learning resource sequences (or learning paths). In this paper, we address the design problem of the Adaptation Model in AEHS proposing an alternative sequencing method that, instead of generating the learning path by populating a concept sequence with available learning resources based on pre-defined adaptation rules, it first generates all possible learning paths that match the learning goal in hand, and then, adaptively selects the desired one, based on the use of a decision model that estimates the suitability of learning resources for a targeted learner. In our simulations we compare the learning paths generated by the proposed methodology with ideal ones produced by a simulated perfect rule-based AEHS. The simulation results provide evidence that the proposed methodology can generate almost accurate learning paths avoiding the need for defining complex rule sets in the Adaptation Model of AEHS.

This research paper presents the positive effect of incorporating individuals' working memory (WM) span as a personalization factor in terms of improving users' academic performance in the context of adaptive educational hypermedia. The psychological construct of WM is robustly related to information processing and learning, while there is a wide differentiation of WM span among individuals. Hence, in an effort to examine the role of cognitive and affective factors in adaptive hypermedia along with psychometric user profiling considerations, WM has a central role in the authors' effort to develop a user information processing model. Encouraged by previous findings, a larger scale study has been conducted with the participation of 230 university students in order to elucidate if it is possible through personalization to increase the performance of learners with lower levels of WM span. According to the results, users with low WM performed better in the personalized condition, which involved segmentation of the web content and aesthetical annotation, while users with medium/high WM span were slightly negatively affected by the same techniques. Therefore, it can by supported it is possible to specifically address the problem of low WM span with significant results.

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 model forms the basis of a learning style taxonomy, introduced here, whose components determine adaptation patterns for AEH. These patterns are of importance both for authoring, as well as for interfacing between adaptive hypermedia systems. From an authoring point of view, these patterns may be used to establish a fine-grain approach to instructional strategies that can be implemented in AEH systems, as a response to a particular learning style. The implementation of this adaptation pattern taxonomy is discussed, both generally and in detail.

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:

In order to assess the positive effect and validity of personalization on the basis of users' cognitive and emotional characteristics, this study presents three subsequent experiments. The first experiment explores the relationship of cognitive style and users' eye gaze behavior as to validate this specific psychological construct in the context of educational hypermedia. The second and third experiments present the effect of a set of human factors (cognitive style, visual working memory span, control/speed of processing, and anxiety) in an adaptive educational system. The eye tracking experiment demonstrated that eye gaze patterns are robustly related to cognitive style (n ¼ 21), while matching the instructional style to users' characteristics was revealed to be statistically significant in optimizing users' performance (n ¼ 219), with the exception of control/speed of processing. Based on this empirical assessment, this paper argues that individual differences at this intrinsic level are important and adaptation on these parameters through personalization technologies may have a positive effect on learning performance.

In order to assess the positive effect and validity of personalization on the basis of users' cognitive and emotional characteristics, this study presents three subsequent experiments. The first experiment explores the relationship of cognitive style and users' eye gaze behavior as to validate this specific psychological construct in the context of educational hypermedia. The second and third experiments present the effect of a set of human factors (cognitive style, visual working memory span, control/speed of processing, and anxiety) in an adaptive educational system. The eye tracking experiment demonstrated that eye gaze patterns are robustly related to cognitive style (n ¼ 21), while matching the instructional style to users' characteristics was revealed to be statistically significant in optimizing users' performance (n ¼ 219), with the exception of control/speed of processing. Based on this empirical assessment, this paper argues that individual differences at this intrinsic level are important and adaptation on these parameters through personalization technologies may have a positive effect on learning performance.

This paper reports about the recent advances towards establishing a common platform for adaptive educational hypermedia (AEH) authoring. We present the conversion from MOT, a dedicated authoring system, to AHA! used in this context as delivery system for AEH. Moreover, we describe two new representation languages that emerged in the process: a common format for defining the static material, CAF, and an extended adaptation language for the description of the dynamic behaviour, LAG. Finally, some evaluations are shown and conclusions are drawn.

Authoring adaptive educational hypermedia is a very complex activity. In order to promote a wider application of this technology, there is a need of methods and tools which support the work of teachers and course designers. In that sense, data mining is a promising technology. Data mining techniques have already been used on/in e-learning systems, but most of the times their application is oriented to provide better support to students; little work has been done for assisting adaptive hypermedia authors through data mining. In this paper we present a proposal for using data mining during the process of adaptive hypermedia design and also for its evaluation. A tool implementing the proposed approach is also presented, along with examples of how data mining technology can assist teachers.

Adaptive Navigation Support techniques aim at manipulating web page links to present relevant and appropriate information for each user. This paper sketches how these techniques can be described using a notational method, the IMS Learning Design specification, and introduces our proposal to depict adaptive techniques using this specification.

Abstract: Web-based Intelligent Design and Tutoring System (WINDS) is a European project 1 with the objective to implement a learning environment integrating an intelligent tutoring system, a computer instruction management system and a set of cooperative tools. This environment will be used to build a large knowledge base supporting Architecture and Civil Engineering Design Courses and to experiment a comprehensive Virtual University of Architecture and Engineering Design. In this paper we present the basic concepts of the ...