Assessment of Cognitive Style Preference: A Conceptual Model

Research in adaptive hypermedia educational systems has increased with the growth of the Internet. Currently, all adaptive hypermedia educational systems collect information about cognitive style through completion of a questionnaire based on a psychometric test. This direct measure may be intrusive and annoying to a student and makes an adaptive system aligned to cognitive style unavailable for students that have not completed the questionnaire. It is posited that non-intrusive methods for determining the cognitive style of hypermedia system users are needed to maximize the usability, functionality, and goals of adaptive hypermedia systems. This paper offers a new approach for the autonomous computer-based assessment of preferred cognitive style that can support studies in user modeling and human-computer interface domains. It further posits a conceptual model that attempts to determine the preferred cognitive style of an online educational hypermedia user through click-stream analysis of their web-based hypermedia choices and browsing patterns

AHyCo: a Web-Based Adaptive Hypermedia Courseware System

Adaptive hypermedia courseware systems resolve the problem of users’ disorientation in hyperspace through the adaptive navigation and presentation support. We describe the AHyCo (Adaptive Hypermedia Courseware) - an adaptive Web-based educational system for creation and reuse of adaptive courseware with emphasis on adaptive navigation support and lessons sequencing. The proposed model consists of the domain model, the student model, and the adaptive model. The system is composed of two environments: the authoring environment and the learning environment

Social personalized adaptive e-learning environment : Topolor - implementation and evaluation

This paper presents a quantitative study on the use of Topolor - a prototype that introduces Web 2.0 tools and Facebook-like appearance into an adaptive educational hypermedia system. We present the system design and its evaluation using system usability scale questionnaire and learning behavior data analysis. The results indicate high level of student satisfaction with the learning experience and the diversity of learning activities

Adaptive Web-Based Educational Hypermedia

This chapter describes recent and ongoing research to automatically personalize a learning experience through adaptive educational hypermedia. The Web had made it possible to give a very large audience access to the same learning material. Rather than offering several versions of learning material about a certain subject, for different types of leaners, adaptive educational hypermedia offers personalized learning material without the need to know a detailed classification of users before starting the learning process. We describe different approaches to making a learning experience personalized, all using adaptive hypermedia technology. We include research on authoring for adaptive learning material (the AIMS and MOT projects) and research on modeling adaptive educational applications (the LAOS project). We also cover some of our ongoing work on the AHA! system, which has been used mostly for educational hypermedia but has the potential to be used in very different application areas as wel

Herramienta autor Indesahc para la creación de cursos hipermedia adaptativos

Un elemento fundamental dentro de una plataforma de gestión del aprendizaje (Learning Management Systems, LMS) es la herramienta autor para la producción del material didáctico. En este artículo presentamos un sistema de desarrollo integrado para la creación y evaluación de cursos hipermedia adaptativos accesibles, basados en páginas Web. INDESAHC (Integrated Development System for Adaptive Hypermedia Courses), facilita la introducción del mapa de contenidos del curso, según un modelo del dominio basado en temas, lecciones, conceptos y escenarios tipos, en los cuales se realiza la integración de los archivos de media mediante un entorno visual intuitivo basado en plantillas. Una vez definidas las relaciones entre los temas y los niveles de dificultad de cada lección, se genera el curso hipermedia adaptativo, cuyo diseño final puede ser evaluado a través de una herramienta accesorio llamada EPRules (Educational Prediction Rules). Esta herramienta utiliza algoritmos de minería de datos, para el descubrimiento de información útil para facilitar un proceso de retroalimentación. Se describe además, el modelo didáctico en que se basa INDESAHC y se presenta una metodología eficaz, para evitar los problemas de desorientación y sobrecarga de contenidos en la navegación.In this paper we present an integrated development system for Web Based Adaptive Hypermedia Courses. We have developed an authoring tool called INDESAHC (Integrated Development System for Adaptive Hypermedia Courses) for this purpose. This tool facilitates to the course designer introducing the conceptual map, according to a domain model based on topics, lessons, concepts and learning components. Furthermore, the program allows the integration of media files in the course by means of an intuitive visual environment based on templates. Once defined the relationship among the topics and the difficulty level of each lesson, the user can generate the hypermedia adaptive course. The final design could be evaluated with an accessory tool called EPRules (Educational Prediction Rules). This tool is based on data mining algorithms in order to discover useful information for feedback. We also discuss on how our methodology can avoid the problems of disorientation and cognitive overload

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)

Adaptive Educational Hypermedia based on Multiple Student Characteristics

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. Keywords: adaptive educational hypermedia, multiple characteristics, student model

A spiral model for adding automatic, adaptive authoring to adaptive hypermedia

At present a large amount of research exists into the design and implementation of adaptive systems. However, not many target the complex task of authoring in such systems, or their evaluation. In order to tackle these problems, we have looked into the causes of the complexity. Manual annotation has proven to be a bottleneck for authoring of adaptive hypermedia. One such solution is the reuse of automatically generated metadata. In our previous work we have proposed the integration of the generic Adaptive Hypermedia authoring environment, MOT ( My Online Teacher), and a semantic desktop environment, indexed by Beagle++. A prototype, Sesame2MOT Enricher v1, was built based upon this integration approach and evaluated. After the initial evaluations, a web-based prototype was built (web-based Sesame2MOT Enricher v2 application) and integrated in MOT v2, conforming with the findings of the first set of evaluations. This new prototype underwent another evaluation. This paper thus does a synthesis of the approach in general, the initial prototype, with its first evaluations, the improved prototype and the first results from the most recent evaluation round, following the next implementation cycle of the spiral model [Boehm, 88]

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Defining adaptation in a generic multi layer model : CAM: the GRAPPLE conceptual adaptation model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA! offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool