14 research outputs found
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 Information Visualization for Personalized Access to Educational Digital Libraries
Personalization is one of the emerging ways to increase the power of modern Digital Libraries. The Knowledge Sea II system presented in this paper explores social navigation support, an approach for providing personalized guidance within the open corpus of educational resources. Following the concepts of social navigation we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The study indicates that Knowledge Sea II became the students' primary tool for accessing the open corpus documents used in a programming course. The social navigation support implemented in this system was considered useful by students participating in the study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigational support, such as the ability to rank the usefulness of a page
Domain Modeling for Personalized Guidance
This chapter attempts to untangle the relationships between personalized guidance and domain modeling, as well as to explain how domain modeling could be used to provide personalized guidance. The problem of personalized guidance has a long history in the area of adaptive educational systems (AES). In fact, the very first recognized AES SCHOLAR (Carbonell, 1970) focused on guiding students to the most relevant facts and questions about the geography of South America. The SCHOLAR functionality was based on a domain model in the form of a semantic network and an overlay student model. Since that time, a considerable share of research in the field of AES has focused on different kinds of personalized guidance, and the majority of this work relied heavily on domain modeling—which makes these two research directions heavily interconnected
ELM-ART - An Interactive and Intelligent Web-Based Electronic Textbook
This paper present provides a broader view on ELM-ART, one of the first Web-based Intelligent Educational systems that offered a creative combination of two different paradigms - Intelligent Tutoring and Adaptive Hypermedia technologies. The unique dual nature of ELM-ART contributed to its long life and research impact and was a result of collaboration of two researchers with complementary ideas supported by talented students and innovative Web software. The authors present a brief account of this collaborative work and its outcomes. We start with explaining the "roots" of ELM-ART, explain the emergence of the "intelligent textbook" paradigm behind the system, and discuss the follow-up and the impact of the original project
Serious Games for Training Myoelectric Prostheses through Multi-Contact Devices
In the medical context, designing and developing myoelectric prostheses has made it
possible for patients to regain mobility lost due to amputations; however, their use requires intensive
training. Serious games through multi-touch devices can serve as a complement to the activities
carried out during face-to-face sessions with occupational therapists and physiotherapists, as a useful
resource to engage patients, especially children, and make them enjoy training. In this paper, we
describe our work to support the training of myoelectric prostheses through digital serious games.
Firstly, we studied the needs of children with myoelectric prostheses and the way they perform
rehabilitation. Secondly, we designed specific games to support training accordingly. Thirdly, we
developed a system able to generate variations of these games dynamically, adapting the elements
at each round to the needs and progress of each child. The interfaces are simple, friendly, and
based on tablets to favor autonomy. Finally, we assessed the potential of the use of these games for
rehabilitation. Specialists in Physiotherapy, Occupational Therapy, Medicine and Special Education
collaborated as experts; they agreed that SilverTouch is good for myoelectric prosthetic training and
confirmed its potential to be widely used in this context.This research was co-funded by the Spanish Ministry of Science and Innovation, project
IndiGo! number PID2019-105951RB-I00 and the Structural Funds FSE and FEDER, project e-MadridCM number S2018/TCS-4307
Supporting delivery of adaptive hypermedia
Although Adaptive Hypermedia (AH) can improve upon the traditional one-size-fitsall
learning approach through Adaptive Educational Hypermedia (AEH), it still has
problems with the authoring and delivery processes that are holding back the
widespread usage of AEH. In this thesis we present the development of the Adaptive
Delivery Environment (ADE) delivery system and use the lessons learnt during its
development along with feedback from adaptation specification authors,
researchers and other evaluations to formalise a list of essential and recommended
optional features for AEH delivery engines.
In addition to this we also investigate how the powerful adaptation techniques
recommended in the above list and described in Brusilovsky and Knutov’s
taxonomies can be implemented in a way that minimises the technical knowledge of
adaptation authors needed to use these techniques. As the adaptation functionality
increases, we research how a modular framework for adaptation strategies can be
created to increase the reusability of parts of an AH system’s overall adaptation
specification. Following on from this, we investigate how reusing these modular
strategies via a pedagogically based visual editor can enable adaptation authors
without programming experience to use these powerful adaptation techniques
Evaluation of topic-based adaptation and student modeling in QuizGuide
This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide—the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation—from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs
Manual and automatic authoring for adaptive hypermedia
Adaptive Hypermedia allows online content to be tailored specifically to the needs
of the user. This is particularly valuable in educational systems, where a student
might benefit from a learning experience which only displays (or recommends)
content that they need to know.
Authoring for adaptive systems requires content to be divided into stand-alone
fragments which must then be labelled with sufficient pedagogical metadata.
Authors must also create a pedagogical strategy that selects the appropriate
content depending on (amongst other things) the learner's profile. This authoring
process is time-consuming and unfamiliar to most non-technical authors. Therefore,
to ensure that students (of all ages, ability level and interests) can benefit from
Adaptive Educational Hypermedia, authoring tools need to be usable by a range of
educators. The overall aim of this thesis is therefore to identify the ways that this
authoring process can be simplified.
The research in this thesis describes the changes that were made to the My Online
Teacher (MOT) tool in order to address issues such as functionality and usability.
The thesis also describes usability and functionality changes that were made to the
GRAPPLE Authoring Tool (GAT), which was developed as part of a European FP7
project. These two tools (which utilise different authoring paradigms) were then
used within a usability evaluation, allowing the research to draw a comparison
between the two toolsets.
The thesis also describes how educators can reuse their existing non-adaptive
(linear) material (such as presentations and Wiki articles) by importing content into
an adaptive authoring system
Domain ontology learning from the web
El Aprendizaje de Ontologías se define como el conjunto de métodos utilizados para construir, enriquecer o adaptar una ontología existente de forma semiautomática, utilizando fuentes de información heterogéneas. En este proceso se emplea texto, diccionarios electrónicos, ontologías lingüísticas e información estructurada y semiestructurada para extraer conocimiento. Recientemente, gracias al enorme crecimiento de la Sociedad de la Información, la Web se ha convertido en una valiosa fuente de información para casi cualquier dominio. Esto ha provocado que los investigadores empiecen a considerar a la Web como un repositorio válido para Recuperar Información y Adquirir Conocimiento. No obstante, la Web presenta algunos problemas que no se observan en repositorios de información clásicos: presentación orientada al usuario, ruido, fuentes no confiables, alta dinamicidad y tamaño abrumador. Pese a ello, también presenta algunas características que pueden ser interesantes para la adquisición de conocimiento: debido a su enorme tamaño y heterogeneidad, se asume que la Web aproxima la distribución real de la información a nivel global. Este trabajo describe una aproximación novedosa para el aprendizaje de ontologías, presentando nuevos métodos para adquirir conocimiento de la Web. La propuesta se distingue de otros trabajos previos principalmente en la particular adaptación de algunas técnicas clásicas de aprendizaje al corpus Web y en la explotación de las características interesantes del entorno Web para componer una aproximación automática, no supervisada e independiente del dominio. Con respecto al proceso de construcción de la ontologías, se han desarrollado los siguientes métodos: i) extracción y selección de términos relacionados con el dominio, organizándolos de forma taxonómica; ii) descubrimiento y etiquetado de relaciones no taxonómicas entre los conceptos; iii) métodos adicionales para mejorar la estructura final, incluyendo la detección de entidades con nombre, atributos, herencia múltiple e incluso un cierto grado de desambiguación semántica. La metodología de aprendizaje al completo se ha implementado mediante un sistema distribuido basado en agentes, proporcionando una solución escalable. También se ha evaluado para varios dominios de conocimiento bien diferenciados, obteniendo resultados de buena calidad. Finalmente, se han desarrollado varias aplicaciones referentes a la estructuración automática de librerías digitales y recursos Web, y la recuperación de información basada en ontologías.Ontology Learning is defined as the set of methods used for building from scratch, enriching or adapting an existing ontology in a semi-automatic fashion using heterogeneous information sources. This data-driven procedure uses text, electronic dictionaries, linguistic ontologies and structured and semi-structured information to acquire knowledge. Recently, with the enormous growth of the Information Society, the Web has become a valuable source of information for almost every possible domain of knowledge. This has motivated researchers to start considering the Web as a valid repository for Information Retrieval and Knowledge Acquisition. However, the Web suffers from problems that are not typically observed in classical information repositories: human oriented presentation, noise, untrusted sources, high dynamicity and overwhelming size. Even though, it also presents characteristics that can be interesting for knowledge acquisition: due to its huge size and heterogeneity it has been assumed that the Web approximates the real distribution of the information in humankind. The present work introduces a novel approach for ontology learning, introducing new methods for knowledge acquisition from the Web. The adaptation of several well known learning techniques to the web corpus and the exploitation of particular characteristics of the Web environment composing an automatic, unsupervised and domain independent approach distinguishes the present proposal from previous works.With respect to the ontology building process, the following methods have been developed: i) extraction and selection of domain related terms, organising them in a taxonomical way; ii) discovery and label of non-taxonomical relationships between concepts; iii) additional methods for improving the final structure, including the detection of named entities, class features, multiple inheritance and also a certain degree of semantic disambiguation. The full learning methodology has been implemented in a distributed agent-based fashion, providing a scalable solution. It has been evaluated for several well distinguished domains of knowledge, obtaining good quality results. Finally, several direct applications have been developed, including automatic structuring of digital libraries and web resources, and ontology-based Web Information Retrieval