Catálogo de publicaciones - libros

In this paper, we describe a personalized recommender system that uses web mining techniques for recommending a student which (next) links to visit within an adaptable educational hypermedia system. We present a specific mining tool and a recommender engine that we have integrated in the AHA! system in order to help the teacher to carry out the whole web mining process. We report on several experiments with real data in order to show the suitability of using both clustering and sequential pattern mining algorithms together for discovering personalized recommendation links.

The success of technology enhanced learning () depends on the careful design of digital media and related communication/col-laboration features. Particulary when designing learning environments for professionals and scientists in non-technical disciplines it is crucial that the system is capable of reflecting the nature of learning discourses in these domains. For that purpose, it is necessary to fully understand the occuring digital media operations and knowledge sharing aspects involved in the learning process. environments therefore need to reflect the nature of the underlying community processes and their discourses. This paper describes a media theroretical approach to , which is capable of synthesizing media operations knowledge sharing aspects involved in . On top of this theoretical framing we introduce our system as a platform for in non-technical disciplines. As a proof of concept, the system is being presented in two different non-technical disciplines: Cultural heritage management and entrepreneurial training.

Education in architecture requires access to a broad range of learning materials to develop flexibility and creativity in design. The learning material is compromised of textual and visual media including images, videos, description of architectural concepts or projects, i.e. digital artifacts on different aggregation levels. The repositories storing such information are not interrelated and do not provide unified access so that retrieval of architectural learning objects is cumbersome and time consuming. In this paper, we describe how an infrastructure of federated architectural learning repositories will provide unique, integrated access facilities for high quality architectural content. The integration of various types of content, usage, social and contextual metadata enables users to develop multiple perspectives and navigation paths that support experience multiplication for the user. A standards–based, service–oriented software architecture, and flexible user interface design solutions, based on embeddable widgets, ensure easy integration and re-combinability of contents, metadata and functionalities.

To answer the challenges of globally distributed organizations facing constant transformation of work, means of ICT supported interorganizational knowledge-creation is needed. First, concept of organizational knowledge-creation is studied and further enriched with concepts of practice and object of activity to conceptualize the social and transforming nature of knowledge. Second, Change Laboratory is introduced as a theory based method of implementing local organizational knowledge-creation. An empirical case of Change Laboratory in a global organization network is presented and the envisioned ICT tools to support the transition from local to networked knowledge-creation are introduced.

We argue that current selection methods and comparison approaches of tools for learning purposes do not fit with the concept of e-learning 2.0 and Web 2.0 applications for creating personal learning environments. Available comparison methods being mainly as black-and-white check lists hinder to see the properties of various Web 2.0 applications. We propose a theoretical framework for designing a support tool for learners as well as for facilitators in order to facilitate the choice of learning/teaching tools from heterogeneous technological landscapes. Our theoretical framework is based on soft ontological approach enabling to compare systems and tools from multidimensional perspectives taking into account users best practices. We focus on elements of learning activities mediated by technology with respect to the concept of affordances and activity theory in combination of Alexander’s pattern approach and IMS LD case descriptions.

In this paper, an evaluation is presented of a framework that supports flexible content repurposing. Unlike the usual practice where content components, such as slides, images, definitions, text fragments, tables, or diagrams, are assembled manually through copy and paste, the framework enables on-the-fly access and repurposing. Retrieval of relevant components is enabled by automatic decomposition of legacy content and storage of individual components, enriched with metadata. Furthermore, the automatic assembly of these components in standard authoring tools is supported. The evaluation presented in this paper aims to assess the effectiveness and efficiency of such content reuse for presentations.

A critical success factor for the reuse of learning objects is the ease by which they may be repurposed in order to enable reusability in a different teaching context from which they were originally designed. The current generation of tools for creating, storing, describing and locating learning objects are best suited for users with technical expertise. Such tools are an obstacle to teachers who might wish to perform alterations to learning objects in order to make them suitable for their context. In this paper we describe a simple set of tools to enable practitioners to adapt the content of existing learning objects and to store and modify metadata describing the intended teaching context of these learning objects. We are deploying and evaluating these tools within the UK language teaching community.

In this paper, we present a (semi) automatic framework that aims to produce a domain concept map (DCM) from text and to derive a domain ontology from this concept map. This methodology targets particularly the educational field because of the need of such structures (Ontologies and CM) within the e-Learning and AIED communities to sustain the production of e-Learning resources tailored to learner’s needs. This paper details the steps that transform textual resources (and particularly textual learning objects) into a domain concept map and explains how this abstract structure is transformed into a more formal domain ontology. The paper also shows how these structures make it possible to bridge the gap between e-learning standard learning objects and Intelligent Tutoring Systems.

Information and Communication Technologies have transformed the way people work and have an increasing impact on the long life learning. Organizational Learning is an increasingly important area of research that concerns the way organizations learn and thus increase their competitive advantage, innovativeness, and effectiveness. Within the project MEMORAe2.0, our objective is to get ready students for their professional life, i.e. to learn to learn. To that end, we made the choice to consider a course as an organization and more precisely a learning organization. We developed the environment E-MEMORAe2.0 based on the model of an organizational memory to capitalize and to distribute knowledge and resources related to a course. By means of E-MEMORAe2.0, learners use, produce and exchange documents and knowledge. In this article, we present our approach of organizational learning in the project MEMORAe2.0 and we describe how we implemented it in the E-MEMORAe2.0 environment.

Folksonomies provide a free source of keywords describing web resources, however, these keywords are free form and unstructured. In this paper, we describe a novel tool that converts folksonomy tags into semantic metadata, and present a case study consisting of a framework for evaluating the usefulness of this metadata within the context of a particular eLearning application. The evaluation shows the number of ways in which the generated semantic metadata adds value to the raw folksonomy tags.