Catálogo de publicaciones - libros

While the growing number of learning resources increases the choice for learners, it also makes it more and more difficult to find suitable courses. Thus, improved search capabilities on learning resource repositories are required. We propose an approach for learning resource search based on preference queries. A preference query does not only allow for hard constraints (like ’return lectures about Mathematics’) but also for soft constraints (such as ’I prefer a course on Monday, but Tuesday is also fine’). Such queries always return the set of optimal items with respect to the given preferences. We show how to exploit this technique for the learning domain, and present the Personal Preference Search Service (PPSS) which offers significantly enhanced search capabilities compared to usual search facilities for learning resources.

Any software development project is based on assumptions about the state of the world that probably will hold when it is fielded. Investigating whether they are true can be seen as an important task. This paper describes how an empirical investigation was designed and conducted for the EU funded APOSDLE project. This project aims at supporting informal learning during work. Four basic assumptions are derived from the project plan and subsequently investigated in a two-phase study using several methods, including workplace observations and a survey. The results show that most of the assumptions are valid in the current work context of knowledge workers. In addition more specific suggestions for the design of the prospective APOSDLE application could be derived. Though requiring a substantial effort, carrying out studies like this can be seen as important for longer term software development projects.

E-Learning grows on the fertile soil of the Internet technologies; it fails, however, to reach their full potential. With new, emerging technologies of the second generation Internet there is even more to be captured and adopted: knowledge sharing with blogs, wikis, and social bookmarking services. In this article we argue that those technologies can be adapted to improve user experience in e-Learning; we present an online social bookmarking system called Social Semantic Collaborative Filtering (SSCF). SSCF supports SIOC (Semantically-Interlinked Online Communities) metadata which ultimately transforms it in to a browser of blogs, fora, and other community sites. We show how a digital library system, such as JeromeDL, utilising this technology can be used in the e-Learning process, which takes advantage of recent research in the Internet.

During their lifecycle, Learning Resources undergo a multitude of processes while being created, used, provided or re-used. However, in order to be reusable, a Learning Resource often has to be adapted to a new context of use. This in turn implies multiple Re-Authoring processes being performed on the Learning Resource. During all these processes different types of information emerge. When captured, this information can be helpful for a later on retrieval, use or re-use of the Learning Resources. In this work, the lifecycle of Learning Resources along with the information being generated herein is analyzed and a distributed architecture is proposed, that allows the capturing, processing, management and utilization of the named information in a generic way.

We report on an ongoing project which aims at improving the effectiveness of retrieval and accessibility of learning object within learning management systems and learning object repositories. The project approaches this task by providing Language Technology based functionalities and by integrating semantic knowledge through domain-specific ontologies. We will report about the development of a keyword extractor and a domain-specific ontology, the integration of these modules into the learning management system ILIAS and the validation of these tools which assesses their added value in the scenario of searching learning objects across different languages.

This work introduces an approach to discover collaboration partners and adequate advising experts in a workplace-embedded collaborative e-learning environment. Based on existing papers dealing with work task and user context modelling, we propose the following steps towards a successful collaboration initiation. In the beginning, the user’s current process task needs to be identified (1). Taking into account the knowledge about the current process, availability of experts as well as organizational and social distance, relevant experts regarding the actual work task of the learner are pre-selected by the environment (2). Depending on the pre-selection and users’ preferences, the potential collaboration partners are displayed in an expert list (3). That way, the learner is able to initiate beneficial collaborations, whose transcripts are used to enhance the existing knowledge base of learning documents (4).

E-learning has been said to offer many benefits to learners, but to be effective, it requires the combination of a complex set of technological, pedagogical and organizational components. Integrating these very different aspects of e-learning can be challenging and may at times require the resolution of contradictory demands and conflicting needs. Yet, at this point, many universities are still trying to redesign their courses, professional practice and administrative procedures in order to address the emerging demands of this new approach. One matter of crucial importance to any e-learning initiative is the appropriate design and development of its learning environment. Instructional Systems Design (ISD) is the framework whereby the technological, pedagogical and organizational components are considered and brought together to create viable learning environments and programmes. This paper reports on research that identifies and assesses Critical Success Factors (CSFs) for ISD in order to develop coherent and consistent environments that can underpin the successful implementation of e-learning courses and programmes, thus providing an explicit theoretical foundation upon which decision-making and strategic thinking about the design and development of e-learning can be based.

In this paper we illustrate how to conceive, implement and play adaptive Units of Learning (UoLs) that embed educational videogames. For this purpose we describe UoL, with the game as a key feature. The UoL includes a pre-test whose outcome is used to adapt the game. The UoL also assesses the learning process using an in-game exam. This UoL has been modeled using IMS Learning Design (LD), and the embedded game has been developed using the < e − Adventure > educational game engine. This UoL may be deployed in any LD-compliant environment, although some of the features like the adaptation of the game or automatic assessment require special plug-ins that enable the communication between the environment and the < e − Adventure > engine. These plug-ins have been developed as an open-source modification of the SLeD player.

Technologies that solve the scarce availability of learning objects have created the opposite problem: abundance of choice. The solution to that problem is relevance ranking. Unfortunately current techniques used to rank learning objects are not able to present the user with a meaningful ordering of the result list. This work interpret the Information Retrieval concept of Relevance in the context of learning object search and use that interpretation to propose a set of metrics to estimate the Topical, Personal and Situational relevance. These metrics are calculated mainly from usage and contextual information. An exploratory evaluation of the metrics shows that even the simplest ones provide statistically significant improvement in the ranking order over the most common algorithmic relevance metric.

Learners and knowledge workers are increasingly facing environments where frequent interruptions, multi-tasking, information overload, and insufficient community awareness are the norm rather than the exception. It has been demonstrated that this situation hinders learning in several manners. This paper, after introducing two approaches aimed at supporting attentional processes, analyses the services needed to support learners in environments presenting the above characteristics. It also discusses the conceptual and technical problems related to the collection, modelling, protection, and distribution of attention-related information.