1,280 research outputs found

    The future of technology enhanced active learning – a roadmap

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    The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap

    Towards a Semantic-based Approach for Modeling Regulatory Documents in Building Industry

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    Regulations in the Building Industry are becoming increasingly complex and involve more than one technical area. They cover products, components and project implementation. They also play an important role to ensure the quality of a building, and to minimize its environmental impact. In this paper, we are particularly interested in the modeling of the regulatory constraints derived from the Technical Guides issued by CSTB and used to validate Technical Assessments. We first describe our approach for modeling regulatory constraints in the SBVR language, and formalizing them in the SPARQL language. Second, we describe how we model the processes of compliance checking described in the CSTB Technical Guides. Third, we show how we implement these processes to assist industrials in drafting Technical Documents in order to acquire a Technical Assessment; a compliance report is automatically generated to explain the compliance or noncompliance of this Technical Documents

    Panning for gold: designing pedagogically-inspired learning nuggets

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    Tools to support teachers and learning technologists in the creation of effective learning designs are currently in their infancy. This paper describes a metadata model, devised to assist in the conception and design of new learning activities, that has been developed, used and evaluated over a period of three years. The online tool that embodies this model was not originally intended to produce runtime executable code such as IMS-LD, but rather focussed on assisting teachers in the thought processes involved in selecting appropriate methods, tools, student activities and assessments to suit the required learning objectives. Subsequently, we have modified the RELOAD editor such that the output from our tool can be translated into IMS-LD. The contribution of this paper is the comparison of our data model with that of IMS-LD, and the analysis of how each can inform the other

    AH 2004 : 3rd international conference on adaptive hypermedia and adaptive web-based systems : workshop proceedings part 2

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    Supporting the tutor in the design and support of adaptive e-learning

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    The further development and deployment of e-learning faces a number of threats. First, in order to meet the increasing demands of learners, staff have to develop and plan a wide and complex variety of learning activities that, in line with contemporary pedagogical models, adapt to the learners’ individual needs. Second, the deployment of e-learning, and therewith the freedom to design the appropriate kind of activities is bound by strict economical conditions, i.e. the amount of time available to staff to support the learning process. In this thesis two models have been developed and implemented that each address a different need. The first model covers the need to support the design task of staff, the second one the need to support the staff in supervising and giving guidance to students' learning activities. More specifically, the first model alleviates the design task by offering a set of connected design and runtime tools that facilitate adaptive e-learning. The second model alleviates the support task by invoking the knowledge and skills of fellow-students. Both models have been validated in near-real-world task settings
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