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Abstract. Context management in pervasive computing environments must reflect the specific characteristics of these environments, e.g. distribution, mobility, resource-constrained devices, or heterogeneity of context sources. Although a number of context models have been presented in the literature, none of them supports all of these requirements to a sufficient extent at the same time. In this paper, we present a comprehensive and integrated approach for context modeling in pervasive computing environments. It combines the advantages of existing approaches and addresses the need for supporting effective software development. The proposed context model follows an ontology-based approach and has three layers of abstraction, i.e. conceptual layer, exchange layer, and functional layer. This layered approach facilitates a model-driven development of context-aware applications. Throughout the paper we compare our solution with the related work in order to clearly demonstrate why we needed to develop a new context management framework and where we have adopted existing ideas.

SUMMARY Although the idea of context‐awareness was introduced almost two decades ago, few mobile software applications are available today that can sense and adapt to their run‐time environment. The development of context‐aware and self‐adaptive applications is complex and few developers have experience in this area. On the basis of several demonstrators built by the joint European research project MUSIC, this paper describes typical context and adaptation features relevant for the development of context‐aware and self‐adaptive mobile applications. We explain how the demonstrators were realised using the open‐source platform MUSIC and present the feedback of the developers of these demonstrators. The main contribution of this paper is to show how the development complexity of context‐aware and self‐adaptive mobile applications can be mastered by using an adaptation framework such as MUSIC. Copyright © 2012 John Wiley & Sons, Ltd.

Dominar los procesos cognitivos individuales y usar estrategias de aprendizaje por parte de los universitarios mejora su rendimiento. Nos proponemos indagar en el aprendizaje del alumnado universitario atendiendo al género a través de un cuestionario EVAAU. Este instrumento, validado mediante juicio de expertos y análisis factorial confirmatorio, consta de 31 ítems que se responden a través de una escala Likert y que se encuentran agrupados en una estructura factorial de 5 dimensiones que inciden en los resultados del aprendizaje del universitario responsable del 67,31% del total de la varianza. Los resultados revelan diferencias significativas de género, donde la dimensión Recursos educativos y de aprendizaje tiene mayor incompatibilidad de igualdad de medias, y la Organización de los tiempos es la mejor valorada. Se concluye remarcando la importancia de las técnicas de estudio antes de comenzar la carrera y de cómo el factor motivacional y la implicación del profesorado influye en el rendimiento académico. Esta información contribuye a mejorar la planificación y el conocimiento del aprendizaje de los universitarios por parte de los docentes. La estructura del trabajo responde a una breve explicación teórica, al desarrollo metodológico del estudio y a la presentación de los resultados extraídos, para finalizar con la discusión y las conclusiones del estudio.