@article {779, title = {El ecosistema de aprendizaje del estudiante universitario en la post-pandemia. Metodolog{\'\i}as y herramientas}, journal = {Ense{\~n}anza y Aprendizaje de Ingenier{\'\i}a de Computadores}, number = {10}, year = {2020}, abstract = {La transici{\'o}n de una modalidad de ense{\~n}anza tradicional y presencial a una de tipo remoto, provocada por el confinamiento a ra{\'\i}z del coronavirus SARS-CoV-2, ha implicado cambios que han debido realizarse de manera acelerada y que afectan no solo al desarrollo de las clases, sino tambi{\'e}n a las actividades pr{\'a}cticas en laboratorio, de comunicaci{\'o}n y de evaluaci{\'o}n de las competencias de los estudiantes. En este trabajo exponemos c{\'o}mo hemos afrontado dicha transici{\'o}n en asignaturas del {\'a}rea Arquitectura y tecnolog{\'\i}a de computadores en la Universidad de Ja{\'e}n, as{\'\i} como la planificaci{\'o}n que hemos llevado a cabo para el pr{\'o}ximo curso 2020/2021 ante el nivel de incertidumbre sobre c{\'o}mo se desarrollar{\'a}.}, keywords = {Aprendizaje activo, Aprendizaje basado en proyectos, Metodolog{\'\i}as de aprendizaje, Modelos de evaluaci{\'o}n}, doi = {http://dx.doi.org/10.30827/Digibug.64779}, author = {Francisco Charte and A.J. Rivera-Rivas and Medina, J. and Espinilla, Macarena} } @conference {10.1007/978-3-319-48799-1_8, title = {Recognition of Activities in Resource Constrained Environments; Reducing the Computational Complexity}, booktitle = {Ubiquitous Computing and Ambient Intelligence}, year = {2016}, pages = {64{\textendash}74}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {In our current work we propose a strategy to reduce the vast amounts of data produced within smart environments for sensor-based activity recognition through usage of the nearest neighbor (NN) approach. This approach has a number of disadvantages when deployed in resource constrained environments due to its high storage requirements and computational complexity. These requirements are closely related to the size of the data used as input to NN. A wide range of prototype generation (PG) algorithms, which are designed for use with the NN approach, have been proposed in the literature to reduce the size of the data set. In this work, we investigate the use of PG algorithms and their effect on binary sensor-based activity recognition when using a NN approach. To identify the most suitable PG algorithm four datasets were used consisting of binary sensor data and their associated class activities. The results obtained demonstrated the potential of three PG algorithms for sensor-based activity recognition that reduced the computational complexity by up~to 95~{\%} with an overall accuracy higher than 90~{\%}.}, isbn = {978-3-319-48799-1}, author = {Espinilla, M. and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and Medina, J. and Mart{\'\i}nez, L. and Nugent, C.}, editor = {Garc{\'\i}a, Carmelo R. and Caballero-Gil, Pino and Burmester, Mike and Quesada-Arencibia, Alexis} }