Delimitaciones previas a la formación para el uso de las TIC en la enseñanza universitaria: funciones y competencias del docente en entornos virtuales

En esta comunicación se esclarecen funciones, roles, competencias y tareas del docente universitario en entornos virtuales de enseñanza y aprendizaje con el propósito de contribuir a mejorar el diseño de acciones formativas dirigidas a la capacitación del profesorado para este ejercicio Este resultado se obtiene del análisis de significativas referencias que tratan el tema y de la valoración del diseño de acciones formativas realizadas en universidades europeas que participan activamente de este propósito. El estudio constituye una acción del proyecto Elene-TT - elearning network for Teacher Training.

Redefining dropping out in online higher education: a case study from the UOC

In recent years, studies into the reasons for dropping out of higher education (including online education) have been undertaken with greater regularity, parallel to the rise in the relative weight of this type of education, compared with brick-and-mortar education. However, the work invested in characterising the students who drop out of education, compared with those who do not, appears not to have had the same relevance as that invested in the analysis of the causes. The definition of dropping out is very sensitive to the context. In this article, we reach a purely empirical definition of student dropping out, based on the probability of not continuing a specific academic programme following several consecutive semesters of "theoretical break". Dropping out should be properly defined before analysing its causes, as well as comparing the drop-out rates between the different online programmes, or between online and on-campus ones. Our results show that there are significant differences among programmes, depending on their theoretical extension, but not their domain of knowledge.

Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine

Psychophysical studies suggest that humans preferentially use a narrow band of low spatial frequencies for face recognition. Here we asked whether artificial face recognition systems have an improved recognition performance at the same spatial frequencies as humans. To this end, we estimated recognition performance over a large database of face images by computing three discriminability measures: Fisher Linear Discriminant Analysis, Non-Parametric Discriminant Analysis, and Mutual Information. In order to address frequency dependence, discriminabilities were measured as a function of (filtered) image size. All three measures revealed a maximum at the same image sizes, where the spatial frequency content corresponds to the psychophysical found frequencies. Our results therefore support the notion that the critical band of spatial frequencies for face recognition in humans and machines follows from inherent properties of face images, and that the use of these frequencies is associated with optimal face recognition performance.