Iria Estévez-Ayres

TL;DR: In this article, the authors present an empirical study on the motivation and learning strategies of MOOC learners and find that learners were highly motivated and confident to do well in the course.

TL;DR: Two algorithms are proposed for the composition of QoS-aware service-based applications with temporal requirements: an exhaustive algorithm that computes the optimal service combination in terms of a figure of merit, suitable for offline composition; and an improved algorithm based on heuristics and partial figures of merit suitable for online composition.

TL;DR: The aim of this work is to compare different machine learning algorithms for sentiment analysis, using a real case study to check how the results can provide information about learners' emotions or patterns in the MOOC.

TL;DR: The results reveal that high self-regulators engage in non-linear navigation and approach MOOCs as an informal learning opportunity, and prefer setting specific goals based on knowledge development and control their learning through assignments.

TL;DR: A software framework containing a reconfiguration mechanism that provides realtime execution guarantees in intensive media processing consumer electronic devices and enhances the budget scheduling model to provide a time-partition that guarantees the availability of resources during transitions.