Learning 2.0 : concepts and experiences with social networks and software

Peer-reviewed

The joy of e-learning : redesigning the e-learning experience

Peer-reviewed

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed

A conceptual model for grid learning services automatic composition

Peer-reviewed

Combining Generic Programming and Service-Oriented Architectures for the Effective and Timely Development of Complex e-Learning Systems

Peer-reviewed

What leads people to keep on e-learning? An empirical analysis of users' experiences and their effects on continuance intention

User retention is a major goal for higher education institutions running their teaching and learning programmes online. This is the first investigation into how the senses of presence and flow, together with perceptions about two central elements of the virtual education environment (didactic resource quality and instructor attitude), facilitate the user¿s intention to continue e-learning. We use data collected from a large sample survey of current users in a pure e-learning environment along with objective data about their performance. The results provide support to the theoretical model. The paper further offers practical suggestions for institutions and instructors who aim to provide effective e-learning experiences.

El aprendizaje reflexivo en la formación inicial del profesorado: un modelo para aprender a enseñar matemáticas = Reflective learning in the early formation of teachers: a model to learn to teach maths

In this study we analize the application of the reflective learning during initial formation mathematics teachers. This model is based on the sociocultural theories of the human learning and assumes that the interaction and the contrast make possible the coconstruction and the active reconstruction of knowledge.In order to make the study, it was left from a sample of 29 teaching students. The qualitative analysis allowed to identify factors that facilitate the incorporation of the reflective learning in university teaching, as well as the degree of effectiveness of this model to learn to teach mathematics

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Network virtualisation is considerably gaining attentionas a solution to ossification of the Internet. However, thesuccess of network virtualisation will depend in part on how efficientlythe virtual networks utilise substrate network resources.In this paper, we propose a machine learning-based approachto virtual network resource management. We propose to modelthe substrate network as a decentralised system and introducea learning algorithm in each substrate node and substrate link,providing self-organization capabilities. We propose a multiagentlearning algorithm that carries out the substrate network resourcemanagement in a coordinated and decentralised way. The taskof these agents is to use evaluative feedback to learn an optimalpolicy so as to dynamically allocate network resources to virtualnodes and links. The agents ensure that while the virtual networkshave the resources they need at any given time, only the requiredresources are reserved for this purpose. Simulations show thatour dynamic approach significantly improves the virtual networkacceptance ratio and the maximum number of accepted virtualnetwork requests at any time while ensuring that virtual networkquality of service requirements such as packet drop rate andvirtual link delay are not affected.