A catalogue of Primitive Scenario-Types : The First Step to the Automation of Learning Scenarios

Peer-reviewed

Metadata for describing learning scenarios under European Higher Education Area paradigm

In this paper we identify the requirements for creating formal descriptions of learning scenarios designed under the European HigherEducation Area paradigm, using competences and learning activities as the basic pieces of the learning process, instead of contents and learning resources, pursuing personalization. Classical arrangements of content based courses are no longer enough to describe all the richness of this new learning process, where user profiles, competences and complex hierarchical itineraries need to be properly combined. We study the intersection with the current IMS Learning Design specification and theadditional metadata required for describing such learning scenarios. This new approach involves the use of case based learning and collaborativelearning in order to acquire and develop competences, following adaptive learning paths in two structured levels.

Learning 2.0 : concepts and experiences

E-learning arises in all educative contexts and levels with the use of information and communication technologies and massive access to internet connected computers. On the other hand, the fast development of social networking tools and web 2.0 technologies are producing an evolution of e-learning towards what is called a learning 2.0 paradigm. In this short paper weshall present the main technologies and pedagogical issues related to that new way of learning and how we can use them to improve the acquisition of competences and new knowledge.

Interactive Calculus in a Virtual Learning Environment

The teaching of higher level mathematics for technical students in a virtual learningenvironment poses some difficulties, but also opportunities, now specific to that virtuality.On the other hand, resources and ways to do now manly available in VLEs might soon extend to all kinds of environments.In this short presentation we will discuss anexperience carried at Universitat Oberta deCatalunya (UOC) involving (an on line university), first, the translation of LaTeX written existent materials to a web based format(specifically, a combination of XHTML andMathML), and then the integration of a symbolic calculator software (WIRIS) running as a Java applet embedded in the materials, intending to achieve an evolution from memorising concepts and repetitive algorithms to understanding and experiment concepts and the use of those algorithms.

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 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

Feedback-related Brain Potential Activity Complies with Basic Assumptions of Associative Learning Theory

Feedback-related negativity (FRN) is an ERP component that distinguishes positive from negative feedback. FRN has been hypothesized to be the product of an error signal that may be used to adjust future behavior. In addition, associative learning models assume that the trial-to-trial learning of cueoutcome mappings involves the minimization of an error term. This study evaluated whether FRN is a possible electrophysiological correlate of this error term in a predictive learning task where human subjects were asked to learn different cueoutcome relationships. Specifically, we evaluated the sensitivity of the FRN to the course of learning when different stimuli interact or compete to become a predictor of certain outcomes. Importantly, some of these cues were blocked by more informative or predictive cues (i.e., the blocking effect). Interestingly, the present results show that both learning and blocking affect the amplitude of the FRN component. Furthermore, independent analyses of positive and negative feedback event-related signals showed that the learning effect was restricted to the ERP component elicited by positive feedback. The blocking test showed differences in the FRN magnitude between a predictive and a blocked cue. Overall, the present results show that ERPs that are related to feedback processing correspond to the main predictions of associative learning models. ■