Scaffolding through the network: analysing the promotion of improved online scaffolds among university students

This article explores how to enrich scaffolding processes among university students using specific Computer Supported Collaborative Learning –CSCL- software. A longitudinal case study was designed, in which eighteen students participated in a twelve-month learning project. During this period the students followed an instructional process, using the CSCL software to support and improve the students’ interaction processes, in particular the processes of giving and receiving assistance. Our research analyzed the evolution of the quality of the students’ interaction processes and the students’ learning results. The effects of the students’ participation in the CSCL environment have been described in terms of their development of affective, cognitive and metacognitive learning processes. Our results showed that the specific activities that students performed while working with the CSCL system triggered specific learning processes, which had a positive incidence on their learning results.

APOE status modulates the changes in network connectivity induced by brain stimulation in non-demented elders.

Behavioral consequences of a brain insult represent an interaction between the injury and the capacity of the rest of the brain to adapt to it. We provide experimental support for the notion that genetic factors play a critical role in such adaptation. We induced a controlled brain disruption using repetitive transcranial magnetic stimulation (rTMS) and show that APOE status determines its impact on distributed brain networks as assessed by functional MRI (fMRI).Twenty non-demented elders exhibiting mild memory dysfunction underwent two fMRI studies during face-name encoding tasks (before and after rTMS). Baseline task performance was associated with activation of a network of brain regions in prefrontal, parietal, medial temporal and visual associative areas. APOE ε4 bearers exhibited this pattern in two separate independent components, whereas ε4-non carriers presented a single partially overlapping network. Following rTMS all subjects showed slight ameliorations in memory performance, regardless of APOE status. However, after rTMS APOE ε4-carriers showed significant changes in brain network activation, expressing strikingly similar spatial configuration as the one observed in the non-carrier group prior to stimulation. Similarly, activity in areas of the default-mode network (DMN) was found in a single component among the ε4-non bearers, whereas among carriers it appeared disaggregated in three distinct spatiotemporal components that changed to an integrated single component after rTMS. Our findings demonstrate that genetic background play a fundamental role in the brain responses to focal insults, conditioning expression of distinct brain networks to sustain similar cognitive performance.

Conflicting claims problem associated with cost sharing of a network

A minimum cost spanning tree (mcst) problem analyzes the way to efficiently connect individuals to a source when they are located at different places. Once the efficient tree is obtained, the question on how allocating the total cost among the involved agents defines, in a natural way, a confliicting claims situation. For instance, we may consider the endowment as the total cost of the network, whereas for each individual her claim is the maximum amount she will be allocated, that is, her connection cost to the source. Obviously, we have a confliicting claims problem, so we can apply claims rules in order to obtain an allocation of the total cost. Nevertheless, the allocation obtained by using claims rules might not satisfy some appealing properties (in particular, it does not belong to the core of the associated cooperative game). We will define other natural claims problems that appear if we analyze the maximum and minimum amount that an individual should pay in order to support the minimum cost tree. Keywords: Minimum cost spanning tree problem, Claims problem, Core JEL classification: C71, D63, D71.

Present and future methodology for the implementation of decision support systems for traffic management

Real-time predictions are an indispensable requirement for traffic management in order to be able to evaluate the effects of different available strategies or policies. The combination of predicting the state of the network and the evaluation of different traffic management strategies in the short term future allows system managers to anticipate the effects of traffic control strategies ahead of time in order to mitigate the effect of congestion. This paper presents the current framework of decision support systems for traffic management based on short and medium-term predictions and includes some reflections on their likely evolution, based on current scientific research and the evolution of the availability of new types of data and their associated methodologies.