The Relationship between Teachers and Students in the Classroom: Communicative Language Teaching Approach and Cooperative Learning Strategy to Improve Learning

The aim of this research is to to investigate how a supportive relationship between teachers and students in the classroom can improve the learning process. By having a good relationship with students, teachers can offer to students chances to be motivated and feel engaged in the learning process. Students will be engaged actively in the learning instead of being passive learners. I wish to investigate how using communicative approach and cooperative learning strategies while teaching do affect and improve students’ learning performance. To achieve these goals qualitative data collection was used as the primary method. The results show that teachers and students value a supportive and caring relationship between them and that interaction is essential to the teacher-student relationship. This sense of caring and supporting from teachers motivates students to become a more interested learner. Students benefit and are motivated when their teachers create a safe and trustful environment. And also the methods and strategies teachers uses, makes students feel engaged and stimulated to participate in the learning process. The students have in their mind that a positive relationship with their teachers positively impacts their interest and motivation in school which contributes to the enhancement of the learning process.

Reference Cluster Normalization Improves Detection of Frontotemporal Lobar Degeneration by Means of FDG-PET.

Positron emission tomography with [18F] fluorodeoxyglucose (FDG-PET) plays a well-established role in assisting early detection of frontotemporal lobar degeneration (FTLD). Here, we examined the impact of intensity normalization to different reference areas on accuracy of FDG-PET to discriminate between patients with mild FTLD and healthy elderly subjects. FDG-PET was conducted at two centers using different acquisition protocols: 41 FTLD patients and 42 controls were studied at center 1, 11 FTLD patients and 13 controls were studied at center 2. All PET images were intensity normalized to the cerebellum, primary sensorimotor cortex (SMC), cerebral global mean (CGM), and a reference cluster with most preserved FDG uptake in the aforementioned patients group of center 1. Metabolic deficits in the patient group at center 1 appeared 1.5, 3.6, and 4.6 times greater in spatial extent, when tracer uptake was normalized to the reference cluster rather than to the cerebellum, SMC, and CGM, respectively. Logistic regression analyses based on normalized values from FTLD-typical regions showed that at center 1, cerebellar, SMC, CGM, and cluster normalizations differentiated patients from controls with accuracies of 86%, 76%, 75% and 90%, respectively. A similar order of effects was found at center 2. Cluster normalization leads to a significant increase of statistical power in detecting early FTLD-associated metabolic deficits. The established FTLD-specific cluster can be used to improve detection of FTLD on a single case basis at independent centers - a decisive step towards early diagnosis and prediction of FTLD syndromes enabling specific therapies in the future.

Motivation and content Language Integrated Learning : an impulse for Second Language Teaching

“Estudiantes motivados producen profesores motivados y viceversa” (Lesley Denham)La cita refleja el efecto recíproco que tiene el comportamiento del profesor en el compromiso de los estudiantes a lo largo del año y viceversa. Es sorprendente como, destacando las fortalezas de cada estudiante en lugar de sus debilidades, nunca comparándolos entre ellos sino con su propio rendimiento, puede despertar una motivación intrínseca en el estudiante, y una merecida satisfacción personal para el profesor.Sin embargo, no existen botones motivacionales mágicos que podamos pulsar y hacer que el alumno quiera aprender. Como profesores, tomar la iniciativa será crucial: dar a nuestros estudiantes el espacio suficiente para experimentar, realzar su autonomía, e intuir las respuestas a través de un proceso inductivo. En definitiva, hacerles protagonistas de su proceso de aprendizaje.Incluir AICLE en la clase de inglés es una metodología que nos ayudará a conseguirlo. Los estudiantes asocian AICLE con algo interesante y divertido, diferente a las sesiones teóricas. Como resultado, al utilizar la lengua, lo hacen movidos por sus sentimientos, aprendiendo de forma implícita.“Estudiants motivats produeixen professors motivats i viceversa” (Lesley Denham)La cita reflecteix l'efecte recíproc que té el comportament del professor en el compromís dels estudiants al llarg de l'any i viceversa. És sorprenent com, destacant les fortaleses de cada estudiant en lloc de les seves debilitats, mai comparant-los entre ells sinó amb el seu propi rendiment, pot despertar una motivació intrínseca a l'estudiant, i una merescuda satisfacció personal per al professor.No obstant això, no existeixen botons motivacionals màgics que puguem prémer i fer que l'alumne vulgui aprendre. Com a professors, prendre la iniciativa serà crucial: donar als nostres estudiants l'espai suficient per experimentar, realçar la seva autonomia, i intuir les respostes a través d'un procés inductiu. En definitiva, fer-los protagonistes del seu procés d'aprenentatge.Incloure AICLE en la classe d'anglès és una metodologia que ens ajudarà a aconseguir-ho. Els estudiants consideren AICLE interessant i divertit, diferent a les sessions teòriques. Com a resultat, en utilitzar la llengua, ho fan moguts pels seus sentiments, aprenent de forma implícita.

Prototip d'instal·lació d'un cluster de càlcul emprant programari lliure i de codi obert

Metodologia d'instal·lació d'un sistema operatiu i aplicacions orientada al seu ús en clusters de càlcul i enfocada principalment a la senzillesa de manteniment i a la compartició d'un màxim de programari entre equips.

Learning and adaptation in physical heterogeneous teams of robots

In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).

Standards and specifications to manage accessibility issues in e-learning

Standards and specifícations to manage accessibility issues in e-learning

Estándares para e-learning adaptativo y accesible

Currently there are many standards that deal with accessibility issues regarding users’ models, learning scenarios, interaction preferences, devices capabilities, metadata for specifying the delivery of any resource to meet users’ needs, and software accessibility and usability. It is difficult to understand the existing relationships between these standards, as each one represents a different viewpoint and thus has its own sets of goals and scope. This paper gives an overview on existing standards addressing accessibility, usability and adaptation issues in e-learning, and discusses their application to cope with the objectives of the A2UN@ project, which focuses on attending the accessibility and adaptation needs for ALL in Higher Education