PIBID e ensino de línguas estrangeiras: o impacto no projeto Luz, câmera... action!

PIBID's subproject from the Letras course at a public university from the interior of Sao Paulo has, as a vision, the teaching of languages in a different way, where the culture is something to be known, not only mentioned, and, because of that, students feel close to the language learning process, for the language is not something to be learned just as grammar, it is, in fact, to be learned as something more complex than that, making the connection between student and language and its values. The students have, as an objective the knowledge and formation in teaching, by participation in public schools where they could put the theory learned during the Letras course in the university in practice with students that could benefit from learning new languages. The public school, mentioned in this research, offered the opportunity for the PIBID students to participate in a project that already existed in this school, where the students were supposed to produce a script based in a tale, and with the script, they were supposed to produce a short movie and a trailer. In 2014, in the first year of the participation of PIBID in the project, PIBID students were asked to choose a tale in the languages that are currently part of the subproject, for the students could use as a base to the production of the short movie. This project is called Luz, Câmera… Action! and the main objective of this research was to verify the participation of PIBID in the project. For such, it was used a semi-structured open questionnaire, which sought to investigate how students and supervisors from the school understood and analyzed PIBID's participation in the project

FLOWS OF INFORMATION AND LANGUAGE IN ORGANIZATIONAL SETTINGS

Information flows are formed naturally or formally induced in organizational settings, passing from the strategic level to operational level, reflecting, and impacting in the processes that make up the organization, including the decision-making process and therefore the action strategies of organization. The management of organizational environments based on information requires careful attention to various kinds of languages used for communication between sectors and employees of the organization, whose goal is to share, disseminate and socialize the information produced in this environment.

Different protocols of physical exercise produce different effects on synaptic and structural proteins in motor areas of the rat brain

The plastic brain responses generated by the training with acrobatic exercise (AE) and with treadmill exercise (TE) may be different. We evaluated the protein expression of synapsin I (SYS), synaptophysin (SYP), microtubule-associated protein 2 (MAP2) and neurofilaments (NF) by immunohistochemistry and Western blotting in the motor cortex, striatum and cerebellum of rats subjected to TE and AE. Young adult male Wistar rats were divided into 3 groups: sedentary (Sed) (n=15), TE (n=20) and AE (n=20). The rats were trained 3 days/week for 4 weeks on a treadmill at 0.6 km/h, 40 min/day (TE), or moved through a circuit of obstacles 5 times/day (AE). The rats from the TE group exhibited a significant increase of SYS and SYP in the motor cortex, of NF68, SYS and SYP in the striatum, and of MAP2, NF and SYS in the cerebellum, whereas NF was decreased in the motor cortex and the molecular layer of the cerebellar cortex. On the other hand, the rats from the AE group showed a significant increase of MAP2 and SYP in the motor cortex, of all four proteins in the striatum, and of SYS in the cerebellum. In conclusion, AE induced changes in the expression of synaptic and structural proteins mainly in the motor cortex and striatum, which may underlie part of the learning of complex motor tasks. TE, on the other hand, promoted more robust changes of structural proteins in all three regions, especially in the cerebellum, which is involved in learned and automatic tasks. (C) 2012 Elsevier B.V. All rights reserved.

Identification of literary movements using complex networks to represent texts

The use of statistical methods to analyze large databases of text has been useful in unveiling patterns of human behavior and establishing historical links between cultures and languages. In this study, we identified literary movements by treating books published from 1590 to 1922 as complex networks, whose metrics were analyzed with multivariate techniques to generate six clusters of books. The latter correspond to time periods coinciding with relevant literary movements over the last five centuries. The most important factor contributing to the distinctions between different literary styles was the average shortest path length, in particular the asymmetry of its distribution. Furthermore, over time there has emerged a trend toward larger average shortest path lengths, which is correlated with increased syntactic complexity, and a more uniform use of the words reflected in a smaller power-law coefficient for the distribution of word frequency. Changes in literary style were also found to be driven by opposition to earlier writing styles, as revealed by the analysis performed with geometrical concepts. The approaches adopted here are generic and may be extended to analyze a number of features of languages and cultures.

VIBRATIONAL ANALYSIS OF COORDINATION COMPOUNDS OF NICKEL (II): AN APPROACH TO THE TEACHING OF POINT GROUPS

VIBRATIONAL ANALYSIS OF COORDINATION COMPOUNDS OF NICKEL (II): AN APPROACH TO THE TEACHING OF POINT GROUPS. This paper presents an IR and Raman experiment executed during the teaching of the course "Chemical Bonds" for undergraduated students of Science and Technology and Chemistry at the Federal University of ABC, in order to facilitate and encourage the teaching and learning of group theory. Some key aspects of this theory are also outlined. We believe that student learning was more significant with the introduction of this experiment, because there was an increase in the discussions level and in the performance during evaluations. This work also proposes a multidisciplinary approach to include the use of quantum chemistry tools.

Effects of Methylphenidate on performance of a practical pistol shooting task: a quantitative electroencephalography (qEEG) study

Abstract Background The present study examined absolute alpha power using quantitative electroencephalogram (qEEG) in bilateral temporal and parietal cortices in novice soldiers under the influence of methylphenidate (MPH) during the preparatory aiming period in a practical pistol-shooting task. We anticipated higher bi-hemispheric cortical activation in the preparatory period relative to pre-shot baseline in the methylphenidate group when compared with the control group because methylphenidate has been shown to enhance task-related cognitive functions. Methods Twenty healthy, novice soldiers were equally distributed in control (CG; n = 10) and MPH groups 10 mg (MG; n = 10) using a randomized, double blind design. Subjects performed a pistol-shooting task while electroencephalographic activity was acquired. Results We found main effects for group and practice blocks on behavioral measures, and interactions between group and phases on electroencephalographic measures for the electrodes T3, T4, P3 and P4. Regarding the behavioral measures, the MPH group demonstrated significantly poorer in shooting performance when compared with the control and, in addition, significant increases in the scores over practice blocks were found on both groups. In addition, regarding the electroencephalographic data, we observed a significant increase in alpha power over practice blocks, but alpha power was significantly lower for the MPH group when compared with the placebo group. Moreover, we observed a significant decrease in alpha power in electrodes T4 and P4 during PTM. Conclusion Although we found no correlation between behavioral and EEG data, our findings show that MPH did not prevent the learning of the task in healthy subjects. However, during the practice blocks (PBs) it also did not favor the performance when compared with control group performance. It seems that the CNS effects of MPH demanded an initial readjustment period of integrated operations relative to the sensorimotor system. In other words, MPH seems to provoke a period of initial instability due to a possible modulation in neural activity, which can be explained by lower levels of alpha power (i.e., higher cortical activity). However, after the end of the PB1 a new stabilization was established in neural circuits, due to repetition of the task, resulting higher cortical activity during the task. In conclusion, MPH group performance was not initially superior to that of the control group, but eventually exceeded it, albeit without achieving statistical significance.

Formazione, apprendimento e nuove prospettive e-learning. Lo sviluppo delle nuove tecnologie didattiche e la creazione di sistemi di apprendimento lungo l’intero arco della vita

The thesis of this paper is based on the assumption that the socio-economic system in which we are living is characterised by three great trends: growing attention to the promotion of human capital; extremely rapid technological progress, based above all on the information and communication technologies (ICT); the establishment of new production and organizational set-ups. These transformation processes pose a concrete challenge to the training sector, which is called to satisfy the demand for new skills that need to be developed and disseminated. Hence the growing interest that the various training sub-systems devote to the issues of lifelong learning and distance learning. In such a context, the so-called e-learning acquires a central role. The first chapter proposes a reference theoretical framework for the transformations that are shaping post-industrial society. It analyzes some key issues such as: how work is changing, the evolution of organizational set-ups and the introduction of learning organization, the advent of the knowledge society and of knowledge companies, the innovation of training processes, and the key role of ICT in the new training and learning systems. The second chapter focuses on the topic of e-learning as an effective training model in response to the need for constant learning that is emerging in the knowledge society. This chapter starts with a reflection on the importance of lifelong learning and introduces the key arguments of this thesis, i.e. distance learning (DL) and the didactic methodology called e-learning. It goes on with an analysis of the various theoretic and technical aspects of e-learning. In particular, it delves into the theme of e-learning as an integrated and constant training environment, characterized by customized programmes and collaborative learning, didactic assistance and constant monitoring of the results. Thus, all the aspects of e-learning are taken into exam: the actors and the new professionals, the virtual communities as learning subjects, the organization of contents in learning objects, the conformity to international standards, the integrated platforms and so on. The third chapter, which concludes the theoretic-interpretative part, starts with a short presentation of the state-of-the-art e-learning international market that aims to understand its peculiarities and its current trends. Finally, we focus on some important regulation aspects related to the strong impulse given by the European Commission first, and by the Italian governments secondly, to the development and diffusion of e-learning. The second part of the thesis (chapters 4, 5 and 6) focus on field research, which aims to define the Italian scenario for e-learning. In particular, we have examined some key topics such as: the challenges of training and the instruments to face such challenges; the new didactic methods and technologies for lifelong learning; the level of diffusion of e-learning in Italy; the relation between classroom training and online training; the main factors of success as well as the most critical aspects of the introduction of e-learning in the various learning environments. As far as the methodological aspects are concerned, we have favoured a qualitative and quantitative analysis. A background analysis has been done to collect the statistical data available on this topic, as well as the research previously carried out in this area. The main source of data is constituted by the results of the Observatory on e-learning of Aitech-Assinform, which covers the 2000s and four areas of implementation (firms, public administration, universities, school): the thesis has reviewed the results of the last three available surveys, offering a comparative interpretation of them. We have then carried out an in-depth empirical examination of two case studies, which have been selected by virtue of the excellence they have achieved and can therefore be considered advanced and emblematic experiences (a large firm and a Graduate School).

Automatic induction of lexical features

This thesis concerns artificially intelligent natural language processing systems that are capable of learning the properties of lexical items (properties like verbal valency or inflectional class membership) autonomously while they are fulfilling their tasks for which they have been deployed in the first place. Many of these tasks require a deep analysis of language input, which can be characterized as a mapping of utterances in a given input C to a set S of linguistically motivated structures with the help of linguistic information encoded in a grammar G and a lexicon L: G + L + C → S (1) The idea that underlies intelligent lexical acquisition systems is to modify this schematic formula in such a way that the system is able to exploit the information encoded in S to create a new, improved version of the lexicon: G + L + S → L' (2) Moreover, the thesis claims that a system can only be considered intelligent if it does not just make maximum usage of the learning opportunities in C, but if it is also able to revise falsely acquired lexical knowledge. So, one of the central elements in this work is the formulation of a couple of criteria for intelligent lexical acquisition systems subsumed under one paradigm: the Learn-Alpha design rule. The thesis describes the design and quality of a prototype for such a system, whose acquisition components have been developed from scratch and built on top of one of the state-of-the-art Head-driven Phrase Structure Grammar (HPSG) processing systems. The quality of this prototype is investigated in a series of experiments, in which the system is fed with extracts of a large English corpus. While the idea of using machine-readable language input to automatically acquire lexical knowledge is not new, we are not aware of a system that fulfills Learn-Alpha and is able to deal with large corpora. To instance four major challenges of constructing such a system, it should be mentioned that a) the high number of possible structural descriptions caused by highly underspeci ed lexical entries demands for a parser with a very effective ambiguity management system, b) the automatic construction of concise lexical entries out of a bulk of observed lexical facts requires a special technique of data alignment, c) the reliability of these entries depends on the system's decision on whether it has seen 'enough' input and d) general properties of language might render some lexical features indeterminable if the system tries to acquire them with a too high precision. The cornerstone of this dissertation is the motivation and development of a general theory of automatic lexical acquisition that is applicable to every language and independent of any particular theory of grammar or lexicon. This work is divided into five chapters. The introductory chapter first contrasts three different and mutually incompatible approaches to (artificial) lexical acquisition: cue-based queries, head-lexicalized probabilistic context free grammars and learning by unification. Then the postulation of the Learn-Alpha design rule is presented. The second chapter outlines the theory that underlies Learn-Alpha and exposes all the related notions and concepts required for a proper understanding of artificial lexical acquisition. Chapter 3 develops the prototyped acquisition method, called ANALYZE-LEARN-REDUCE, a framework which implements Learn-Alpha. The fourth chapter presents the design and results of a bootstrapping experiment conducted on this prototype: lexeme detection, learning of verbal valency, categorization into nominal count/mass classes, selection of prepositions and sentential complements, among others. The thesis concludes with a review of the conclusions and motivation for further improvements as well as proposals for future research on the automatic induction of lexical features.

Uno Two Drei: la televisione nella promozione linguistica per i giovani. Un caso italiano nel contesto europeo.

The aim of this work is to assess the effectiveness of a project carried out in the Autonomous Province of Trento to promote language learning among young people. The project consisted in a television quiz show featuring games in Italian, English and German. Eight classes from five high schools took part in the game show, which was aired between November 2014 and January 2015 by the local channel Trentino TV. Since multilingualism has been part of the European Union policy, legislation and practices for many decades, the first chapter of this dissertation offers an overview of the main EU language education policies which seek to promote language learning among all citizens. It then focuses on the commitment of the EU and its member States to find new strategies to show the benefits of language learning outside formal education, also using both old and new media. Some TV and radio shows on multilingualism broadcast in Italy and in other EU countries are also briefly described. The second chapter is devoted to Uno Two Drei, the quiz show created by the Autonomous Province of Trento and Trentino TV to promote the learning of English and German among students while enhancing their personal growth and development. The role of everyone involved in the project is outlined, as well as the concept and planning phases and the realization stage. The third chapter focuses largely on the impacts of the initiative on the target group, namely 17-year-old students. Opinions of the participants were first collected in January, when a final meeting was held between the organizers and the schools. Since the researcher has managed the German part of the project, in the following months a questionnaire was administered to the 48 students who played in the German team and to their teachers as well. From both the meeting and the questionnaires it has emerged that the main goals of the initiative were achieved: generally speaking, students have been motivated to study languages and learn new things, the bonds between them have been strengthened and their sense of solidarity has been enhanced.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Reinforcement learning in dendritic structures

The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.

Reinforcement learning in dendritic structures

The discovery of binary dendritic events such as local NMDA spikes in dendritic subbranches led to the suggestion that dendritic trees could be computationally equivalent to a 2-layer network of point neurons, with a single output unit represented by the soma, and input units represented by the dendritic branches. Although this interpretation endows a neuron with a high computational power, it is functionally not clear why nature would have preferred the dendritic solution with a single but complex neuron, as opposed to the network solution with many but simple units. We show that the dendritic solution has a distinguished advantage over the network solution when considering different learning tasks. Its key property is that the dendritic branches receive an immediate feedback from the somatic output spike, while in the corresponding network architecture the feedback would require additional backpropagating connections to the input units. Assuming a reinforcement learning scenario we formally derive a learning rule for the synaptic contacts on the individual dendritic trees which depends on the presynaptic activity, the local NMDA spikes, the somatic action potential, and a delayed reinforcement signal. We test the model for two scenarios: the learning of binary classifications and of precise spike timings. We show that the immediate feedback represented by the backpropagating action potential supplies the individual dendritic branches with enough information to efficiently adapt their synapses and to speed up the learning process.

Retraining of interjoint arm coordination after stroke using robot-assisted time-independent functional training

We have developed a haptic-based approach for retraining of interjoint coordination following stroke called time-independent functional training (TIFT) and implemented this mode in the ARMin III robotic exoskeleton. The ARMin III robot was developed by Drs. Robert Riener and Tobias Nef at the Swiss Federal Institute of Technology Zurich (Eidgenossische Technische Hochschule Zurich, or ETH Zurich), in Zurich, Switzerland. In the TIFT mode, the robot maintains arm movements within the proper kinematic trajectory via haptic walls at each joint. These arm movements focus training of interjoint coordination with highly intuitive real-time feedback of performance; arm movements advance within the trajectory only if their movement coordination is correct. In initial testing, 37 nondisabled subjects received a single session of learning of a complex pattern. Subjects were randomized to TIFT or visual demonstration or moved along with the robot as it moved though the pattern (time-dependent [TD] training). We examined visual demonstration to separate the effects of action observation on motor learning from the effects of the two haptic guidance methods. During these training trials, TIFT subjects reduced error and interaction forces between the robot and arm, while TD subject performance did not change. All groups showed significant learning of the trajectory during unassisted recall trials, but we observed no difference in learning between groups, possibly because this learning task is dominated by vision. Further testing in stroke populations is warranted.

Different Learning Curves for Axillary Brachial Plexus Block: Ultrasound Guidance versus Nerve Stimulation

Little is known about the learning of the skills needed to perform ultrasound- or nerve stimulator-guided peripheral nerve blocks. The aim of this study was to compare the learning curves of residents trained in ultrasound guidance versus residents trained in nerve stimulation for axillary brachial plexus block. Ten residents with no previous experience with using ultrasound received ultrasound training and another ten residents with no previous experience with using nerve stimulation received nerve stimulation training. The novices' learning curves were generated by retrospective data analysis out of our electronic anaesthesia database. Individual success rates were pooled, and the institutional learning curve was calculated using a bootstrapping technique in combination with a Monte Carlo simulation procedure. The skills required to perform successful ultrasound-guided axillary brachial plexus block can be learnt faster and lead to a higher final success rate compared to nerve stimulator-guided axillary brachial plexus block.