Da representação social do ser professor da EJA à descoberta de seu aluno como referente

Evidence of learning object like representation to social teachings that active in the education of young and adult with the point of view of the ―To be teacher‖ in this modality of teaching, to direct the intention, this research is way, understand the existence of this representation in center acting teachers in the initial periods of the EJA in the Rio Grande do Norte and its reasoning the theories of social representation (MOSCOVICI, 1978, 2003; JODELET, 2001; ABRIC, 1998). We interviewed one hundred and ten (110) teachers who work at schools in the Metropolitan Regions of Natal. We use two procedures: focal group (GATTI, 2005) and multiple classification analysis MCA (ROAZZI, 1995).Thus us with the focal group, attended by eight (08) teachers and seek to know understanding their ideas about EJA, what was possible from the content analysis (BARDIN, 1977; FRANCO, 2007) of the following category: the teacher s view of the EJA context. Developing the MCA, we met twenty (20) teachers in the first stage, free-word association technique FAT (ABRIC, 1998), and ninety in the second stage, including the participants of the focal group. The results of this procedure were submitted to multidimensional analysis and content analysis. The first showed three facets: having and being teacher dimension (ideal), which was about the example teacher s characteristics and behaviors; teacher/ student relation which was about the difficulties and doubts of this relationship as well as its success; at last, conflicting dimension from/ with practice, based on the conflicts experienced by the teachers as EJA workers. Content analysis based on the theme organization from the interpreted data showed four categories: resources to be a teacher which also brought out the definition of an ideal teacher; talk about teaching which disclosed teachers thoughts about the knowledge and being a teacher; obstacles to EJA which showed situations and conditions that prejudice EJA development; and also admission as EJA teacher: viewing reasons which revealed the reasons why teachers went to EJA even though they were formed to deal with children. The conjoint analysis us evidenced the little the dominion of the teachers a participation these search at respect of origin, of the meaning of the character while the singular of EJA modality of teaching the conformation of the social representation from the ―To be‖ on the general vision dissociating with it of inexistence of a social representation of ―to be teacher of the EJA‖ white striking element in the reference at singularity that define the related modality of teaching

Mecanismos sócio-genéticos da representação social de trabalho docente por grupos de licenciandos de física e de química da Universidade Federal do Rio Grande do Norte

This research aims to understand the social representations Teaching Work in groups of undergraduate students of Physics and Chemistry of the Federal University of Rio Grande do Norte. For this, the proposal was based on the three theoretical and methodological consensus Carvalho (2012) in the explanation of socio-genetic mechanisms constituents of dynamic consensus that has functionality to your organization. It Was used to achieve this goal, the theoretical-epistemological Serge Moscovici (1978, 2003), Jodelet (2011), Wagner (1998,( 2011) and Carvalho (2012). The corpus analyzed results from a qualitative and quantitative research, developed in three stages. The first two (2) questionnaires to fifty (50) of each undergraduate course, a questionnaire and another profile for collection of free associations concerning motes inductors "Give Lesson," "Student" and "Teacher". The second step in the procedure Multiple Classifications, Roazzi (1995), aimed for another thirty (30) undergraduate students for each course, as well as Document Analysis of Educational Projects Curriculum courses in Physics and Chemistry. The data analysis of the first stage focused on descriptive statistics and frequency and average order of the words associated with motes inductors. The results from the Multiple Classification Procedure submitted to multidimensional analysis (MSA multidimensional scalogram analysis) and SSA (Similarity Structure Analysis), were interpreted by the theoretical and methodological proposal of the three consensus, supported by analysis of the rhetorical nature of justifications classifications and categorizations of words, boosted in times of application of Procedure Multiple Classification. The data revealed that the groups surveyed were the same Social Representation with specific dynamic consensual. Thinking Teaching Work for these groups it is considered in three dimensions: the BE-DO-HAVE of teaching. In the group of Physics consensus was clear semantic, which expressed a dynamic in which the interpretations of "Teaching Work" peacefully coexist on perceptions of two concepts: An identity around the "BE" "Teacher" or "BE" "Educator" and the other, how they think about professional development. The type of group consensus Chemistry pointed to a consensual logic hierarchical order in which the gradual between the elements of BE-DO-HAVE attested conflicts and disagreements about the perceptual object "Teaching Work", around what value most, whether they are the attributes of personal or professional-technical dimension of teaching, in the course of professional development. The thesis to explain the mechanisms of socio-genetic Representation Social Teaching Work by theoretical and methodological proposal was confirmed

A representação social de disciplinas didático-pedagógicas no contexto da formação inicial docente: um entremear de saberes

We seek, through this study, to analyze about social representation that the students of licentiate degree course of the Federal Institute of Education, Science and Technology of Rio Grande do Norte - IFRN - have about didactic-pedagogical subjects. We utilize the Social Representations Theory (MOSCOVICI, 2009; 2012; JODELET, 2001) as a theoretical and methodological contribution and as aim, we identify this social representation and understand how it is influenced by the formation of these undergraduates. So we developed the research under the seven undergraduate classroom courses offered by IFRN, namely: Biology, Spanish, Physics, Geography, Computer Science, Mathematics and Chemistry, covering units located both in the capital and in the countryside. While methodological approach we used the Procedure of Multiple Classification (PMC) - (ROAZZI, 1995), whose realization requires a set of words achieved through of Free Technique of Words Association - FTWA - (ABRIC, 1998). For this realization we have a total of one hundred twenty (120) participants, with thirty (30) in FTWA and other stage in the realization of free classification and directed that correspond to the MPR. Achieved the empirical data, we use the analysis of content (BARDIN, 2011; FRANCO, 2007) and multidimensional (ROAZZI, 1995) for the course of his interpretation. Finally, we identify the social representation of didactic and pedagogical subjects centered around the idea that it is through these subjects that can achieve the profile of "good professional" as one who gathers knowledge and attributes required for the full development of teaching involving capabilities it and characteristics that mark the sense of professionalism. Furthermore, we found that the anchoring social representation on the understanding that these disciplines "teach the teacher to be" in the image and its objectification of the "good teacher", ie, one that reaches through the training process and experiences, qualities that make it unique and able to carry full professional. Still see that the actions of the teacher trainer affects the way students perceive, assume and engage in the study of teaching and pedagogical subjects and it reflects significantly the social representation then created

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Multiple Resolution Image Classification

Binary image classifiction is a problem that has received much attention in recent years. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. We then apply that system to images at one-half through one-sixteenth resolution, and consider the corresponding error rates. In addition, we further observe generalization performance as it depends on the number of training images, and lastly, compare the system's best error rates to that of a human performing an identical classification task given teh same set of test images.

Probabilistic classification of acute myocardial infarction from multiple cardiac markers

Logistic regression and Gaussian mixture model (GMM) classifiers have been trained to estimate the probability of acute myocardial infarction (AMI) in patients based upon the concentrations of a panel of cardiac markers. The panel consists of two new markers, fatty acid binding protein (FABP) and glycogen phosphorylase BB (GPBB), in addition to the traditional cardiac troponin I (cTnI), creatine kinase MB (CKMB) and myoglobin. The effect of using principal component analysis (PCA) and Fisher discriminant analysis (FDA) to preprocess the marker concentrations was also investigated. The need for classifiers to give an accurate estimate of the probability of AMI is argued and three categories of performance measure are described, namely discriminatory ability, sharpness, and reliability. Numerical performance measures for each category are given and applied. The optimum classifier, based solely upon the samples take on admission, was the logistic regression classifier using FDA preprocessing. This gave an accuracy of 0.85 (95% confidence interval: 0.78-0.91) and a normalised Brier score of 0.89. When samples at both admission and a further time, 1-6 h later, were included, the performance increased significantly, showing that logistic regression classifiers can indeed use the information from the five cardiac markers to accurately and reliably estimate the probability AMI. © Springer-Verlag London Limited 2008.

Real-Time Multiple Event Detection and Classification Using Moving Window PCA

This paper proposes a method for the detection and classification of multiple events in an electrical power system in real-time, namely; islanding, high frequency events (loss of load) and low frequency events (loss of generation). This method is based on principal component analysis of frequency measurements and employs a moving window approach to combat the time-varying nature of power systems, thereby increasing overall situational awareness of the power system. Numerical case studies using both real data, collected from the UK power system, and simulated case studies, constructed using DigSilent PowerFactory, for islanding events, as well as both loss of load and generation dip events, are used to demonstrate the reliability of the proposed method.

Parkinsonian tremor identification with multiple local field potential feature classification

This paper explores the development of multi-feature classification techniques used to identify tremor-related characteristics in the Parkinsonian patient. Local field potentials were recorded from the subthalamic nucleus and the globus pallidus internus of eight Parkinsonian patients through the implanted electrodes of a Deep brain stimulation (DBS) device prior to device internalization. A range of signal processing techniques were evaluated with respect to their tremor detection capability and used as inputs in a multi-feature neural network classifier to identify the activity of Parkinsonian tremor. The results of this study show that a trained multi-feature neural network is able, under certain conditions, to achieve excellent detection accuracy on patients unseen during training. Overall the tremor detection accuracy was mixed, although an accuracy of over 86% was achieved in four out of the eight patients.

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.

3D MPRAGE improves classification of cortical lesions in multiple sclerosis.

BACKGROUND: Gray matter lesions are known to be common in multiple sclerosis (MS) and are suspected to play an important role in disease progression and clinical disability. A combination of magnetic resonance imaging (MRI) techniques, double-inversion recovery (DIR), and phase-sensitive inversion recovery (PSIR), has been used for detection and classification of cortical lesions. This study shows that high-resolution three-dimensional (3D) magnetization-prepared rapid acquisition with gradient echo (MPRAGE) improves the classification of cortical lesions by allowing more accurate anatomic localization of lesion morphology. METHODS: 11 patients with MS with previously identified cortical lesions were scanned using DIR, PSIR, and 3D MPRAGE. Lesions were identified on DIR and PSIR and classified as purely intracortical or mixed. MPRAGE images were then examined, and lesions were re-classified based on the new information. RESULTS: The high signal-to-noise ratio, fine anatomic detail, and clear gray-white matter tissue contrast seen in the MPRAGE images provided superior delineation of lesion borders and surrounding gray-white matter junction, improving classification accuracy. 119 lesions were identified as either intracortical or mixed on DIR/PSIR. In 89 cases, MPRAGE confirmed the classification by DIR/PSIR. In 30 cases, MPRAGE overturned the original classification. CONCLUSION: Improved classification of cortical lesions was realized by inclusion of high-spatial resolution 3D MPRAGE. This sequence provides unique detail on lesion morphology that is necessary for accurate classification.