O programa institucional de bolsa de iniciação à docência PIBID na formação inicial de professores de matemática

This research sought to understand the space training provided by Institutional Scholarship Program Initiation of Teaching to a group of students of Degree in Mathematics that had activities developed in the same public school. The goal is to qualify them for teaching practice for these basic institutions. We decided to conduct a qualitative study of type ethnographic case study. For a year and a half while we were at the meetings and activities of the Group, we did what we call as a participant observation. To obtain the data, we used different survey instruments: the researcher\'s field notes through his observation of everyday life of the group, photographs and filming of the activities, document analysis and database produced, physically and digitally, in addition to questionnaires and interviews with records written, which complemented each other and helped establish a triangulation of information collected. We analyze the trajectory of the group on three axes: on the first, we present and understand the paths taken by the Group in the process of setting up training spaces, and production of their professional training, in the second, we analyze how the space of PIBID is being integrated with others spaces of formations in the educational institution of the degree course in mathematics and, in the third axis, we understand the process of knowledge production of that group. The trajectory taken by the group was marked by a process of reflection and discussion systematic and collective, which favored the pursuit for be a better professional and also confirmed a possible path to be followed in initial teacher education.

Seleção de características para identificação de diferentes proporções de tipos de fibras musculares por meio da eletromiografia de superfície

Skeletal muscle consists of muscle fiber types that have different physiological and biochemical characteristics. Basically, the muscle fiber can be classified into type I and type II, presenting, among other features, contraction speed and sensitivity to fatigue different for each type of muscle fiber. These fibers coexist in the skeletal muscles and their relative proportions are modulated according to the muscle functionality and the stimulus that is submitted. To identify the different proportions of fiber types in the muscle composition, many studies use biopsy as standard procedure. As the surface electromyography (EMGs) allows to extract information about the recruitment of different motor units, this study is based on the assumption that it is possible to use the EMG to identify different proportions of fiber types in a muscle. The goal of this study was to identify the characteristics of the EMG signals which are able to distinguish, more precisely, different proportions of fiber types. Also was investigated the combination of characteristics using appropriate mathematical models. To achieve the proposed objective, simulated signals were developed with different proportions of motor units recruited and with different signal-to-noise ratios. Thirteen characteristics in function of time and the frequency were extracted from emulated signals. The results for each extracted feature of the signals were submitted to the clustering algorithm k-means to separate the different proportions of motor units recruited on the emulated signals. Mathematical techniques (confusion matrix and analysis of capability) were implemented to select the characteristics able to identify different proportions of muscle fiber types. As a result, the average frequency and median frequency were selected as able to distinguish, with more precision, the proportions of different muscle fiber types. Posteriorly, the features considered most able were analyzed in an associated way through principal component analysis. Were found two principal components of the signals emulated without noise (CP1 and CP2) and two principal components of the noisy signals (CP1 and CP2 ). The first principal components (CP1 and CP1 ) were identified as being able to distinguish different proportions of muscle fiber types. The selected characteristics (median frequency, mean frequency, CP1 and CP1 ) were used to analyze real EMGs signals, comparing sedentary people with physically active people who practice strength training (weight training). The results obtained with the different groups of volunteers show that the physically active people obtained higher values of mean frequency, median frequency and principal components compared with the sedentary people. Moreover, these values decreased with increasing power level for both groups, however, the decline was more accented for the group of physically active people. Based on these results, it is assumed that the volunteers of the physically active group have higher proportions of type II fibers than sedentary people. Finally, based on these results, we can conclude that the selected characteristics were able to distinguish different proportions of muscle fiber types, both for the emulated signals as to the real signals. These characteristics can be used in several studies, for example, to evaluate the progress of people with myopathy and neuromyopathy due to the physiotherapy, and also to analyze the development of athletes to improve their muscle capacity according to their sport. In both cases, the extraction of these characteristics from the surface electromyography signals provides a feedback to the physiotherapist and the coach physical, who can analyze the increase in the proportion of a given type of fiber, as desired in each case.

Rede de aprendizagem em robótica : uma perspectiva educativa de trabalho com jovens

The present research sought to comprehend what is the development perspective of a collective work of educational robotics with high school students. The work started from the development activities Mathematics Sub Project of PIBID (Programa Institucional de Bolsa de Iniciação à Docência, Institutional Program of Initiation to Teaching Scholarship) in a school network from the state of Minas Gerais. The production process of data of this research was done through the follow up of high school students that participated in workshops robotics at the mentioned public school and were selected to continue the project at the Faculty of Mechanical Engineering in Federal University of Uberlândia (UFU). Subsequently, these students were involved in activities related to Robotics championships, elapsed through different spaces in public and private schools of basic education, University and Non-Governmental Organization. The data at the research were registered by photos, videos, field notes, documents produced by the participants and arising from internet like the social media Facebook, questionnaires and, mainly, interviews. At the analysis process of data the followed axes were constituted: Movement Learning Network with Robotics; The Different Roles at the Robotics Events and Experiences in Engineering and Technology. By this axes we understand what is the trajectory of the constitution process of a learning network in educational robotics that we find in expansion and consolidation. In this network the research participants performed different roles which left imprints responsible for their transformation. As a more evident imprint, we detected the robot construction and programming, which as for as they moved their studies forward, they developed the subject autonomy, collaboration, sharing and technological authorship.