THE LEARNING-MARKET OF EDTECHS IN BRAZILIAN EDUCATION FROM THE IMPACT OF THE PANDEMIC OF COVID-19

The text seeks to discuss the impacts of the Covid-19 pandemic on the educational sector considering the deepening of learning-market. Remote education emerges as a solution by governments to reduce the consequences of the suspension of classes. The use of these resources in a centralized way expresses the interests of the learning market, since the financialization of education is deepened by the purchase of packages from EdTechs companies by the public sector. It appears that business sectors linked to some spheres of education, especially distance education through the sale of technological resources and with the work of EdTechs seek to carry out learning-market in an opportunistic way and without considering the future of state public education and students from popular layers.

SCIENCE TEACHING IN PANDEMIC TIMES: REFLECTIONS FROM TRAINING TEACHERS

The pandemic caused by the new coronavirus has caused significant impacts in all social segments, including Science Education. We developed this article of qualitative character, to understand the perceptions and implications of graduate students in Education for Science and Mathematics about the consequences of the pandemic for Science Education and teacher training. For this, we used as an instrument of collection an online questionnaire, with three open questions on the theme. The answers obtained were categorized and analyzed through content analysis, based on studies by Bardin (2011), from which two very evident reflective categories emerged: Science Teaching and technological innovations and transformations in the teacher training process. The results obtained in this study demonstrated several interpretations of the participants about this historical moment, the difficulties, challenges, and possibilities that can guide the theoretical, epistemological and practical discussions and reflections of the readers on the theme.

The Adaptation of a Design Research-Action for Social Innovation Within the Criminal Justice System in Times of Pandemic

Action research is identified as an intensely participatory methodology, as it is associated with an action or problem solving, so that researchers and participants work together to develop a process of change. When using it to develop social innovation processes through Design, it becomes even more participatory, as in this context it is necessary to empower the participants so that they themselves can solve problems or generate new opportunities. In this context, the article reveals how an investigation carried out through action research during the COVID19 pandemic had to be adapted. This investigation intends to favour the social reintegration of (ex) offenders through training courses for the different actors related to the problem. Although the participants are not adapted to codesign works and in a completely online way, the training sessions were evaluated as positive and ended up corroborating their replication. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Novel Approach to Differentiate COVID-19 Pneumonia in Chest X-ray

Radiological chest examinations like chest X-ray play a fundamental role in the fight against the outbreak of COVID-19 pneumonia, caused by the coronavirus strain SARS-Cov-2. This study aims to investigate classification models to differentiate chest X-ray images of COVID-19-based and typical pneumonia using hand-crafted radiomic features, understanding the distinctive radiographic features of COVID-19. A total of 136 segmented chest X-rays from two public databases were used to train and evaluate the classification methods. The PyRadiomics library was used to extract first and second-order statistical texture features in the right (R), left (L), and in superior, middle and bottom lung zones for each lung side. For performing feature selection, data was split in training (80%) and test (20%) sets. Stratified K-folds (K=5) was used within the training dataset for cross-validation. The most relevant radiomic features were selected after measuring validation accuracy and relative feature importance. Support vector machines (SVM), random forest (RF), AdaBoost (AB), and logistic regression (LR) were analyzed as potential classifiers. The AB model was the best discriminant method between features related to COVID-19-based when compared to typical pneumonia, using a model of lung segmentation in six distinct lung zones (AUC = 0.98). Our study shows a predominance of radiomic feature selection in the right lung, with a tendency to the upper lung zone.