A better understanding of the relationships between Computational Thinking and disciplines already present in the school curriculum may help the identification of possible educational benefits. In this sense, this article presents a Systematic Literature Review (SLR) that includes studies published between 2006 and 2014. The 48 included studies describe and evaluate didactic activities that develop Computational Thinking together with skills or contents related to Math. A wide variety of mathematical topics is being developed, with some emphasis on Algebra, Calculus and also higher-order thinking skills. In the last two years there was an increase in the number of activities focused on basic educational levels. Also, more rigorous methodological procedures have been used to evaluate learning effects. On the other hand, there are few studies focused on Math modelling and teacher training.
Resumo. Uma melhor compreensão das relações entre o Pensamento Computacional e disciplinas já presentes no currículo da educação básica pode contribuir para a identificação de possíveis benefícios educacionais. Dessa forma, este artigo apresenta uma Revisão Sistemática da Literatura (RSL), incluindo 48 estudos publicados em língua inglesa entre 2006 e 2014 que apresentam atividades didáticas desenvolvendo o PensamentoComputacional e competências, habilidades ou conteúdos da Matemática. Vários tópicos matemáticos são desenvolvidos, com predominância da Álgebra, Cálculo e habilidades cognitivas de alto nível. Verifica-se ainda, nos últimos dois anos, um aumento do desenvolvimento de experiências na educação básica e um maior rigor metodológico na avaliação dos efeitos de aprendizagem. Por outro lado, há uma carência de estudos relacionados à Modelagem Matemática e à formação de professores.
The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is related to new coronavirus disease (COVID-19) has mobilized several scientifics to explore clinical data using soft-computing approaches. In the context of machine learning, previous studies have explored supervised algorithms to predict and support diagnosis based on several clinical parameters from patients diagnosed with and without COVID-19. However, in most of them the decision is based on a "black-box" method, making it impossible to discover the variable relevance in decision making. Hence, in this study, we introduce a non-supervised clustering analysis with neural network self-organizing maps (SOM) as a strategy of decision-making. We propose to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention. Based on SOM features (visual relationships between clusters and identification of patterns and behaviors), and using linear discriminant analysis , it was possible to detect a group of units of the map with a discrimination power around 83% to SARS-CoV-2-positive patients. In addition, we identified some variables in admission blood tests (Leukocytes, Basophils, Eosinophils, and Red cell Distribution Width) that, in combination had strong influence in the clustering performance, which could assist a possible clinical decision. Thus, although with limitations, we believe that SOM can be used as a soft-computing approach to support clinician decision-making in the context of COVID-19. Keywords Covid-19 diagnostic • SARS-CoV-2 • Self-organizing maps Communicated by Victor Hugo C. de Albuquerque.
The pervasiveness of computer devices in everyday situations poses a fundamental question about Computer Sciences as being part of those known as basic sciences. However, it would be more beneficial not to consider computation only as a technique, but instead as a way of reasoning and problem solving. Under this perspective, there are inherent relationships among the knowledge, skills and attitudes that emanate from this field and those ones commonly related to Math. This paper discusses the relationship between the so-named Computational Thinking and the foundations of Math Education, based on a literature review. Three groups of skills that can be jointly developed by both areas are identified and some challenges and implications for education inComputer Sciences are discussed.
Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model. This model proposes that emotions are distributed in a two-dimensional circular space. However, methods to determine the feature vector are highly susceptible to noise that may exist in the signal. In this article, a new method to adjust the classifier is proposed using metaheuristics based on the black hole algorithm. The method is aimed at obtaining results similar to those obtained with manual noise elimination methods. In order to evaluate the proposed method, the MAHNOB HCI Tagging Database was used. Results show that using the black hole algorithm to optimize the feature vector of the Support Vector Machine we obtained an accuracy of 92.56% over 30 executions.
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