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Paper aims: This paper presents a comparative evaluation of different forecasting methods using two artificial neural networks (Multilayer Perceptron network and Radial Basis Functions Neural Network) and the Gaussian process regression.Originality: Due to the current world scenario, solving economic problems has become extremely important. Artificial neural networks are one of the most promising tools to forecast economic trends and are being widely studied in economic analyses. Therefore, due to the concerns about the performance of different forecasting methods to solve economic problems, this study contributes with an example of the forecasting performance of artificial neural network models compared with Gaussian process regression using Nelson-Plosser and U.S. macroeconomic real-life data sets.Research method: Two real-life data sets were used to evaluate the forecasting methods proposed in this paper. These data sets were normalised to values between zero and one. After that, the data training was performed and, once it was built, a model was used to generate forecasts. Thus, observations were made to verify how accurately the fitted model forecast the values. Main findings:The results obtained from the study show that, for all forecasting horizons, multi-layer perceptron networks and Gaussian process regression models had the most satisfactory results. On the other hand, the radial basis functions neural network model was unsuitable for econometric data. Implications for theory and practice:This study contributes to a discussion about artificial neural networks and Gaussian process regression models for econometric forecasting. Although artificial neural networks are mainly used in economic analyses, the results showed that not all models, such as radial basis functions neural networks, present good results. In addition, the regression of the Gaussian process showed promising results to forecast econometric data.

Este artigo explora a “escolarização aberta” promovida pela União Europeia cujo foco é a coaprendizagem formal, não-formal e informal por meio da cooperação entre estudantes, cientistas e comunidades para resolver problemas reais da vida visando educação profissional sociocientífica e cidadania responsável. O objetivo deste estudo foi compreender as práticas, estratégias e necessidades de professores interessados em inovação educacional com tecnologias emergentes e escolarização aberta. Este estudo de métodos mistos foi apoiado por um instrumento reflexivo semiestruturado do projeto CONNECT de escolarização aberta. Este projeto visa empoderar jovens apoiados em pesquisa e inovação responsáveis, ciência-ação e “diversão emancipatória" – prazer intrínseco de aprender. Os participantes foram 34 professores de escolas de ensino médio, incluindo educação profissional, técnica e vocacional no Brasil, que concluíram um curso de extensão sobre o uso de tecnologias emergentes. Os resultados destacam vários desafios para professores ainda centrados no ensino tradicional transmissivo: ensinar habilidades de pesquisa com problemas da vida real; ajudar os estudantes a gerar perguntas com visões baseadas em evidências; avaliar o quão bem os estudantes usam as evidências para formar um argumento e elaborar narrativas científicas e promover discussão sobre ciência na sociedade em sala de aula. Além disso, quatro estratégias de ensino e aprendizagem dos professores precisam tornar-se mais frequentes para que estudantes possam: elaborar questões científicas sobre o tópico abordado; desenvolver projeto de investigação colaborativa; usar jogos colaborativos com divisão de papéis e dialogar sobre questões científicas atuais.

Paper aims: This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series.Originality: This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient.Research method: Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings:The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice:This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.