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One of the teacher's first goals should be to motivate students to learn. Involving students in the learning process using project-based learning could be a useful and powerful tool to prepare the students for them professional future. As part of a degree course in Biomedical Engineering, from a Portuguese University, students were asked to look at society and identify a possible failure related to the biomedical field. From the identification of this fault, the project consisted in the planning and its possible resolution. In this article we present the case study of a student's team, whose project was candidate and winner for a national prize in the field of health innovation. Despite the particularization of this case study, the students considered the experience innovative and simultaneously motivating. They also highlighted the added value of a project going beyond the classroom. Therefore, in addition to allowing them to obtain classification to the curricular unit, this type of teaching and learning could have a special impact on civil society.

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Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach has to find a solution inside a search space that contains every possible combination of robots and goals. This leads to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a new way to solve this kind of tasks efficiently. Previous works on MAP have proposed to factorize the problem to decrease the planning effort i.e. dividing the goals among the agents (robots). However, these techniques do not scale when the number of agents and goals grow. Also, in most real world scenarios with big maps, goals might not be reached by every robot so it has a computational cost associated. In this paper we propose a combination of robotics and planning techniques to alleviate and boost the computation of the goal assignment process. We use Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. Thus, specific information can be extracted to know which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to a multi-agent planning algorithm, the goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.

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This paper aims to explore and describe fundamental factors that influence the knowledge and use of Information and Communication Technologies (ICT), in particular of Educational Software (ES) as a tool, by teachers who teach mathematics in any portuguese teaching cycle. The adopted method has a descriptive and exploratory nature, focusing on a quantitative paradigm. The study participants were 96 teachers who teach mathematics, from various elementary and secondary schools, from different regions of Portugal, as well as from other countries where the portuguese curriculum is inserted. The questionnaire, the chi-square independence test and Cramer's V test were used as instruments. Data analysis was performed using SPSS (version 25) and Excel (Office 2016). The results suggest that the age, gender and length of service of mathematics teachers may be factors that influence the knowledge and use of Kahoot ES and that having training may be an essential condition for the use of Modellus and Scratch ESs. We did not find any relationship of dependence between having training and the use of the rule and compass ES by the respondents.

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A number of approaches have been proposed to identify service boundaries when decomposing a monolith to microservices. However, only a few use systematic methods and have been demonstrated with replicable empirical studies. We describe a systematic approach for refactoring systems to microservice architectures that uses static analysis to determine the system's structure and dynamic analysis to understand its actual behavior. A prototype of a tool was built using this approach (MonoBreaker) and was used to conduct a case study on a real-world software project. The goal was to assess the feasibility and benefits of a systematic approach to decomposition that combines static and dynamic analysis. The three study participants regarded as positive the decomposition proposed by our tool, and considered that it showed improvements over approaches that rely only on static analysis.

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