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Dificuldade na aprendizagem de algoritmos é uma realidade enfrentada por estudantes de graduação da área de ciências exatas. O presente artigo investiga as causas do problema, descrevendo uma experiência que integrou ábaco, operações básicas da matemática e sistemas de numeração com o objetivo de explorar o raciocínio lógico dos estudantes e prepará-los para o estudo dos algoritmos. Foram utilizados na implementação Visualg e Free Pascal para verificar a influência do idioma do software na aprendizagem. O ambiente Moodle apoiou as discussões sobre o tema e os resultados da pesquisa apontaram avanços no desenvolvimento de algoritmos e programas de computador.

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In the context of smart home, it is very important to identify usage patterns of Internet of things (IoT) devices. Finding these patterns and using them for decision-making can provide ease, comfort, practicality, and autonomy when executing daily activities. Performing knowledge extraction in a decentralized approach is a computational challenge considering the tight storage and processing constraints of IoT devices, unlike deep learning, which demands a massive amount of data, memory, and processing capability. This article describes a method for mining implicit correlations among the actions of IoT devices through embedded associative analysis. Based on support, confidence, and lift metrics, our proposed method identifies the most relevant correlations between a pair of actions of different IoT devices and suggests the integration between them through hypertext transfer protocol requests. We have compared our proposed method with a centralized method. Experimental results show that the most relevant rules for both methods are the same in 99.75% of cases. Moreover, our proposed method was able to identify relevant correlations that were not identified by the centralized one. Thus, we show that associative analysis of IoT device state change is efficient to provide an intelligent and highly integrated IoT platform while avoiding the single point of failure problem.

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Emotions are part of human life, and they are present on several occasions, like decision making and in social interactions. Computational identification of emotions in texts can be useful in many applications, especially in distance learning courses. This research introduces an animated pedagogic agent, integrated to a Moodle virtual learning environment, with the objective of assisting the tutor in accompanying students, helping the students to acquire knowledge, identifying their emotions, and motivating the student to participate in activities and discussions. As a way of assessing students' emotional state, an experiment was conducted using real data from a completed course, involving students. The results obtained are promising, evidencing the importance of knowing the emotional state of the students, contributing to the learning process.

The evolution of smart things technologies caused the growth in the popularity of concepts such as smart homes and industry 4.0. The Internet of Things (IoT) is the paradigm that encompasses and give a base for these topics. The development of devices that are used in this paradigm requires knowledge of subjects such as programming, embedded cyber-physical systems, web protocols, networking and others. This paper proposes a method to make it easier for people who do not have this knowledge to create smart IoT devices. To achieve this goal, we decide to create a visual language based on blocks that automatically generate code to Internet of Things devices. This language gives support to design the behavior of devices, which is represented by a model of a finite state machine. This model is generated using the tool Graphviz, which is a graph generator. We created a compiler for this language using the compiler generator Coco/r. The compiler translates the block code into the C language which is one of the programming language recognised by the Arduino IDE. We advocate that this process is more intuitive than the normal development process. after conducting tests with users, the first evaluation about this method is that it can be useful for people who understand the base concepts of it. However, there is just a few data about tests, turning it into a not definitive conclusion.

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