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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.