With the universal use of mobile computing devices, there has been a notable increase in the number of mobile applications developed for educational purposes. Gamification strategies offer a new set of tools to educators and, combined with the location services provided by those devices, allow the creation of innovative location-based mobile learning experiences. In this literature review, we conduct an analysis of educational mobile location-based games. The review includes articles published from January of 2010 to October of 2020, and from 127 records screened, 26 articles were analysed in full-text form. This analysis allowed us to answer the following six predefined research questions: Who are the target audiences for location-based games? In which subjects are location-based games most used? Which strategies are implemented with mobile devices to improve the student’s learning process? What are the main impacts of location-based games on students’ learning? What are the main challenges to the development of location-based games for education? What future tendencies and research opportunities can be identified from the analysis of the current state of the art?

This paper proposes the use of Particle Swarm Optimisers as a tool for data mining. To evaluate its usefulness, we empirically compare the performance of three variants of the Particle Optimiser with another evolutionary algorithm, namely a Genetic Algorithm, in rule discovery for classification tasks. Such tasks are considered core tools for Decision Support Systems in a widespread area, ranging from the industry, commerce, military and scientific fields. The data sources used here for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Optimisers are competitive with other evolutionary techniques, and can be successfully applied to more demanding problem domains.

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