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The Conflict Detection and Resolution Problem for Air Traffic Flow Management consists of deciding the best strategy for airborne aircraft so that there is guarantee that no conflict takes place, i.e., all aircraft maintain the minimum safety distance at every time instant. Two integer linear optimization models for conflict avoidance between any number of aircraft in the airspace are proposed, the first being a pure 0-1 linear which avoids conflicts by means of altitude changes, and the second a mixed 0-1 linear whose strategy is based on altitude and speed changes. Several objective functions are established. Due to the small elapsed time that is required for solving both problems, the approach can be used in real time by using state-of-the-art mixed integer linear optimization software.

Learning how to program in Primary Education has attracted significant research in recent years. It is unclear though how programming environments and languages should be adapted to children to achieve better learning and use, but one trend seems to be the use of Scratch. The question in this paper is what programming environment can be used to continue teaching programming to children who have already been taught Scratch for years. This paper's proposal is that students aged between 10 and 12 can benefit from interacting with a friendly learning companion using p-code such as Alcody. The hypothesis is that students (aged between 10 and 12) with a knowledge of Scratch will be able to significantly improve their scores by using a learning companion to teach them how to program even during the COVID-19 pandemic. To check the hypothesis, an experiment was carried out during the 2019/2020 academic year with 137 students in Ecuador. A significant improvement in the scores of the students was recorded together with high satisfaction.

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Several years ago we presented an experimental, discovery-learning approach to the active learning of greedy algorithms. This paper presents GreedEx, a visualization tool developed to support this didactic method. The paper states the design goals of GreedEx, makes explicit the major design decisions adopted, and describes its main characteristics in detail. It also describes the experience of use, the usability evaluations conducted, and the evolution of GreedEx in these years in response to the findings of the usability evaluations. Finally, the positive results obtained in an evaluation of educational effectiveness are shown. The paper has three main contributions. First, the GreedEx system itself is an innovative system for experimentation and discovery learning of greedy algorithms. Second, GreedEx is different from other visualization systems in its support to higher levels of learning, in particular evaluation tasks. Finally, GreedEx is an example of a medium-term research project, where an educational system was designed from explicit learning goals and was later refined in a user-centered design process involving instructors and students, before carrying out a successful evaluation of educational effectiveness.