Most novice teachers and even some experienced teachers can lack appropriate tools for designing teaching strategies that ensure the quality of education. The ability of working in teams is crucial in educating professionals. The literature proves that social relations influence the performance of teams. For instance, the team cohesion is directly related with its performance. In the current work, we have developed an agent-based tool for assisting teachers in simulating their teaching strategies to estimate their influence on the group sociometrics like cohesion, coherence of reciprocal relations, dissociation and density of relations. The experiments with nine scenarios in disciplines of computer science, electronic, psychology, business, tourism and renewal energies with 239 students and six teachers including experienced and novice ones show that its underlying agentbased framework can adapt to different disciplines obtaining similar outcomes to the real ones. We learned that the tool was especially reliable in predicting the density of relations and the cohesion, being the latter one probably the most relevant due to its known relation with academic performance. In addition, we also learned that it was difficult to assess the prediction quality of the dissociation in higher education, due to the usual low amounts or absence of reciprocal rejections in the students' groups in this educational stage. The presented agent-based tool is publicly distributed as open source for facilitating other researchers in following this research line.
Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.
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