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Teaching computer programming is a real challenge in the State University of Milagro (UNEMI), located in one of the least-developed zones in Ecuador, a non-WEIRD country (WEIRD stands for Western, Educated, Industrialized, Rich and Democratic). Despite the application of various learning strategies, the historical pass rate does not exceed 43%. To solve this problem, we have relied on visual programming languages, specifically Scratch. Scratch is an open source software to learn programming that has a strong assumption of the benefits of community work. A quasi-experiment conducted with 74 undergraduate students during the first semester of CS showed that: (1) Both groups (control and experimental) are homogeneous in terms of their demographic characteristics, previous academic performance and motivation (expectations) concerning the course; (2) Scratch is strongly accepted by students in the experimental group and concerning the learning process, both groups showed similar levels of satisfaction; (3) the experimental group showed a pass rate four times higher than the control group; (4) in general, student success is associated with having learned programming with Scratch. While limited, our results are an important step in our road to improve the learning of programming in a low social status area of Ecuador.

Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called ''variable mesh optimization'' (VMO), in which the set of nodes (potential solutions) are distributed as a mesh. This mesh is variable, because it evolves to maintain a controlled diversity (avoiding solutions too close to each other) and to guide it to the best solutions (by a mechanism of resampling from current nodes to its best neighbour). This proposal is compared with basic population-based meta-heuristics using a benchmark of multimodal continuous functions, showing that VMO is a competitive algorithm.

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In this paper we propose a new model of ACO called Two-Step Ant Colony System. The basic idea is to split the heuristic search performed by ants into two stages. We have studied the performance of this new algorithm for the Feature Selection Problem. Experimental results obtained show the Two-Step approach significantly improves the Ant Colony System in term of computation time needed.

In this report are used two data sets involving the main antidiabetic enzyme targets α‐amylase and α‐glucosidase. The prediction of α‐amylase and α‐glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α‐amylase and 1546 compounds in the case of α‐glucosidase are selected to develop the tree model. In the case of CT‐J48 have the better classification model performances for both targets with values above 80%–90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10‐fold cross‐validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double‐target approach for increasing the estimation of antidiabetic chemicals identification aimed by double‐way workflow in virtual screening pipelines.