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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