Gamification is usually understood as a pedagogical strategy that favors student engagement and motivation. Traditionally it is composed of dynamics, mechanics, and components. The purpose of this study was to compare Engineering and Economics and Social Sciences undergraduate students in their performance (grades), motivation, quality of assignments, participation, and emotion when their teachers used gamification as an innovative teaching method during the COVID-19 pandemic. Pearson correlations, Principal Component Analysis (PCA), and Mann–Whitney test were conducted. Additionally, four students were interviewed to describe the emotional downside of the lockdown. The main results indicate that there are higher positive relationships among variables in the Engineering undergraduate students rather than in Economics and Social Sciences and show that emotion poorly correlates with performance, especially for the Economics and Social Sciences students, as many have a negative attitude toward learning mathematics. Additionally, gender and scholarship status are not differential factors. Gamification proved to be a useful pedagogical strategy to promote participation and enhance motivation among undergraduate students, particularly in a context of academic confinement. This study gives teachers an idea of the benefits and extent to which gamification can be used in the classroom.

The idea behind hyper-heuristics is to discover rules that relate different problem states with the best single heuristic to apply. This investigation works towards extending the problem domain in which a given hyper-heuristic can be applied and implements a framework to generate hyper-heuristics for a wider range of bin packing problems. We present a GA-based method that produces general hyper-heuristics that solve a variety of instances of one- and two dimensional bin packing problem without further parameter tuning. The two-dimensional problem instances considered deal with rectangles, convex and non-convex polygons.

In various approaches for combinatorial optimization, a problem instance is represented by a numerical vector that summarizes to some extent the state of such instance. Such representation intends to include the most relevant features related to the instance and the problem domain. Previous approaches for hyper-heuristics have been relying in intuitive ways to determine the feature set. In this paper, a more general methodology for establishing an adequate problem-state representation is proposed. As an experimental environment to test the methodology, we employed the irregular case of the two-dimensional Bin Packing Problem (2D irregular BPP) and a GA-based solution model with training and testing phases. Our developed representation shows a significant improvement in performance with respect to a more conventional representation.