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Emotions are essential drivers of the learning process, influencing motivation, performance, and problem-solving abilities. In the field of Computer Science, students often struggle with negative emotions during programming activities, impacting their performance and project quality. To address this challenge, there is a growing need to introduce Computational Thinking skills at the pre-university level. This study focuses on understanding the emotions experienced by primary and secondary school students during Computational Thinking activities, involving both plugged and unplugged tools. A significant performance difference was observed between primary and secondary education levels, with the latter outperforming the former. The research identifies specific associations between concepts and emotions, highlighting age-related differences with younger students exhibiting more positive emotions. While gender-based disparities in Computer Science perception exist in secondary education, there are no corresponding distinctions in emotional responses. The study reveals a gender-based effect, with girls showing reduced emotional responses and lower Computational Thinking performance than boys. In summary, this research underscores the profound role of emotions in learning, providing essential insights for tailoring educational strategies considering gender-specific and programming concept-related factors. It also connects lower emotional reactions to inferior results, emphasizing the importance of heightened emotional engagement.

SCHOOLTHY: Automatic Menu Planner for Healthy and Balanced School Meals is a decision support tool that addresses the multi-objective menu planning problem in order to automatically produce meal plans for school canteens. Malnutrition is a widespread problem nowadays and is particularly serious when it affects children. In our environment, nutrition experts design healthy and balanced meal plans for children manually, which leaves significant room for improvement in terms of convenience and efficiency. SCHOOLTHY is presented herein as a proposal to improve and facilitate the work of these professionals. We focus on offering healthy and balanced meal plans that not only satisfy the recommended energy and nutrient intakes, but that also have a minimum cost and maximum variety of courses and food groups. Quantitative analyses that compare the meal plans yielded by SCHOOLTHY for meal plans designed by experts at hand and served in regional schools demonstrate the suitability of the proposal. Finally, we note that, thanks to its flexibility, SCHOOLTHY might be easily adapted to deal with other environments, such as hospitals, prisons and retirement homes, among others.

The transformation of traditional education into a Sensitive, Manageable, Adaptable, Responsive and Timely (SMART) education involves the comprehensive modernisation of all educational processes. For such a transformation, smart pedagogies are needed as a methodological issue while smart learning environments represent the technological issue, both having as an ultimate goal to cultivate smart learners. Smart learners need to develop 21st century skills so that they can become into smart citizens of our changing world. Technology and computers are an essential aspect for this modernisation, not only in terms of technological support for smart environments but also in terms of offering new methodologies for smart pedagogy and the development of smart skills. In this context, computational thinking appears as a promising mechanism to encourage core skills since it offers tools that fit learners’ interests and gives them the possibility to better understand the foundations of our ICT-based society and environments. In this work, we raise to make an effort to encourage the development of computational thinking as an opportunity to transform traditional pedagogies to smarter methodologies. We provide a general background about computational thinking and analyse the current state-of-the-art of smart education, emphasizing that there is a lack of smart methodologies which can support the training of 21st century smart skills. Finally, we provide —to those educators interested in pursuing the philosophy of smart education— information about initiatives devoted to the dissemination or promotion of computational thinking; existing tools or materials which support educators for the development of computational thinking among the students; and previous experiences and results about the application of computational thinking in educational environments.

Emotions play a crucial role in knowledge acquisition and can significantly impact motivation when studying a new field. Unfortunately, young people, especially girls, are often not drawn to Computer Science. To address this issue, we conducted an analysis of emotions among 8-9-year-old and 12-13-year-old students engaged in Computational Thinking activities, considering educational level, gender, and type of intervention. Our study sought to understand the lack of interest by examining the emotions present in primary and secondary school students. Hour-long in-person classes were conducted, focusing on Computational Thinking activities. We used the Developmental Channels Questionnaire, which includes 13 emotions rated on a Likert scale from 0 to 10, to measure emotions. The results showed that the predominant emotions were mostly positive and ambiguous, with low intensity negative emotions, particularly in primary education. Gender differences were observed only in secondary education, while in primary education, the differences were not significant. Girls demonstrated an emotional evolution when engaging in these activities, unlike boys. These findings provide valuable quantitative insights for primary and secondary school teachers. Understanding the emotions experienced can help guide effective teaching approaches. By addressing emotional factors, educators can enhance students' interest in computer science, thus fostering a more inclusive and engaging learning environment.

A multi-objective formulation of the Menu Planning Problem, which is termed the Multi-objective Menu Planning Problem, is presented herein. Menu planning is of great interest in the health field due to the importance of proper nutrition in today’s society, and particularly, in school canteens. In addition to considering the cost of the meal plan as the classic objective to be minimized, we also introduce a second objective aimed at minimizing the degree of repetition of courses and food groups that a particular meal plan consists of. The motivation behind this particular multi-objective formulation is to offer a meal plan that is not only affordable but also varied and balanced from a nutritional standpoint. The plan is designed for a given number of days and ensures that the specific nutritional requirements of school-age children are satisfied. The main goal of the current work is to demonstrate the multi-objective nature of the said formulation, through a comprehensive experimental assessment carried out over a set of multi-objective evolutionary algorithms applied to different instances. At the same time, we are also interested in validating the multi-objective formulation by performing quantitative and qualitative analyses of the solutions attained when solving it. Computational results show the multi-objective nature of the said formulation, as well as that it allows suitable meal plans to be obtained.

One of the main components of most modern Multi-Objective Evolutionary Algorithms (MOEAs) is to maintain a proper diversity within a population in order to avoid the premature convergence problem. Due to this implicit feature that most MOEAs share, their application for Single-Objective Optimization (SO) might be helpful, and provides a promising field of research. Some common approaches to this topic are based on adding extra—and generally artificial—objectives to the problem formulation. However, when applying MOEAs to implicit Multi-Objective Optimization Problems (MOPs), it is not common to analyze how effective said approaches are in relation to optimizing each objective separately. In this paper, we present a comparative study between MOEAs and Single-Objective Evolutionary Algorithms (SOEAs) when optimizing every objective in a MOP, considering here the bi-objective case. For the study, we focus on two well-known and widely studied optimization problems: the Knapsack Problem (KNP) and the Travelling Salesman Problem (TSP). The experimental study considers three MOEAs and two SOEAs. Each SOEA is applied independently for each optimization objective, such that the optimized values obtained for each objective can be compared to the multi-objective solutions achieved by the MOEAs. MOEAs, however, allow optimizing two objectives at once, since the resulting Pareto fronts can be used to analyze the endpoints, i.e., the point optimizing objective 1 and the point optimizing objective 2. The experimental results show that, although MOEAs have to deal with several objectives simultaneously, they can compete with SOEAs, especially when dealing with strongly correlated or large instances.