Abstract
O estudo analisou diversos documentos do curso de mestrado-integrado em Engenharia Elétrica e de Computadores de uma Universidade Pública Portuguesa. Avaliou-se quais são os drivers institucionais potencialmente responsáveis pela adoção de novas metodologias no curso. Realizou-se um estudo de caso com abordagem qualitativa baseada em métodos mistos: análise estatística aplicada a corpus textuais e complementada por análise de conteúdo. Como resultados emergiram duas classes de análise de conteúdo: competências e conhecimentos esperados dos estudantes; e aspectos da formação em Engenharia Elétrica e do ciclo de estudos. Identificaram-se sete drivers da adoção de novas metodologias no curso, com base nas teorias da difusão da inovação e teoria institucional: formação, desenvolvimento, competência, ciclo de estudos, novo, tecnologia e UC. Cada um destes drivers possui os seus próprios outcomes (resultados), 16 no total, que apresentam os efeitos percebidos pelos docentes, avaliadores de curso e coordenadores.

Abstract
While living in a digital era, both teachers and students of Engineering Courses were not ready for the drastic change associated with the Covid-19 first confinement (March 2020). This forced change from a presential mode to a fully on-line mode provided teaching/leaning difficulties as well as new opportunities. Moreover, as most engineering courses require laboratory practice, on-line teaching raised additional challenges. This paper reports two different experiences in two different Control Engineering university courses in the North of Portugal. The goal is to share some learning tools that are particularly relevant in the pandemic time we are living: pocket-sized laboratory kits that students can easily take home and experience real-world control contents; an open Mural that can serve as an exchange of knowledge. Perceptions received both from students and lecturers regarding these two experiments are presented.

Abstract
Innovative teaching-learning methodologies in the fields of Control, Automation and Robotics are of great interest to researchers, educators and students. Nowadays there is a wide range of technological options available that can be used to improve learning and motivate students in their knowledge acquisition and skills development. Concepts such as Pocket-Sized Labs, Virtual and Remote Labs, as well as Web-Based Learning, are increasingly included in the teaching-learning processes, where students are expected to acquire their knowledge as active and central elements in the entire process. This article focuses on the review of various teaching-learning methodologies in the fields of Control, Automation and Robotics, taking several aspects into account: the portability and low cost of devices and applications, the possibility of autonomous and distance learning and centering of the learning process in the student. The conclusions drawn allow us to state that it is possible to apply innovative, effective and motivating methodologies with tools, devices and applications that are both low-cost and easy to access. It can also be inferred that the future of teaching demands a radical departure from the traditional methodologies, as well as taking advantage of technologies and students' skills to use and put them into practice.

Abstract
Multiple traveling salesman problem (mTSP) is a variant of the famous and standard traveling salesman problem, an NP-hard problem in combinatorial optimization. This kind of problem can be solved using exact methods but usually results in high exponential computational complexities. Heuristics and metaheuristics are required to overcome this shortcoming. This study proposes a hybrid method based on the Genetic Algorithm, Ant Colony Optimization, and 2-opt to improve the solution. Computational results with some benchmark instances are provided and compared with other published studies. In three instances, the proposed technique provides better results than the best-known solutions reported in the literature.

Abstract
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Tras-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique's results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an Fl-Score of 81% in the final test set, concluding that students' marks somehow incorporate their living conditions.

Abstract
This article analyses the impact that educational robotics has on the learning and motivation of primary school students. The study was based on a set of activities developed during the school year, within the scope of the Programming and Robotics Club (PRC), at Agrupamento de Escolas de Monserrate (AEM). These activities involved 66 4th grade students attending two primary schools that belong to AEM. These activities addressed different subjects such as the Discovery of Electrical Continuity, Programming without a Computer and the Discovery of Robotics, among others. At the same time, the AEM Programming and Robotics Club participated in the national contest together with other clubs from the country. At the end of the activities, a questionnaire was applied to the participants, in order to assess the impact they had on these students. The results obtained were very positive, as the students said that the club and its activities are a valuable asset for their development, learning and motivation.