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
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.

Abstract
The goal for a virtual reality (VR) training system is to enable trainees to acquire all the knowledge they need to perform effectively in a real environment. Such a system should provide an experience so authentic that no further real-world training is necessary, meaning that it is sufficient to train in VR. We evaluate the impact of a haptic thermal stimulus, which is of paramount importance to decision making, on trainees performance and knowledge acquisition. A thermal device was created to deliver the stimulus. As a proof of concept, a procedure from firefighter training is selected, in which sensing the temperature of a door with one's hand is essential. The sample consisted of 48 subjects divided among three experimental scenarios: one in which a virtual thermometer is used (visual stimulus), another in which the temperature is felt with the hand (thermal stimulus) and a third in which both methods are used (visual + thermal stimuli). For the performance evaluation, we measured the total time taken, the numbers of correctly executed procedures and identified neutral planes, the deviation from the target height, and the responses to a knowledge transfer questionnaire. Presence, cybersickness, and usability are measured to evaluate the impact of the haptic thermal stimulus. Considering the thermal stimulus condition as the baseline, we conclude that the significantly different results in the performance among the conditions indicate that the better performance in the visual-only condition is not representative of the real-life performance. Consequently, VR training applications need to deliver the correct stimuli for decision making.

Abstract
Through technological advents from Industry 4.0 and the Internet of Things, as well as new Big Data solutions, predictive maintenance begins to play a strategic role in the increasing operational performance of any industrial facility. Equipment failures can be very costly and have catastrophic consequences. In its basic concept, Predictive maintenance allows minimizing equipment faults or service disruptions, presenting promising cost savings. This paper presents a data-driven approach, based on multiple-instance learning, to predict malfunctions in End-of-Line Testing Systems through the extraction of operational logs, which, while not designed to predict failures, contains valid information regarding their operational mode over time. For the case study performed, a real-life dataset was used containing thousands of log messages, collected in a real automotive industry environment. The insights gained from mining this type of data will be shared in this paper, highlighting the main challenges and benefits, as well as good recommendations, and best practices for the appropriate usage of machine learning techniques and analytics tools that can be implemented in similar industrial environments. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Computational thinking is the thinking process involved in formulating problems to admit a computational solution. This article describes a study in which the code.org platform was used to develop computational thinking with Elementary school students. After proper introduction and contextualization, we describe the 198 students from 4th grade involved in the study, following the process of collecting and analyzing data from the code.org platform. We conclude with the evaluation carried out by the students. The main conclusion of this study is that code.org is a valid option for developing computational thinking with Elementary school students. Also, a reliable way for students to start solving real-life problems, stimulating the capacity for abstraction through simulated and experienced practice.