Abstract

Abstract
This work focuses on the motivation levels of introductory programming students and their relationship with their learning performance. The study involved students enrolled in the Introduction to Programming (IP) course included in two slightly different Informatics Engineering degrees at the same institution. The motivation section of the Motivated Strategies for Learning Questionnaire (MSLQ) instrument includes several scales and subscales used to analyse different motivational factors.Four research questions guided the study. The first is comparing the results of the two groups of students. The second considered the student's previous programming experience and tried correlating it with motivational factors. The third is similar but separates students following IP for the first time and those who had failed it in previous years. Finally, the fourth research question examined the influence of motivational factors on students' learning performance measured by their final grades.This paper provides a detailed study description and presents and discusses its results. © 2023 IEEE.

Abstract
The 'Scratch4All' project aims to reduce school dropout rates by encouraging and motivating students in the 1st, 2nd, and 3rd cycles of elementary schools to achieve academic success. In addition, the project promotes true equality of opportunity in terms of educational resources. A learning environment that contributes positively to the improvement of educational results is created through the use of new technologies, specifically programming in the Scratch language and robotics. Impact indicators were developed and used as project evaluation tools based on Theory of Change. This paper presents and analyzes the changes observed from the perspective of the project's teachers' stakeholders during the 2020/2021 school year. This group of beneficiaries is critical to the project's success and continuation, not only because of the special relationship they have with the students, but also with their families and the school. The assessment of impact is very positive in terms of indicators such as school success and motivation, particularly in Mathematics and Portuguese Language, as well as in the reduction of inequalities, access to new technologies, and contribution to gender equality. © 2023 ITMA.

Abstract
In a global world, within the context of an unprecedented climate crisis, STEM education may decisively contribute to sustainable economic growth. Within this context, Portugal has been following EU guidelines by creating programs that encourage students and teachers to envision STEM education with innovative methodologies. Today's schools should provide students with motivating approaches to increase their interest in enrolling STEM courses and professions. Despite all the efforts, the results are still far from the desired goals in some areas, such as electrical engineering. In this paper, an articulation program between Coimbra Institute of Engineering (ISEC), secondary schools, and a Private Social Solidarity Institution - CASPAE, being developed under the PO ISE Program, co-financed by EU, is described. The main objective of this program is to promote STEM subjects next to young students, in an interesting, experimental and interactive environment. The program proposes several experiments that are closely related to renewable energies and sustainable energy use, in which STEM knowledge is mandatory. With technical support from ISEC, a set of interactive experiments of real-world problems was developed. To make each experiment more appealing, a multidisciplinary approach was used, bringing together experts in electrical engineering, computer science, and art designers. Four experiments called "Energy Rivers", "A Breath of Wind", "Sun Flower"and "Kilometer by Kilometer"integrate a physical prototype and a simulator of a Hydroelectric Power Plant, a Photovoltaic Panel, a Wind Turbine, and an Electrical Vehicle, respectively. Each experiment is integrated into an attractive design that suggests the purpose of the experiment, enclosed in portable modules. The set of experiments will travel to schools, thus increasing the project audience target. © 2023 Fontys University of Applied Science.

Abstract
Computational thinking is a fundamental competence for the 21st century. It refers to a set of capacities and skills that can be stimulated to facilitate the teaching-learning process in a wide range of fields, including Science, Technology, Engineering and Mathematics (STEM). Experts in information technology argue that the earlier children are exposed to programming through digital platforms appropriate for their age, the easier it will be for them to assimilate their concepts in the future. This effort should be continued throughout the educational stages of children and youth to increase students' interest in pursuing STEM studies and careers. This paper describes the Scratch4All project promoted by the consortium CASPAE ( a Private Social Solidarity Institution) and Inova-Ria, with technical assistance from professors at the public higher education institution Coimbra Institute of Engineering. Scratch4All Project includes the activities Scratch on Road, Programming and Robotics Lab, and the Scratch4All Digital Platform. According to the impact assessment for the school year 2020-2021, the Scratch4All project promotes school success and true equality in access to new technologies for students in the 1st, 2nd, and 3rd cycles of elementary school, developing essential skills for their academic and professional future such as computational thinking, STEM competencies and social skills. By encouraging young girls to participate in technological projects, this project also aims to combat gender stereotypes.

Abstract
Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support and effort. This study investigates the use of a supervised machine learning model, a Multi-Layer Perceptron (MLP), for anomaly detection in microservices. The study covers the creation of a microservices infrastructure, the development of a fault injection module that simulates application-level and service-level anomalies, the creation of a system monitoring dataset, and the creation and validation of the MLP model to detect anomalies. The results indicate that the MLP model effectively detects anomalies in both domains with higher accuracy, precision, recovery, and F1 score on the service-level anomaly dataset. The potential for more effective distributed system monitoring and management automation is highlighted in this study by focusing on service-level metrics such as service response times. This study provides valuable information about the effectiveness of supervised machine learning models in detecting anomalies across distributed software systems.