Abstract
Computer Programming Learners usually fail to get approved in introductory courses because solving problems using computers is a complex task. The most important reason for that failure is concerned with motivation; motivation strongly impacts on the learning process. In this paper we discuss how techniques like program animation, and automatic evaluation can be combined to help the teacher in Computer Programming courses. In the article, PEP system will be introduced to explain how it supports teachers in classroom and how it engages students on study sessions outside the classroom. To support that work, students' motivation was studied; to complement that study, a survey involving students attending the first year of Algorithms and Programming course of an Engineering degree was done. It is also presented a tool to analyse surveys, using association rules. © 2022 Paula Correia Tavares et al., published by De Gruyter.

Abstract
The Informatics Engineering degree at ISEP, aligned with international standards, was the first undergraduate degree in Portugal to be certified with EUR-ACE®. The programme emphasizes project-based learning, in which students, working in teams, develop interdisciplinary projects applying knowledge from all courses in each semester. A specific laboratory-project course coordinates an integrative project that aims to address complex problems. In the 2nd semester, two computer engineering courses (object-oriented programming and software engineering), and two mathematics courses (discrete mathematics and statistics) are involved, besides the laboratory/project course. This paper focuses on the integration of mathematics with informatics courses in this project, addressing real-world-like problems, bridging software engineering with mathematical topics. To assess the adopted PBL, enquiries were carried out among students. This approach fosters active learning and reinforces the relevance of mathematics within engineering, preparing students for job market demands. © 2026, IGI Global Scientific Publishing. All rights reserved.

Abstract
Sound-based uroflowmetry (SU) is a non-invasive technique emerging as an alternative to traditional uroflowmetry (UF) to calculate the voiding flow rate based on the sound generated by the urine impacting the water in a toilet, enabling remote monitoring and reducing the patient burden and clinical costs. This study trains four different machine learning (ML) models (random forest, gradient boosting, support vector machine and convolutional neural network) using both regression and classification approaches to predict and categorize the voiding flow rate from sound events. The models were trained with a dataset that contains sounds from synthetic void events generated with a high precision peristaltic pump and a traditional toilet. Sound was simultaneously recorded with three devices: Ultramic384k, Mi A1 smartphone and Oppo Smartwatch. To extract the audio features, our analysis showed that segmenting the audio signals into 1000 ms segments with frequencies up to 16 kHz provided the best results. Results show that random forest achieved the best performance in both regression and classification tasks, with a mean absolute error (MAE) of 0.9, 0.7 and 0.9 ml/s and quadratic weighted kappa (QWK) of 0.99, 1.0 and 1.0 for the three devices. To evaluate the models in a real environment and assess the effectiveness of training with synthetic data, the best-performing models were retrained and validated using a real voiding sounds dataset. The results reported an MAE below 2.5 ml/s and a QWK above 0.86 for regression and classification tasks, respectively.

Abstract
Despite the important advances observed, nowadays, the Engineering programmes keep being challenged to better prepare their students to work on complex and multidisciplinary projects while demonstrating awareness of environmental and socio-economic issues and other soft skills as communication and teamwork. Recently, to meet these challenges, the ISEP' Informatics Engineering programme (LEI) successfully adopted a project-based learning approach. In this approach, throughout the entire semester, students develop a real-world project that allows the application and assessment of the competencies taught by all course units of the semester in an integrated, multidisciplinary, and transversal way. In this paper, the authors (i) present this approach as well as the main challenges faced in implementing it; (ii) report the major findings and the perceived benefits and drawbacks; and (iii) discuss the ongoing adaptations and/or others seen as required to improve the approach and its results. © CDIO 2022.All rights reserved.

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