The widespread adoption of Generative AI (GenAI) tools by students across different educational levels highlights the need for them to develop robust GenAI literacy, including a working understanding of these systems' fundamental concepts, their limitations, and implications for responsible use. However, misconceptions about GenAI, such as perceiving these systems as mere search engines or database lookup systems, are commonly observed among students, while the availability of teaching resources remains fragmented, and learning objectives lack alignment. This Working Group aims to design pedagogical resources for computing science instructors, enabling them to develop students' GenAI literacy. To achieve this, the Working Group will first identify a concise set of GenAI literacy learning objectives informed by instructor experience, research literature, and community input, and subsequently design pedagogical resources aligned with these objectives.

This study examines the integration of futuristic thinking methodologies into X-Ops education, encompassing topics such as DevOps, AIOps, DataOps, and DevSecOps. Traditional teaching methods often fail to bridge the gap between theoretical knowledge and practical application, leaving students unprepared for the evolving demands of the industry. To address these challenges, this research introduced speculative and experiential learning strategies, including science fiction prototyping, movie analysis, flash fiction storytelling, and innovation labs. These activities aimed to enhance engagement, knowledge retention, critical thinking, and collaborative skills by immersing students in creative and hands-on problem-solving scenarios.The findings demonstrate that futuristic thinking methodologies significantly improve students’ understanding of X-Ops concepts, fostering creativity and innovation while promoting collaboration and ethical reasoning. Quantitative data revealed marked improvements in familiarity with X-Ops principles and problem-solving confidence, while qualitative reflections highlighted the value of group-based activities in contextualizing abstract theories. Despite limitations such as sample size and short-term observation, the study underscores the transformative potential of active and future-oriented pedagogies. By bridging the gap between theory and practice, these approaches prepare students to thrive in the dynamic and evolving landscape of modern technological systems.

The rapid evolution of technology and the complexity of global challenges demand innovative educational methodologies to prepare learners for uncertain futures. This study examines the integration of futuristic thinking methodologies, such as the analysis of science fiction films, science fiction prototyping, and flash fiction stories, in higher education to promote critical thinking, creativity, and ethical awareness. Involving 17 higher education professors from the UNITA Alliance in a two-hour interactive session, the study revealed significant improvements in participants’ familiarity, confidence, and readiness to apply these methodologies in their professional contexts, showcasing the value of practical and hands-on approaches.While the session effectively addressed initial hesitancy and knowledge gaps, challenges such as resource accessibility, content clarity, and implementation support remain. The study emphasizes the need for targeted strategies, including centralized resources, follow-up training, and fostering peer networks, to ensure sustained adoption. By addressing these challenges and promoting interdisciplinary collaboration, the findings demonstrate the potential of futuristic thinking methodologies to prepare educators and students to navigate and influence a rapidly evolving world, contributing to educational innovation and future research directions.

The growing use of Internet of Things (IoT) technology within the healthcare system has created vast, heterogeneous data sets that are generated from wearable sensors, smart monitoring devices, and medical records. The lack of semantic interoperability among the vast diversity of these data sources represents one of the largest challenges because of format differences, domain-specific standards, and context differences. Scalability, flexibility, and inefficiency of the knowledge extraction process limit current solutions to provide worthwhile efficacy in large-scale IoT-healthcare systems. This research proposes a Unified Semantic Framework for interoperability and converting diverse healthcare and IoT data into semantically homogeneous, machine-understandable, structured forms. The ontologies are used in this framework to enable SPARQL querying as well as context data enrichment of information for interoperability within a domain. In contrast to common methods based on static relational schema and manually designed features, the approach enhances knowledge discovery and data consistency and enables scalable predictive analytics for IoT-based healthcare applications. The efficacy of this approach is depicted with conceptual modeling and implemented case scenarios and showcases scalable data integration capability, intelligent decision support, and effective query. With the addition of a graph-based semantic layer, the framework provides a foundation for high-end machine learning-enabled analysis in IoT and health informatics environments.

This study explores the integration of futuristic thinking and soft skills development within educational settings, emphasizing their potential role in equipping students to navigate the complexities of the modern workforce. In this paper, we report our insights captured from both higher education teachers and K-12 students. The findings from two experimental sessions-one involving teachers and the other with K-12 students-demonstrate not only high levels of engagement and satisfaction but also the adaptability and relevance of these methodologies across different age groups and educational stages. These results underscore the effectiveness of integrating futuristic thinking with soft skills training, highlighting their potential to be widely adopted in educational curricula.

GenAI is playing an increasingly important role in computing courses at all levels, offering new opportunities to support teaching and learning. However, using GenAI effectively raises important concerns regarding trust, academic integrity, and broader social and ethical dimensions. This Working Group was formed to report on the current state of the art in using GenAI in upper-level computing courses to aid educators. The working group will undertake a methodological review of published work and solicit input from the computing educational community as part of the report.

Efficient bug triage is a critical aspect of large-scale software development, yet it remains a labor-intensive and error-prone task. This paper presents a novel approach to automated bug report classification by leveraging BERT, a state-of-the-art transformer-based LLM, to categorize bug reports into well-defined software defect types. We introduce a structured and scalable classification taxonomy designed to reflect the complexities of real-world bug reports. The proposed method incorporates fine-tuning of BERT on domain-specific datasets and evaluates performance across multiple bug categories using accuracy, precision, recall, and F1-score metrics. Empirical results demonstrate that our approach outperforms traditional machine learning methods, achieving an overall accuracy of 72% and delivering particularly strong performance in critical categories such as security and performance bugs. Using both the bug title and description as input produced the best results, underscoring the importance of contextual detail in effective bug triage. This work contributes to the field of software defect classification by providing a replicable and adaptable methodology for automated bug triage, with practical implications for enhancing software maintenance and quality in large-scale development projects.

Agile methodologies, such as Scrum, have been widely adopted in software development because they enhance project management and deliver high-quality products in dynamic environments. However, their application in higher education presents both unique challenges and opportunities. This paper reviews relevant literature to identify Scrum’s challenges, benefits, limitations, and potential adoption barriers in in-class activities. Additionally, the application of Scrum in educational settings, particularly focusing on project-based learning (PBL) in a Software Engineering course unit, is distilled. In line with this, to assess the adoption of Scrum in a PBL approach, a survey was conducted with students from diverse backgrounds. This survey aimed to evaluate their experience in collaborating with peers and instructors, teamwork dynamics, understanding of software engineering technologies and tools, and their overall perspective on the course. The study also examined the level of understanding and application of Scrum principles by students. It focused on the effectiveness of Scrum in enhancing student communication skills, the adequacy of technologies in supporting the software development methodology used, and the students’ comprehension of software engineering principles and artifacts. The findings revealed varied experiences among students, highlighting both successes and areas needing improvement. Some students reported enhanced communication and teamwork skills, attributing these improvements to the structured nature of Scrum. However, challenges such as the initial learning curve, varying levels of engagement, and the adequacy of supporting technologies were also noted. The study’s outcomes suggest that while Scrum can significantly enhance learning outcomes, careful consideration must be given to its implementation. The insights gained from this study may inform the design of future educational interventions, aiming to optimize the integration of Scrum and similar methodologies to enhance students’ learning experiences and outcomes in software engineering education.

The growing adoption of instructional technology in education has been driven by its contribution to society's development. Our study aimed to examine the use of Augmented Reality (AR) in teaching Python programming principles. The study consisted of three phases: a systematic review of AR using the PRISMA statement, the development of a mobile AR application called “EDUpy,” and the evaluation of EDUpy through two experiences with undergraduate students enrolled in different course units. The study found 30 studies in literature focused on the introduction of AR in educational contexts, ranging from kindergarten to higher education. Additionally, 57 students were enrolled in the experiences and the results showed that EDUpy was suitable in terms of user experience and effective in achieving students' learning goals. Further research is necessary to determine the impact of the application on students' soft skills, such as creativity, engagement, motivation, teamwork, and self-learning.

Flaky tests can pose a challenge for software development, as they produce inconsistent results even when there are no changes to the code or test. This leads to unreliable results and makes it difficult to diagnose and troubleshoot any issues. In this study, we aim to identify flaky test cases in software development using a black-box approach. Flaky test cases are unreliable indicators of code quality and can cause issues in software development. Our proposed model, Fast-Flaky, achieved the best results in the cross-validation results. In the per-project validation, the results showed an overall increase in accuracy but decreased in other metrics. However, there were some projects where the results improved with the proposed pre-processing techniques. These results provide practitioners in software development with a method for identifying flaky test cases and may inspire further research on the effectiveness of different pre-processing techniques or the use of additional test smells.

The recommendation that there must be a balance between insulin, food, and exercise to keep diabetes under control provides an opportunity for developing mobile applications for self-management of the disease. Real predictions can improve the quality of patients’ lives by avoiding unwanted events, namely, hypoglycaemia. We proposed a hypoglycaemia prediction approach combining information fusion and classifiers consensus to predict the risk of hypoglycaemia in a 24-h window. First, we train a multi-classifiers system from different sources of different patients. After using data from a unique patient, we performed the prediction of the risk of hypoglycaemia and evaluate the consensus decision of the single models resulting from the learning process. The predictions were performed for 54 patients from the University of California Irvine diabetes dataset. The results from classifiers consensus decision provide very promising results, which are acceptable considering that we used sparse data and data from self-monitoring blood glucose. Our approach shows that with a 24-h window is possible to catch appropriate patterns associated with the risk of hypoglycaemia and proposed a solution that can improve the hypoglycaemia prediction with a higher specificity, i.e. less false alarms, when compared with similar literature.

The increasing adoption of technology in the classroom offers several advantages, but also challenges all stakeholders, including instructors, students and decision makers. E-learning has gained momentum, especially in the last two years with the onset of the pandemic, forcing lectures to migrate to the internet and requiring new methodologies to support knowledge transfer between instructors and students. Thus, to verify the situation of Angola in relation to practices and methodologies related to the teaching of informatics, we search to identify existing practices through the application of a questionnaire to teachers of informatics subjects in secondary schools, colleges, and universities of the country. This study aims to discover the effects of using technology as a support for teaching computer science classes, including software engineering, programming, and computer networks. Therefore, 82 participants were involved in this study answering an online survey. Results shown that, although digital technologies are revolutionizing the world and consequently education, institutions in Angola still face several challenges, such as, lack of laboratories for practical classes, lack of electricity, lack of internet, and lack of computers for students. In addition, the adoption of technologies in the classroom proved ineffective, perhaps because of the absence of determining factors for technological inclusion by the institutions, or even because of the training of teachers in the area, whether weak, inefficient, or incomplete. In this sense, although the use of software in the classroom facilitates the acquisition of knowledge, there are still several barriers to be overcome for this to happen broadly. This study concludes that it is necessary to consider technology as a basic need for the facilitating role it plays in society.

The adoption of technologies such as tablets or smartphones for children at home or at school is one of the most recent trends in today’s world. This challenges for suitable teaching methodologies and practices, along with oriented learning contents for such devices. On the one hand, teachers and tutors may acquire skills and competences to cope with gadgets and its technologies. On the other hand, learners must be versatile onto a complementary and complex environment in which the virtual meets the traditional lectures. Moreover, when the learning ecosystem includes children and younger newbies with technologies and gadgets is promising the adoption of targeted approaches such as gamification, playful activities and/or high interactive and supportive learning. This study presents the introduction of game-based learning techniques for young children at Cape Verde, with 17 students aged from 4 to 11 years, using the Code Karts app. Pre- and post-assessments measured familiarity with technology, appeal of the coding app, ability to play Code Karts, conceptual understanding of coding, and in-class behaviours. Results revealed that children prefer haptic interfaces to interact with technological devices. Congruently, the adoption of mobile phones and tablets was easier at the expense of the use of laptops. In addition, the proposed learning methodology, based on gamification principles, was determinant to promote in-class interaction and tutor-children interaction. In addition, gamification was crucial for the learning outcomes and to provide insight on programming. Complementary studies must be addressed aiming at to evaluate the long term effect on children due to the adoption of programming activities in the classroom.

The software engineering community celebrated, in 2018, the 50th anniversary of what is considered to be the official start of the profession of software engineering. Software engineering is a young and promising discipline which is still under development and improvement. This is reflected when teaching software engineering in higher education. The aim of this study is to investigate the challenges and perspectives of software engineering education. To do so, a questionnaire study was conducted. 21 software engineering faculty and experts in teaching software engineering related courses participated in this study. The questionnaire contained demographic questions, questions related to students’ engagement and to different methodologies adopted by respondents in the classroom. Results showed that the majority of respondents found engaging students in software engineering courses to be the biggest challenge they faced in the classroom. Almost half of the participants found difficulties designing practical activities for students. Results also revealed that the problem-based learning approach is the most used in software engineering lectures, followed by gamification techniques and role-playing which are new trends used to engage students. Moreover, the majority of the participants considered that the adoption of new teaching methodologies in the classroom produced high impact in the students’ learning experience. Based on the outcomes of this questionnaire study, a conceptual model to engage students in software engineering courses is proposed. For future work, complementary studies should be implemented to evaluate the proposed model in a real-world scenarios including its effect on the achievement of learning outcomes.

The introduction of gamification principles into the classroom may enhance and extend the student engagement in learning activities, and therefore, contribute to a more efficient knowledge acquisition. In this paper, we report a case study whose methodology is based on awarding merit badges as a result of the successful completion of learning activities focused on either soft and hard skills. This initiative was implemented in two universities, involving 221 participants, aimed at assessing how the proposed model may contribute to students’ motivation, appealing for its participation in the classroom, and measuring their stimulation for continuous study. Early stage results are promising and suggest not only a higher acceptance of the system and its effectiveness, but also, the suitability of gamification and reward systems on education. Further studies should be implemented in order to determine the effect of the reward system on students’ grades.

Fusion multidisciplinary subjects in order to present a unique and complementary perspective may enhance and extend the knowledge acquisition, and consequently, the student experience. In this paper we report a case study on methodology, which interlinks various subjects creating and engaging learning process by combining Science, Technology, Engineering and Mathematics (STEM) and Design. In line with this, an Experimental Interaction Design summer course was designed and implemented based on a multi-cultural, defiant and creative environment in order to provide an effective learning on solving the real-world challenges. Twelve students were involved in this initiative, which resulted in the development of several projects such as a pets' tracking and behavior analysis, a smart home for elderlies, and a medication reminder device, in which design, prototyping, usability evaluation and programming concepts were combined. In this report, we focus on the study design with the aim to provide scaffolding for multidisciplinary teams of students in design-based projects that require STEM competences.

The recognition of Activities of Daily Living (ADL) and their environments based on sensors available in off-the-shelf mobile devices is an emerging topic. These devices are capable to acquire and process the sensors' data for the correct recognition of the ADL and their environments, providing a fast and reliable feedback to the user. However, the methods implemented in a mobile application for this purpose should be adapted to the low resources of these devices. This paper focuses on the demonstration of a mobile application that implements a framework, that forks their implementation in several modules, including data acquisition, data processing, data fusion and classification methods based on the sensors? data acquired from the accelerometer, gyroscope, magnetometer, microphone and Global Positioning System (GPS) receiver. The framework presented is a function of the number of sensors available in the mobile devices and implements the classification with Deep Neural Networks (DNN) that reports an accuracy between 58.02% and 89.15%.

This paper presents a suitable and efficient implementation for detecting minute based analysis of sleep apnea by Electrocardiogram (ECG) signal processing. Using the PhysioNet apnea-ECG database, a median filter was applied to the recordings in order to obtain the Heart Rate Variability (HRV) and the ECG-derived respiration (EDR). The subsequent extracted features were used for training, testing and validation of a Artificial Neural Network (ANN). Training and testing sets were obtained by randomly divide the data until it reaches a good performance using a k-fold cross validation (k=10). According to results, the ANN classification has sufficient accuracy for sleep apnea detection and diagnosis (82,120%). This promising early-stage result may leads to complementary studies including alternative features selection methods and/or other classification models.

The emergent importance of the sleep medicine together with the rapid adoption of mobile devices and wearables along with a growing habit for using these devices on the daily-living activities offer an opportunity to use them as a sleep monitor. Traditional sleep monitoring systems, such as polysomnography, involves a myriad of sensors attached around the patients' body, and therefore is limited to clinical usage. In line with this, alternative solutions have been widely investigated focused both to infer sleep information based on a reduced number of sensors, and to use non-invasive and feasible technologies. This study presents an ubiquitous architecture based on portability and interoperability concepts in which a smartwatch is combined with a smartphone to implement a sleep monitoring system. This system joints heart rate, accelerometer, and sound signals collected into the smartwatch. Thus, the preliminary results revealed that the proposed system is feasible and suitable for sleep monitoring. In spite of the early stage of this system, the developed framework already includes an API for data exchange between the smartwatch and the smartphone. However, additional studies should be addressed in order to complement and enhanced the proposed system with novel algorithms for signal treatment, including the development of several classifiers specific oriented to snore, motion and sleep detection.

The main challenge of big data processing includes the extraction of relevant information, from a high dimensionality of a wide variety of medical data by enabling analysis, discovery and interpretation. These data are a useful tool for helping to understand disease and to formulate predictive models in different areas and support different tasks, such as triage, evaluation of treatment, and monitoring. In this paper, a case study based on a predictive model using the radial basis function neural network (RBFNN) combined with a filtering technique aiming the estimation of electrocardiogram (ECG) waveform is presented. The proposed method revealed it suitability to support health care professionals on clinical decisions and practices.