Abstract
Context. Software testing is a critical aspect of the software development lifecycle, yet it remains underrepresented in academic curricula. Despite advances in pedagogical practices and increased attention from the academic community, challenges persist in effectively teaching software testing. Understanding these challenges from the teachers' perspective is crucial to aligning education with industry needs. Objective. To analyze the characteristics, practices, tools, and challenges of software testing courses in higher education, from the perspective of educators, and to assess the integration of recent pedagogical approaches in software testing education. Method. A structured survey consisting of 52 questions was distributed to 143 software testing educators across Western European universities, resulting in 49 valid responses. The survey explored topics taught, course organization, teaching practices, tools and materials used, gamification approaches, and teacher satisfaction. Results. The survey revealed significant variability in course content, structure, and teaching methods. Most dedicated software testing courses are offered at the master's level and are elective, whereas testing is introduced earlier in less specialized (NST) courses. There is low adoption of formal guidelines (e.g., ACM, SWEBOK), limited integration of non-functional testing types, and a high diversity in textbooks and tools used. While modern practices like Test-Driven Development and automated assessment are increasingly adopted, gamification and active learning approaches remain underutilized. Teachers expressed a need for improved and more consistent teaching materials. Conclusion. The study highlights a mismatch between academic practices and industry expectations in software testing education. Greater integration of standardized curricula, broader adoption of modern teaching tools, and increased support for teachers through high-quality, adaptable teaching materials are needed to enhance the effectiveness of software testing education.

Abstract
Recent technological advancements have increased computer crime, requiring public authorities to implement structured mitigation strategies. While initiatives exist to improve digital literacy on device security, they must also address the complexities of computer crime. Using Design Science Research, this study investigated the applicability of chatbots to raise awareness of computer crime in a public administration setting. A systematic literature review highlighted the issue's relevance and identified knowledge gaps. A scoping review gathered concepts, methodologies, technologies, architectures, and tools for developing and evaluating an effective chatbot. The design and development phase included a detailed proposal for a sophisticated chatbot architecture. During the demonstration and evaluation phases, the utility of the chatbot was tested in the domain of conversational flow efficiency and usability. The study's primary results and contributions are to assess the chatbot's effectiveness in raising awareness of computer crime on public websites. Future work should focus on implementing the chatbot in the actual context of public administration, proposing a network of specialized conversational assistants, and improving public service interoperability to enhance computer crime awareness.

Abstract
This study examines the role of online health communities in Brazil dedicated to cannabis treatments for chronic diseases as platforms for evidence-based activism. Using a mixed-methods approach, the research combines qualitative analysis with computational techniques, including Latent Dirichlet Allocation (LDA) topic modeling, to analyze six online groups from WhatsApp and Facebook. Key themes emerging from the analysis include treatment per pathology, treatment effects, access barriers, peer support, and advocacy efforts. The findings reveal how these communities act as epistemic networks, where patients and caregivers co-produce knowledge by sharing personal experiences and engaging in dialogue with healthcare professionals. This study highlights how online health communities transform experience sharing into structured evidence, enabling collective action to address barriers such as limited access to cannabis-based treatments. It underscores the potential of digital platforms to empower patients, foster collaboration with healthcare professionals, and influence health governance.

Abstract
Online health communities enable patients and caregivers to share experiences, seek advice, and collaboratively generate knowledge about treatments and condition. However, accessing relevant information often proves challenging due to platform limitations like insufficient search functionalities. A previous study identified key topics discussed in Brazilian online health groups centered on cannabis treatments for chronic diseases. Building on these findings, this study introduces a proof-of-concept chatbot designed to enhance access to the collective knowledge within these communities. The chatbot prototype, built using Google Dialogflow, was tailored to provide contextually relevant, accurate, and user-friendly responses. A user study involving 38 participants evaluated its performance, showing high user satisfaction, task completion rates, and trust in the information provided. The results highlight the chatbot's potential enhance knowledge accessibility, promote patient engagement, and support evidence-based activism by organizing and disseminating community-generated content effectively.

Abstract
Wikipedia is the largest and most globally well-known online encyclopedia, but its collaborative nature leads to a significant disparity in article quality. In this work, we explore real-time and automatic quality assessment within Wikipedia through machine-learning. We first constructed a dataset of 36,000 English articles and 145 features, then compared the performance of multiple classification and regression algorithms and studied how the number of classes and features affects the model's performance. The six-class experiments achieved a classifier accuracy of 64% and a mean absolute error of 0.09 in regression methods, which matches or beats most state-of-the-art approaches. Our model produces similar results on some non-English Wikipedias, but the error is slightly higher on other versions. We have also determined that the features measuring the article's content and revision history bring the largest performance boost.

Abstract