Arboviruses such as dengue, Zika, and chikungunya remain a serious public health challenge in Brazil and other Latin American countries. Effective surveillance and vector control require integrated information systems to support planning and monitoring efforts. This study presents a heuristic evaluation of a web-based system developed for arbovirus control coordinators in Brazil, aiming to identify interface limitations and propose improvements based on user experience. The Design Science Research Methodology (DSRM) guided the study, and a structured questionnaire grounded in Nielsen’s Heuristics was applied. A total of 72 undergraduate students from technology-related programs participated in the evaluation. Each participant performed guided tasks within the system and provided feedback through both closed and open-ended questions. Results indicated positive perceptions regarding interface consistency and organization, while usability issues were identified in areas such as error prevention, flexibility, and feedback mechanisms. Statistical analysis revealed differences among participant groups, highlighting the influence of user profiles on perceived usability. The findings underscore the value of heuristic evaluation in improving public health digital tools and demonstrate the potential for adapting such systems to similar regional contexts across Latin America.

This study investigates the use of gamification as an active methodology in Software Engineering education, aiming to enhance engagement, motivation, and learning effectiveness. The research proposes guidelines to assist educators in planning gamified activities, covering aspects such as scenario definition, player profiles, and game elements. The guidelines were validated through an exploratory study with teachers, who evaluated their effectiveness, ease of use, and adaptability to the educational context. Results indicate that the guidelines facilitate the integration of gamification in teaching. While validated with a reduced sample size and focused on Software Engineering, the findings highlight their potential for broader interdisciplinary application, offering a practical resource to foster more dynamic and engaging learning experiences.

Current paper describes the use of a feature selection technique to reduce the number of features while the goodness set is selected on a framework for forensic handwriting identification. A sequential forward search and an evaluation criterion based on dependency were used to obtain a goodness subset (GS) to improve the identification rate. The accuracy of the system applied to 100 different writers and taking account all features (N = 81) is 58%, whereas the accuracy based on goodness subset (GS) is 80% applied to the same number of writers. The validation of results was verified initially against all the features and later against some empirically set of features. Results are comparable to others in the literature on graphometric features.