In modern organizations, agile methods have become key strategies for project development, promoting collaboration and adaptability within teams. These approaches optimize communication and cooperation, enabling effective responses to evolving environmental demands. However, collaboration relies on effective communication, coordinated action, and cooperative participation elements that are difficult to evaluate without automated methodological support. Meanwhile, the emergence of transformer-based natural language processing (NLP) models has enabled the identification of semantic features for analyzing communication and collaboration. This study presents a verbal intervention classification system based on natural language processing (NLP) techniques, utilizing multimodal learning analytics to transcribe audio into text and characterize interactions. The system, built upon DistilBERT and trained with manually annotated examples, identifies and categorizes interventions into five classes: question, answer, feedback, suggestion, and comment. The model has been implemented in a functional platform that visualizes results through an interactive interface, allowing facilitators of collaborative activities to analyze the dynamics of interactions and participant contributions, thereby supporting the continuous improvement of such activities.

The underrepresentation and low retention of women in STEM fields—particularly in software development—remains a structural challenge. For this reason, understanding how gender composition affects collaborative participation in agile teams, especially in contexts where there are gender-isolated participants, is crucial. Unlike traditional approaches, this work employs nonverbal communication analysis through Multimodal Learning Analytics, focusing on two key indicators: speaking time and bodily mimicry. The analysis was based on audiovisual recordings of 16 student teams in Computer Engineering, who participated in collaborative user story estimation sessions, both with and without the use of the Planning Poker technique. Teams were categorized based on their gender composition as homogeneous, balanced, or asymmetric. The results show that individuals who belonged to the gender minority within the team tended to participate less, both in speaking time and in nonverbal synchronization behaviors, suggesting subtle exclusion or the adoption of a peripheral role. Additionally, stronger mimicry patterns were observed before final voting, reinforcing their potential as early indicators of group convergence. This work provides empirical evidence on the impact of gender composition in agile collaboration dynamics and outlines new research directions for designing more inclusive teams in educational and professional settings.

Learning analytics (LA) and educational data mining (EDM) are two complementary approaches to modeling and understanding teaching-learning processes and, in general, data from academic environments. LA is applied to data from various sources, which can vary in format, granularity, and structure. Integrating these data is key to addressing the challenge of scalability in LA, a fundamental aspect. To this end, interoperability, understood as the ability of different systems, devices, or applications to connect, interact, and work together effectively, is crucial and generates the need for specifications for the case of academic information systems and Learning Management Systems. According to this context, the objective of this work was to address through a literature review the following main question: What are the main challenges for modeling architecture to support the interoperability of educational data to apply Learning Analytics? To develop the review, the team used Parsifal, an online tool designed to conduct systematic literature reviews in the context of software engineering. The initial search was done in six databases, deciding to include twenty papers in the final report. The results showed that there are still many open spaces for research and development in terms of the design and use of educational data specifications for the subsequent application of LA, to make the transition from models built on data coming from a single source to the construction of models that report results from the integration of several sources using specifications like Caliper Analytics or Experience API.

Stakeholder engagement is a key aspect for the successful implementation of Learning Analytics (LA) in Higher Education Institutions (HEIs). Studies in Europe and Latin America (LATAM) indicate that, overall, instructors and students have positive views on LA adoption, but there are differences between their ideal expectations and what they consider realistic in the context of their institutions. So far, very little has been found about stakeholders’ views on LA in Brazilian higher education. By replicating the survey conducted in other countries, in seven Brazilian HEIs, we found convergences both with Europe and LATAM, reinforcing the need for local diagnosis and indicating the risk of assuming a ”LATAM identity”. Our findings contribute to building a corpus of knowledge on stakeholders expectations with a contextualised comprehension of the gaps between ideal and predicted scenarios, which can inform institutional policies for LA implementation in Brazil.

The identification of urban locations with a high risk of diseases infections is a central aspect of public policies aiming at controlling these diseases. The presence of diseases, such as dengue fever, can be attributed to environmental factors in the urban scenario. Previous works have leveraged street-level imagery to provide estimates of dengue rates in an urban setting. In this paper, we apply Dense Deep Convolutional Neural Networks to both street-level and aerial imagery, providing evidence that aerial photography can provide better results than street-level images alone, while combining both leads to further improvements.

The present work describes the challenges faced during the development of a countrywide Learning Analytics tool focused on tracking the trajectories of Uruguayan students during their first three years of secondary education. Due to the large-scale of the project, which covers an entire national educational system, several challenges and constraints (both technical and legal) were faced during its conception and development. This paper presents the design decisions and solutions found to address or mitigate the problems found, with the current state of the project. Early results point out the feasibility of finding meaningful patterns in the available data (using data mining techniques) which can be embedded into a prototype for tracking the students scholar trajectory.

Communication in Online Distance Learning courses revolves around two distinct forms: synchronous and asynchronous. A lot of work has been already developed focused on better understanding the roles that each of these forms of communication plays in Distance Learning and to which extent they are sufficient to provide rich and in-depth interaction experience for students and professors. The present paper focuses on better understanding the perceptions of Online Distance Learning students and supervisors about communication tools available for them during the Final Year Project supervision (FYP). A total of 262 students and 62 professors were surveyed about their impressions related to three different aspects of communication during FYP distance supervision: preferences for one form of communication over the other (synchronous versus asynchronous), appropriateness of both forms of communication to different types of discussions, and the sufficiency of these forms of communication as the sole forms of communication in the FYP discipline. Among other things, results point out an explicit preference from students and supervisors for using the asynchronous form throughout the discipline (even though the synchronous form also received good ratings). Moreover, both forms of communication were more used by students and supervisors for the discussion of academic and important topics. At last, both students and supervisors consider distance supervision as efficient as face-to-face supervision, and less experienced students consider more important to have face-to-face meetings with their supervisors than more experienced students.