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 diagnosis of Oral Epithelial Dysplasia (OED), presents a high interobserver variability due to subjectivity in the evaluation criteria. In this work, we propose an automatic histological image classification strategy based on the multiple instance learning (MIL) approach, using VGG-16 convolutional neural networks for feature extraction. Four models were trained: two for classifying the degree of OED (mild, moderate, and severe) and two for the detection of six relevant histopathological criteria. To optimize the training process, we implemented the Black Hole metaheuristic to find the learning rate that maximizes the performance of the models. Evaluation of performance and interobserver variability was performed using Cohen’s Kappa coefficient. The results suggest that the use of MIL, together with metaheuristic optimization strategies, can consistently reproduce expert diagnostic perception.

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.

The follow-up of alumni, that is, graduates of a house of studies, is an increasingly important process in universities to maintain relationships and professional opportunities between the parties and to validate graduation profiles and other relevant factors of institutional management. Once graduated, the alumni continue their professional careers taking further training and different types of jobs, thus establishing relationships with multiple organizations in the public and private world. These interpersonal ties form social networks that can be studied using social network analysis techniques. In this article, we analyze an alumni network from the computer engineering career at a Chilean university based on data collected from the LinkedIn platform. Through the analysis techniques, it was possible to characterize the alumni network, identifying a rich diversity of behaviors with different clusters and variable patterns over time.

Various didactic strategies to develop Computational Thinking (CT) skills have been successful in terms of student engagement and educational outcomes. However, monitoring the learning progress of students is still a hurdle to teachers and researchers. In this context, we explore the use of self-organizing maps for analyzing games produced in a game building workshop offered simultaneously to technical education students in Brazil and to undergraduate students in Computer Engineering in Chile. Metrics from seven CT features present in the games were extracted with the Dr. Scratch tool and used as an input in the training process. The results allowed a clustering analysis considering the identified features and the correlation between learning behaviors. The organization of the map reflected a progressive skill acquisition identified by features present in the developed games. Also, it could be identified that students of both educational levels reached similar levels of CT skill development.