Muñoz, RobertoRobertoMuñozNoël, RenéRenéNoëlAdrian Fernández CaninoFernando Silva VargasDayana PalmaCristian CechinelMiranda, DiegoDiegoMiranda2025-08-252025-08-252024-01-0110.1109/sccc63879.2024.107676522-s2.0-85213510537https://cris-uv-2.scimago.es/handle/123456789/6287WOS:001458245200037Nonverbal behavior mimicry is fundamental for effective social interaction and group cohesion, especially in dynamic professional environments such as agile teams. However, the automatic detection and analysis of technique in these contexts present significant challenges due to the complexity of human interactions and the diversity of nonverbal expressions. This article presents the development of a predictive model for detecting nonverbal behavior mimicry in a collaborative contexts using Multimodal Learning Analytics, which collects and analyzes data from multiple sources. The results show advances in the automatic detection of mimicry in groups of more than two people, using a long-short term memory (LSTM) machine learning model focused on complete participant information. This innovative approach allows learning the progression of 'storylines', allowing the model to determine when to forget or integrate past events to inform decision-making. In this way, it is possible to achieve a better understanding of how episodes of mimicry occur during collaborative activities. To visualize these episodes, various visualizations are presented, illustrating the interactions and similarities in nonverbal behavior among team members. This approach not only facilitates the identification of mimicry patterns but also offers an alternative for understanding group dynamics in agile environments, thus favoring the efficiency and effectiveness of work teams.enacceso restringidoComputer ScienceEngineeringconference paper