Roberto Munoz

The widespread adoption of virtual and remote laboratories has transformed practical education by offering scalable, safe, and flexible environments for developing technical skills. Despite these benefits, challenges remain in providing timely, meaningful feedback that supports self-regulated learning and improves student outcomes. In this study, we analyze anonymized interaction logs from a virtual laboratory system focused on Industrial Electrical Control Training Lab, used by 839 students enrolled in Electrical, Electromechanical, Mechanical, or Industrial Automation vocational programs offered by the National Service of Industrial Apprenticeship of Santa Catarina (SENAI/SC, Brazil). We introduce a novel methodology that combines sequential pattern mining (SPM) with automatic performance-based segmentation to analyze student behavior in virtual laboratory environments. Using interaction logs from students containing time-stamped cable connections and interface interactions, we segment learners into performance (High, Intermediate, and Low) based on the z-scores of accuracy and speed, using two strategies: (i) a K-means clustering approach, and (ii) a rule-based decision function. Building on this segmentation, we propose an algorithm to identify frequent action sequences and recurrent error patterns specific to each group. We further apply chi-square analysis to determine statistically significant associations between specific errors and performance profiles. Our findings indicate that students in the high-performance group are likely to refer to schematics beforehand, whereas those in the lower performance group frequently begin with montage connections. Inference analysis demonstrated that these segmented groups, for both the rule-based and K-means segmentation approaches, commit statistically distinct types of errors. These results can be integrated into LA dashboards to support real-time feedback for students and instructors, ultimately enhancing teaching strategies and learning in practical disciplines.

Las metodologías ágiles han transformado el desarrollo de software al priorizar la colaboración y la adaptabilidad. Sin embargo, aspectos importantes como la diversidad, la equidad y la inclusión no suelen ser centrales en estos marcos. Esta omisión puede reforzar involuntariamente las desigualdades dentro de equipos diversos, especialmente en entornos de trabajo híbridos. Este estudio aborda directamente este problema mediante el uso del análisis multimodal para explorar la relación entre la dinámica de interacción y la equidad percibida en equipos ágiles. Mediante experimentos con estudiantes de ingeniería, los investigadores examinaron métricas como el tiempo de conversación y la dirección de la mirada, comparándolas con cuestionarios de equidad percibida. Los resultados mostraron correlaciones débiles, lo que indica que estas métricas cuantitativas por sí solas no son suficientes para comprender plenamente la equidad percibida. Esta investigación contribuye de forma valiosa a la creación de prácticas ágiles más inclusivas, abogando por el uso de indicadores cuantitativos para evaluar la dinámica colaborativa desde una perspectiva de equidad.