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 the 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. Chi-square tests confirmed significant associations (p < 0.05) between distinct error types and performance levels, and one-way ANOVA validated that both segmentation methods produce statistically distinct clusters. 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. 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. © 2013 IEEE.
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