Predicting career development paths of college students using biomechanical and behavioral data with machine learning
Abstract
Accurately predicting career development paths is crucial to enhancing educational guidance and aligning student outcomes with labor market demands. This study presents a novel approach that integrates biomechanical and behavioral data with machine learning techniques to forecast career paths for college students. Using a dataset of 150 students, the study examines key biomechanical variables, such as joint angles, gait parameters, and ground reaction forces, alongside behavioral traits, including confidence levels, engagement, and personality. A Random Forest model was employed to analyze these multidimensional data and identify patterns predictive of career outcomes. The model achieved % overall accuracy of 82.57%, with individual performance metrics across four career categories showing substantial precision and recall. Integrating biomechanical and behavioral factors improved the model’s predictive power, demonstrating that physical attributes, when combined with traditional behavioral data, provide a more comprehensive understanding of career suitability. These findings have significant implications for career counseling, educational interventions, and workforce development, offering a data-driven approach to support students in making informed career decisions.
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