| Abstract | Emotional intelligence (EI) has been increasingly recognized as a critical factor influencing
academic success among college students. This study investigates the impact of EI components—
self-awareness, self-regulation, motivation, empathy, and social skills—on academic achievements
using the XGBoost algorithm, a powerful machine learning technique known for its high predictive
accuracy. The primary objective of this work is to determine the relative importance of EI traits in
predicting academic performance and to develop a robust model that can identify key emotional
intelligence factors contributing to student success. A dataset comprising EI assessments and
academic records of college students is analyzed, with XGBoost employed for feature importance
analysis and predictive modeling. The findings reveal which EI dimensions most significantly
influence academic outcomes, providing actionable insights for educators and policymakers to
enhance student support programs. This research contributes to the growing body of literature on EI
in education by leveraging advanced machine learning to quantify its impact, offering a data-driven
approach to improving academic interventions.
Keywords: Emotional Intelligence, Academic Achievement, XGBoost, Machine Learning,
Predictive Modeling, Student Performance
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