From Crisis to Prevention: Machine Learning Reveals Behavioral Predictors Outperform GPA in Early Student Risk Detection

Key Points

• The aim is to explore how machine learning can identify behavioral predictors of student risk more effectively than GPA.
• Machine learning algorithms were applied to student data to analyze behavioral patterns.
• Behavioral metrics were developed and compared with traditional GPA assessments.
• Predictive accuracy of the models was evaluated using standard statistical methods.
• Behavioral predictors showed a higher accuracy in identifying at-risk students compared to GPA.
• Machine learning models identified specific patterns linked to student performance issues.
• Early detection allowed for timely interventions, potentially improving student outcomes.