A Machine-Learning Approach to Examine Personality, Adjustment, and Engineering Identity among College Engineering Students

This study examined the relationship between personality, college adjustment, and student outcomes among undergraduate engineering students. Specifically, we investigated whether general personality traits based on the Big Five model, vocational personality types, and students’ adjustment to college predicted academic achievement and engineering identity. College adjustment was measured using the Student Adaptation to College Questionnaire (SACQ). Engineering identity was measured using engineering identity measure (EIM).Academic achievement was measured by cumulative grade point average (CGPA). We recruited 115 college students from diverse ethnic and cultural backgrounds, all of whom were enrolled in the civil engineering program at a private university in a metropolitan area in the US. Participants were classified into either above-average or below-average groups based on their CGPA and EIM scores. Five machine learning models were trained and evaluated using nested 10-fold cross-validation. Feature importance was examined using Shapley Additive exPlanations (SHAP) to identify the variables that contributed most strongly to academic achievement and engineering identity. The findings suggest that personality characteristics and college adjustment play important roles in college engineering students’ academic performance and engineering identity. Academic adjustment emerged as the strongest positive predictor of both CGPA and engineering identity, whereas neuroticism and extraversion were associated with lower academic achievement. Future researchers should examine factors affecting academic achievement, engineering identity, and adjustment to college among students from diverse backgrounds.