Admission score, family Income, HSGPA, and learning approaches to predict academic performance in mathematics

Abstract

This research aims to examine factors which can predict academic performance in mathematics in higher education. The factors of concern include learning approaches, introvert and extrovert traits of personality, High School GPA (HSGPA), University Admission Score, and Family Income. Our sample size of 795 was collected from students at six universities in Ho Chi Minh City. Data analysis was performed with the multiple regression technique. The result shows that Admission Mark has the highest prediction power of Academic Performance in mathematics. In addition, HSGPA, Family Income, Deep Approach, and Strategic Approach are positively correlated with academic performance, but Surface Approach is negatively correlated with academic performance. Furthermore, the two personality traits have no prediction power when it comes to Academic Performance. Our final model can explain 35.7 percent of the variation in Academic Performance in mathematics. These findings can provide insights into students’ academic performance and ways to improve universities’ education service.