Predicting graduation grades using Machine Learning: A case study of Can Tho University students

Abstract

A number of factors influence a student's attainment of graduation. Besides scholastic performance within the academic curriculum, other variables such as living circumstances, gender, and choice of major significantly contribute to the probability of achieving graduation. The capacity to forecast academic performance at the time of graduation holds profound importance for universities, especially in discerning the influential factors that contribute to a student's successful completion of their educational pursuits. This study employs multiple machine learning algorithms, including K-nearest neighbor, Neural network, Decision tree, Random forest, and Gradient boosting, to prognosticate the graduation outcomes of 7,837 undergraduate students from Can Tho University during the academic year 2022. These selected students were enrolled in 16 colleges and institutes affiliated with Can Tho University. The efficacy of the employed algorithms was assessed through performance evaluation metrics encompassing accuracy, precision, recall, and F-measure. Furthermore, a 15-fold cross-validation technique was employed for validation. The findings revealed that the Random forest model yielded the most reliable predictions. The factors that significantly impact graduation grades comprise GPA, training point, residential address, college, major, and gender. Based on the experimental findings, these factors were ranked to ascertain their effects on student graduation.