Predicting Student Success in English Tests Using Artificial Intelligence Algorithm

In Vietnam, English proficiency is a graduation requirement and offers students great opportunities to win scholarships and employability after graduation. Universities in the Mekong Delta region (MDR) often face challenges in foresting students’ English proficiency despite continuous assistance offered. Although students have taken online supplementary courses (OSC) delivered through e-learning systems to support their English formal classes for several years, students’ successes in English tests with such supplementary courses and the predictors of this issue remain unknown. Therefore, we developed a model to predict students’ success in English final tests based on behaviors and grades in OSC using logistic regression (LR) and classification and regression tree (CART) classifiers. A total of 109 students of OSC in a target university in MDR participated in this study, and the result showed that CART (area under the curve (AUC) = 89.3%) was slightly better than LR. The outcomes of this study contribute to students’ success in English tests and the enhancement of the effectiveness of online supplementary courses for English improvements.