Author(s) :

eJenn Huang.

Volume/Issue :

Abstract :

Mental health challenges, including depression, anxiety, and stress, are increasingly affecting students worldwide, with significant implications for academic performance. This study investigates the relationship between mental health and academic success, specifically focusing on CGPA (Cumulative GPA), while considering demographic and school-related variables such as age, gender, course, and year of study. The data utilized for this research originates from the Kaggle “Student Mental Health” dataset, consisting of 101 student responses. To address the limitation of a small sample size, the dataset was expanded to 10,000 entries using bootstrapping and numeric perturbation. Various statistical methods, including ANOVA, Chi-Square tests, multinomial logistic regression, and random forest feature importance rankings, were applied to both the original and simulated datasets. The results indicate that age has no significant effect on CGPA, while mental health variables such as depression and treatment exhibit significant associations with academic performance in the original dataset. The simulated dataset, however, showed exaggerated relationships, emphasizing the need for careful validation when using simulated data. Feature importance rankings identified “Course” and “Current Year” as the most critical predictors of CGPA, with mental health variables ranking lower. These findings highlight the complex interplay between mental health and academic performance and call for enhanced mental health support within educational systems.