Explaining factors influencing students' depression with a deep learning approach

Student mental health has emerged as an increasingly prominent issue in sustainable educational healthcare systems. Accurately and promptly identifying students' depression and analyzing the key factors associated with it are crucial for improving student mental health.

We propose an artificial intelligence algorithm, GLNet, that integrates Mamba and convolutional layers to extract features from students' demographic, academic, and lifestyle information for depression analysis. The performance of GLNet is validated on the publicly available Student Depression Dataset.

GLNet achieves an accuracy of 88.84% on the Student Depression Dataset, outperforming other methods and verifying its effectiveness in student depression analysis. Factor contribution analysis indicates that academic pressure and financial stress may be associated with student depression, while healthy dietary habits and academic satisfaction may alleviate depression. Subgroup analysis further reveals that a higher Cumulative Grade Point Average may be positively correlated with depression in female students, and unhealthy dietary habits may be linked to depression among doctoral students.

GLNet can serve as a reliable tool for enhancing student mental health. It also provides valuable insights for educators to identify students at risk of depression, contributing to the optimization of student mental health intervention strategies.