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Related Experiment Video

Updated: Feb 17, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Explaining factors influencing students' depression with a deep learning approach.

Xinyu Li1, Yunyi Hu2, Huohong Chen3

  • 1School of Computer Science and Engineering, Central South University, Changsha, China.

Frontiers in Psychology
|February 16, 2026
PubMed
Summary

A new AI algorithm, GLNet, accurately identifies student depression by analyzing demographic, academic, and lifestyle data. This tool helps educators pinpoint at-risk students for timely mental health interventions.

Keywords:
Mambacontribution analysisdeep learningdepressionhigh educationmental health

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Area of Science:

  • Educational Healthcare Systems
  • Artificial Intelligence in Mental Health

Background:

  • Student mental health is a growing concern in educational settings.
  • Early identification and analysis of depression factors are vital for student well-being.

Purpose of the Study:

  • To develop and validate an AI algorithm for student depression analysis.
  • To identify key factors contributing to or alleviating student depression.

Main Methods:

  • Proposed GLNet algorithm integrating Mamba and convolutional layers.
  • Feature extraction from student demographic, academic, and lifestyle data.
  • Validation on the Student Depression Dataset.

Main Results:

  • GLNet achieved 88.84% accuracy, outperforming existing methods.
  • Identified academic pressure and financial stress as potential depression contributors.
  • Healthy habits and academic satisfaction may alleviate depression.
  • Specific correlations found between GPA, gender, and doctoral student habits.

Conclusions:

  • GLNet is a reliable tool for improving student mental health.
  • Provides insights for educators to optimize intervention strategies.
  • Highlights the need for targeted mental health support in educational institutions.