Socially enhanced graph network for early identification of at-risk students in virtual learning environments

Key Points

• Early identification of at-risk students is crucial for improving educational outcomes in virtual environments.
• The study utilizes a graph network approach, integrating social factors for better predictive analysis.
• Analysis reveals that social enhancement within the graph network increases accuracy in identifying at-risk students.
• Findings suggest that implementing these networks can enable educators to tailor support effectively.