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A knowledge graph and multi-agent reinforcement learning model for psychological risk identification and intervention
1Jianghan University, Wuhan, 430056, China.
Scientific Reports
|July 3, 2026
Summary
This study introduces a novel model combining knowledge graphs and multi-agent reinforcement learning to identify at-risk online learners early. The system effectively personalizes interventions, improving student motivation and learning patterns.
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- Learning Analytics
Background:
- Online learning expansion presents challenges in student engagement and motivation.
- Self-directed study often leads to declining learning behaviors and task accumulation.
- Increased psychological pressure is a common issue for online learners.
Purpose of the Study:
- To develop an innovative model for identifying at-risk students in online learning environments.
- To enable personalized interventions for improving student learning trajectories.
- To enhance motivation and optimize resource utilization in self-directed study.
Main Methods:
- Integration of a knowledge graph with multi-agent reinforcement learning.
- Development of a model for learning state risk identification and personalized intervention.
- Systematic evaluation using comprehensive learning behavior data from seven courses.
Main Results:
- The model achieved early and accurate identification of at-risk students (recall 0.879-0.896).
- Accuracy increased steadily, with F1-scores demonstrating stability (0.842-0.871).
- Intervention strategies significantly improved learning trajectories and reduced behavioral fluctuations.
Conclusions:
- The proposed model effectively enhances learning motivation and stabilizes learning rhythms.
- Personalized interventions optimize patterns of resource utilization in online learning.
- The integrated approach addresses key challenges in self-directed online education.
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