SymCART: a symbiotic cognitive-affective reinforcement transformer for optimizing educational interventions

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Cognitive Science

Background:

• Personalized learning requires effective emotion and cognition analysis in educational interventions.
• Existing models struggle with multimodal data integration and adaptive strategy optimization.
• Intelligent educational frameworks are needed to overcome these limitations.

Purpose of the Study:

• To propose the SymCART model, an intelligent educational intervention framework.
• To integrate multimodal data fusion with deep reinforcement learning for strategy optimization.
• To enhance personalized learning outcomes through adaptive teaching strategies.

Main Methods:

• Developed the SymCART model with a multimodal perception encoder, dynamic cognitive-affective graph inference engine, and adaptive teaching strategy optimizer.
• Utilized deep reinforcement learning for optimizing teaching strategies based on fused multimodal data.
• Conducted experiments on IMPROVE, student learning behavior, and validation datasets.

Main Results:

• SymCART demonstrated higher predictive accuracy and more effective strategy recommendations than traditional models.
• Achieved statistically significant improvements in AUC, RMSE, weighted F1, nDCG, and ADR.
• Ablation studies confirmed the critical role of multimodal fusion and strategy optimization modules.

Conclusions:

• The SymCART model offers a robust solution for personalized educational interventions by effectively analyzing emotion and cognition.
• The framework exhibits broad applicability for adaptive learning strategies and improving student learning outcomes.
• SymCART represents a significant advancement in intelligent educational systems.