A review on data fusion in multimodal learning analytics and educational data mining
This study addresses the challenges of fusing heterogeneous educational data—such as audio, video, eye-tracking, physiological signals, and behavioral logs—in multimodal learning analytics (MLA), and the consequent lack of robust intervention support. We systematically review and establish a taxonomy and technical pathway for data fusion in educational contexts. Methodologically, we propose a novel three-tier fusion framework spanning feature-level, decision-level, and model-level integration, synergizing machine learning and educational data mining techniques to enhance cross-modal collaborative modeling. Our analysis identifies critical bottlenecks in temporal alignment, interpretability, and real-time intervention capability, and clarifies a theoretical paradigm and developmental roadmap for data fusion in intelligent learning environments. Results demonstrate that principled multimodal fusion significantly improves learning state recognition accuracy and the efficacy of pedagogical interventions, thereby providing a methodological foundation for next-generation adaptive learning systems.