Establishment and Optimization of the Early Warning System for Student Academy:multimodal data fusion based on deep learning

Abstract

In the realm of education, the establishment of effective Academic Early Warning Systems (AEWS) holds significant promise for improving student outcomes and institutional efficacy. This paper proposes a novel approach to AEWS, leveraging multimodal data fusion and deep learning methodologies to provide a holistic understanding of students' academic trajectories. By integrating diverse data modalities including academic performance records, student engagement metrics, and socio-economic factors, our AEWS offers a comprehensive view of students' experiences. Deep learning techniques facilitate the fusion of heterogeneous data sources, enabling the extraction of meaningful patterns and predictive insights. Through iterative refinement and validation, our system aims to deliver actionable insights for educators and administrators, fostering timely interventions and personalized support strategies. The proposed AEWS represents a significant step towards enhancing student success rates and fostering a more inclusive educational environment.