The rapid digitalization of education has led to an explosive growth of multi-source and multi-modal learning data, providing new opportunities for advancing Educational Data Mining (EDM). By learning diverse data such as learning behaviors, assessment records, and interaction logs, EDM enables deeper insights into student learning processes and supports the development of personalized and intelligent education. However, several critical challenges remain, including the heterogeneity and fragmentation of multi-source data, the difficulty of extracting meaningful knowledge through effective data fusion, and the limited interpretability of high-performance predictive models. To address these c... More >