Educational Digital Twin: Tackling Complexity in Educational Big Data

"Everything is bigger in Texas" and this includes the big data challenges of the state’s educational system. But just as big is the opportunity for digital twin technologies to improve decision-making in education, with potential to improve student outcomes. This paper formulates an Educational Digital Twin, a novel approach to understanding, modeling, and analyzing educational data to address the complexities inherent in student pathways. By "student pathways", we refer to the joint facets of student behavior—evolving dynamically over time and encompassing not only course enrollments but also behavioral and academic factors. Unlike traditional approaches that rely on static analyses of historical data, the Educational Digital Twin applies the digital twin paradigm to model these pathways as a living construct that dynamically evolves alongside the physical world. At the heart of the Educational Digital Twin is a knowledge graph that organizes data into a semantic structure. By employing graph-theoretic methods, we manipulate this structure to derive multi-granular insights that inform decision-making. Our evaluation demonstrates how the Educational Digital Twin not only facilitates the dynamic integration of new data but also scales efficiently to handle complex datasets covering millions of students across hundreds of demographic and academic dimensions.