Enhancing FinTech engineering education through knowledge graph integration in higher education

How can we enhance the education of financial technology (FinTech) data engineers to meet the evolving demands of engineering roles in the Greater Bay Area? This study proposes a virtual-physical integration model based on the engineering knowledge graph (KG). The model integrates virtual simulation-based teaching with real-world engineering practices in industry and development by leveraging KGs. This approach aims to elevate learners’ theoretical understanding, practical experience, and comprehensive engineering capabilities to align with the emerging requirements of the industry. The study begins by highlighting the significance, advantages, and potential applications of engineering knowledge graphs in education by analyzing the research background, objectives, and implications. It then details the theoretical framework, principles, and design components underlying the virtual-physical integration model. Using this model, learners engage in simulation-based training and hands-on problem-solving in hybrid environments, guided by KGs. A quantitative evaluation demonstrates how this approach effectively bridges the gap between educational outcomes and industry expectations, optimizing engineering capabilities. Finally, the paper discusses the strengths and limitations of the model and outlines prospects for future research, aiming to serve as a practical framework for cultivating exceptional FinTech data engineering talent.