Digital Transformation and Application of Blended Teaching of Probability Theory and Mathematical Statistics

Abstract

As the digital transformation of education continues to advance, the traditional teaching model of "Probability Theory and Mathematical Statistics" a core course in higher education that combines profound theoretical foundations with practical applicationhas
increasingly revealed pressing challenges such as low teaching efficiency, weak practical components, and difficulties in providing personalized learning guidance. Grounded in the advanced principles of blended learning, this paper explores how to organically integrate digital and
intelligent technologies, including but not limited to artificial intelligence, big data analytics, and virtual simulations, into every aspect of the
course. It proposes a systematic closed-loop teaching model: "Intelligent Pre-class PreparationInteractive In-class ExplorationPersonalized Post-class Extension." Specifically, this involves building a resource repository rich in digital teaching materials, developing an intelligent assessment system with real-time learning feedback, and designing multiple practical virtual experiment scenarios. These efforts aim to
dynamically optimize course content, accurately profile students 'learning processes, and deeply cultivate their practical skills. Practical applications demonstrate that this transformation not only significantly enhances students' learning initiative and enthusiasm but also effectively
improves their ability to apply knowledge and solve real-world problems, providing a feasible reference framework and valuable experience
for the digital and intelligent reform of mathematics courses in higher education.