Statistics Courses and Competition-Driven Teaching and Learning: Pathways, Implementation, and Reflections on Integrating Mathematical Modeling

DOI: https://doi.org/10.62517/jhet.202515608

Author(s)

Gaofeng Liu*

Affiliation(s)

School of Mathematics and Statistics, Leshan Normal University, Leshan, Sichuan, China *Corresponding Author

Abstract

Targeting statistics majors, this paper leverages mathematical modeling as a key vehicle to implement a “competition-driven teaching and learning” completion-oriented reform. By reconstructing the course cluster around a “contest problem–course–assessment” closed loop, we build a case library and rubrics, forming an isomorphism between training and assignments and achieving coordination between online and offline learning. An interdisciplinary teaching team is established to embed competition tasks into courses such as Statistical Methods and Data Analysis and Modeling, supported by a provincial first-class course and the Xueyin Online platform. Practice shows that students’ engagement, modeling ability, and collaboration improve significantly, and higher-order course performance rises. Meanwhile, a replicable course roadmap and organizational mechanism are formed, and suggestions are offered for optimizing resource allocation and expanding coverage.

Keywords

Statistics; Mathematical Modeling; Competition-Driven Teaching; Competition-driven Learning

References

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