CODEE Journal

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Using Differential Equations to Engage Students in Data-Driven Modeling and Analysis

Introduction

We are pleased to introduce the Fourth Special Issue of the CODEE Journal dedicated to Using Differential Equations to Engage Students in Data-Driven Modeling and Analysis. This collection of research articles and classroom-ready modules reflects an important evolution in undergraduate differential equation education as classical theory is integrated with real-world data and modern computational practices.

In an era defined by data-driven decision making, differential equations continue to serve as a foundational language for describing dynamical systems. When combined with data analysis techniques, they offer powerful opportunities not only for advancing research, but also for transforming undergraduate education. The contributions in this special issue highlight how integrating empirical data into modeling activities can deepen student understanding, foster engagement, and bridge the gap between theoretical mathematics and practical applications.

The articles presented here span a wide range of perspectives and applications. They explore innovative approaches to parameter estimation and model calibration, the integration of ODEs with machine learning methodologies, and the development of hybrid models that combine mechanistic insight with data-driven components. Contributors also present compelling case studies drawn from fields such as epidemiology, ecology, public health, chronic pain, physics, and engineering. In addition, several works emphasize pedagogical strategies that make data-driven modeling accessible and meaningful in undergraduate classrooms.

Together, these contributions demonstrate how differential equations can serve as a unifying framework for interpreting data, building predictive models, and developing critical analytical skills. By engaging students with real-world problems and datasets, educators can cultivate a deeper appreciation for the relevance and power of mathematics in addressing contemporary challenges.

We would like to express our sincere gratitude to all authors and reviewers, whose dedication and expertise made this special issue possible. Their thoughtful contributions exemplify and uphold the mission of the CODEE Journal. We are also grateful to the broader CODEE community for its continued commitment to excellence in the teaching and learning of differential equations.

We hope that the articles presented in this issue will inspire further innovation, collaboration, and exploration at the intersection of differential equations and data science and will support educators in preparing students for a data-rich and rapidly evolving world.

Iordanka Panayotova* and Viktoria Savatorova** CODEE Journal Co-Editors-in-Chief

*Department of Mathematics, Christopher Newport University
**Department of Mathematical Sciences, Central Connecticut State University