A Mathematical Modelling Approach to Analyze the Dynamics of Math Anxiety
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The main objective of this study is to develop a mathematical modeling framework for a deeper understanding of dynamics of math anxiety as a contagious process. Borrowing from theories of the spread of infectious disease, we develop two classes of mathematical models representing the spread of math anxiety in math gateway classes. The first mathematical model does not entirely fit with our collected data of math anxiety (n=53, Calculus II & III summer of 2020). However, the second mathematical model, which is a generalization of the first model, can exhibit periodic solutions as observed in the collected data. In addition to the mathematical modeling framework, we have applied a variety of statistical methods and models to analyze the survey data. This includes descriptive analysis of the data, correlation and hypothesis testing, and a machine learning approach, which utilizes the classification and regression tree models to identify key factors associated with math anxiety. These regression tree models include factors such as gender, academic level, number of hours studied, motivation, and confidence. In conclusion, the present work lays the foundation for applying mathematical models to measure the spread of math anxiety in gateway STEM courses.
Table of Contents
Introduction to math anxiety -- inference and math anxiety data -- Modelling math anxiety using machine learning -- Mathematical modelling of math anxiety -- Advanced mathematical modelling and analysis -- Conclusion and future work