Fitting statistical models for studying teachers' influence on students' math anxiety
T.J.M. Schelling (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A.J. Cabo – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
M.E. Kootte – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Math anxiety, defined as fear or anxiety in math-related situations, is an increasing problem worldwide. This type of anxiety is experienced not only by students but also by adults, in situations such as paying with cash. This trend is concerning because individuals with high levels of math anxiety often avoid math-related activities, including educational and career opportunities. There is a shortage of professionals in technical fields, and this shortage will not decrease when more math anxiety is present.
Research into how to reduce the math anxiety of people begins at (high) school. Studies have confirmed that high school teachers have an influence on the increase or decrease of students' math anxiety. The key question is how this influence is expressed. Previous studies have investigated this question using a variety of statistical models. The purpose of this study is to determine which statistical model explains the most about the influence of teachers on students' math anxiety.
To investigate this question, data were collected at a high school in the Netherlands via questionnaires for teachers and students. The teacher survey contained the topics: mindset, instruction, and teacher emotions. Students completed questionnaires on their math anxiety and their perceived relationship with their teacher.
Subsequently, three models are constructed. The first model is a linear regression model where the average math anxiety of all students of one teacher is the response variable and the teacher characteristics are the explanatory variables. The second model uses the interpersonal relationship of a class with their teacher as explanatory variables and the average math anxiety per class as the response variable. This is a two-level mixed model. The last model is a three-level mixed-effects model and uses the interpersonal relationship of student and teacher as explanatory variables and the math anxiety of that student as the response variable. Three variants of the student-level model are examined: a model that has clusters on class and teacher levels. And two that only contain one cluster level, a two-level mixed model.
The models were compared using $R^2$ measures, the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). The results indicate that the models based on teacher-level average anxiety and student-level anxiety with teacher clustering provided the best fit to the data. Future research should examine generalized models and validate the findings using larger datasets.