Uncovering the Most Effective Pedagogical Techniques for Math Education Using Machine Learning

Abstract

Many new math educators express that their first years in the teaching field are extremely challenging. They struggle to discover and apply the most effective teaching techniques and behaviors, often without the support of more experienced colleagues. In this study, we use machine learning to find the strategies that novice teachers can adopt to enhance their teaching effectiveness. The core aim is to uncover the relationship between teachers’ performance, as assessed by student evaluations, and their pedagogical methods. These strategies are derived from the final decision tree model, which is trained on a large dataset of empirical data from schools. The data consists of input from 72 math teachers of grades 7-9 and their students in Dubai, used to train two decision tree models: a classification tree and a regression tree. The structure of these trees is analyzed to identify and rank the effectiveness of nine teaching techniques—such as Visualization, Practice, Math Rules, Gamification, Collaboration, Problem-Solving, Case Studies, Assessments, and Language Switching—and four behavioral methods—such as Inspiration, Engagement, Entertainment, and Bonding— in relation to the Student Evaluation Index (SEI), which is derived from student feedback. Results indicate that techniques such as "gamification" and "inspirational behavior" are consistently associated with higher SEI scores across different tree configurations. However, factors such as the demographics and culture of both students and teachers may need to be considered when generalizing these findings to other regions of the world.