Reframing Educational Inquiry: Uncovering the Value of Decision Tree Analysis — A Practical Guide for Educational Researchers

As educational research increasingly grapples with complex, multivariate data, traditional linear models often fall short in capturing the nuanced and conditional relationships that shape learning outcomes. Decision tree analysis (DTA), a non-parametric and interpretable machine learning method, offers a promising alternative. This article explores the potential of decision trees to enhance both the analytical rigor and practical relevance of educational research. It discusses the method’s strengths—flexibility, transparency, and capacity to handle diverse data types—as well as common barriers to adoption, such as concerns about overfitting and perceived methodological complexity. Through a practical guide and discussion of key applications, the article advocates for broader use of DTA as a complement to traditional statistical approaches. By making findings more accessible and actionable, decision trees can support more informed decision-making in education policy and practice.