Predictive studies have been widely undertaken in the field of education to provide strategic information about the extensive set of processes related to teaching and learning, as well as about what variables predict certain educational outcomes, such as academic achievement or dropout. As in any other area, there is a set of standard techniques that is usually used in predictive studies in the field education. Even though the Decision Tree Method is a well-known and standard approach in Data Mining and Machine Learning, and is broadly used in data science since the 1980's, this method is not part of the mainstream techniques used in predictive studies in the field of education. In this paper, we support a broad use of the Decision Tree Method in education. Instead of presenting formal algorithms or mathematical axioms to present the Decision Tree Method, we strictly present the method in practical terms, focusing on the rationale of the method, on how to interpret its results, and also, on the reasons why it should be broadly applied. We first show the modus operandi of the Decision Tree Method through a didactic example; afterwards, we apply the method in a classification task, in order to analyze specific educational data. Accessed 2,463 times on https://pareonline.net from November 06, 2017 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.