ERIC Number: EJ1160667
Record Type: Journal
Publication Date: 2017-Nov
Abstractor: As Provided
Advocating the Broad Use of the Decision Tree Method in Education
Gomes, Cristiano Mauro Assis; Almeida, Leandro S.
Practical Assessment, Research & Evaluation, v22 n10 Nov 2017
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.
Descriptors: Predictive Measurement, Statistical Analysis, Decision Making, Foreign Countries, College Students, Data Analysis, Enrollment, Classification
Dr. Lawrence M. Rudner. e-mail: email@example.com; Web site: http://pareonline.net
Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education
Authoring Institution: N/A
Identifiers - Location: Portugal