A Critical Exploration of the Potential Utility of Rule-Induction Data Mining Methods to "Orthodox" Education Research

• In Event: 54.018 - Promising Scholarship in Education: Dissertation Fellows and Their Research

Abstract

Despite its theoretical promise, it is unclear whether rule-induction data mining approaches add any methodological value to "more orthodox" research in Education, i.e., research unrelated to computer-based education. To better understand whether and how rule-induction methods could be useful to Education researchers, I explored whether rule-induction methods, relative to regression approaches, (1) improve classification accuracy, and/or (2) offer new avenues of explanation through their unique ability to generate if-then rules about subgroups. My secondary aim has been methodological—to illustrate a practical and principled way to use the various rule-induction approaches that are available in Education research, so researchers can more easily choose to use it. To these ends, I conducted an extended literature review on rule-induction methods, and have been re-analyzing two regression studies (Byrnes & Miller, 2007; Thomas, 2006) on the National Educational Longitudinal Study of the Eight Grade Class of 1988 (United States Department of Education. National Center for Education, 2006) using ten rule-induction approaches. Data mining happened in two rounds for each study: first, by using only the predictors used in the original study, and second by using all reasonable and available predictors.

Author

• Emi Iwatani, Digital Promise