Education researchers are increasingly interested in applying data mining approaches, but to date, there has been no overarching exposition of their methodological advantages and disadvantages to the field. This is partly because the use of data mining in education research is relatively new, so its value and consequences are not yet well understood. Yet statisticians, sociologists and those who study computerbased education have discussed the methodological merits of data mining in education research. This article brings together their perspectives, providing an interdisciplinary overview of potential benefits and drawbacks. The benefits, regardless of scholar background, largely emphasize the speed and ease with which data mining approaches can help explore very large datasets. Perceived drawbacks, however, differ based on disciplinary expertise. For example, statisticians question data mining’s exploratory nature and non-reliance on sampling theory, while sociologists raise concerns about an excessive reliance on data in research designs and in understandings of education.