Machine learning techniques in higher education are used to help universities, colleges, instructors and the students getting better in their performance, consequently enveloped in term called Educational Data Mining (EDM). EDM is contributing to education and education research in a multitude of ways, as can be seen from the diversity of educational problems considered in the subsequent chapters of this book. EDM s contributions have influenced thinking on pedagogy and learning, and have promoted the improvement of educational software, improving software's capacity to individualize students learning experiences. These contributions in education build off of data mining's past impacts in other domains such as commerce and biology. In some ways, the advent of EDM can be considered as education "catching up" to other areas, where improving methods for exploiting data have promoted transformative impacts in practice.