ABSTRACT
This chapter examines the analytical models to predict and explain the reasons behind freshmen student attrition. It describes a quantitative research approach where the historical institutional data from student databases is used to develop models that are capable of predicting, as well as explaining, the institution-specific nature of the attrition problem. The chapter explains prediction models using three main types of ensembles: bagging, busting, and information fusion. For quite some time, data analytics projects were carried out as experimental or trial-and-error endeavors. Because the data mining is driven by experience and experimentation, depending on the problem situation and the skills, knowledge, and experience of the analyst, the whole process can be very iterative and time demanding. Student retention is a critical part of many enrollment management systems. Affecting university rankings, school reputation, and financial well-being, student retention has become one of the most important priorities for decision makers in higher education institutions.
