  One of the long term goals of any college or university is increasing the
student retention. The negative impact of student dropout are clear to
students, parents, universities and society. The positive effect of decreasing
student attrition is also self-evident including higher chance of having a
better career and higher standard of life for college graduate. In view of
these reasons, directors in higher education feel increasingly pressurized to
outline and implement strategies to increase student retention. In this paper,
we provide a detailed analysis of the student attrition problem and use
statistical methods to predict when students are going to dropout from school
using real case data. Our work has a number of advantages with the potential of
being employed by higher education administrator of universities. We take
advantage of multiple kinds of information about different aspects of student's
characteristic and efficiently utilize them to make a personalized decision
about the risk of dropout for a particular student.
