Decision tree analysis is a popular data mining technique
that can be used in many areas of education. In this study,
decision trees are used to make important design decisions
and explain the interdependencies among the properties of
drop out students. This study also provides examples of how
data mining technique can be used to improve the
effectiveness and efficiency of the modeling process.
This study is an extension of the educational model
developed and published in the information technology
journal[1] . The main contribution in this study is addressing
the capabilities and strengths of data mining technology in
identifying drop out students and to guide the teachers to
concentrate on appropriate features associated and counsel
the students or arrange for financial aid to them.