  The ability to recognize students weakness and solve any problem that may
confront them in timely fashion is always a target for all educational
institutions. Thus, colleges and universities implement the so-called academic
advising affairs. On the academic advisor relies the responsibility of solving
any problem that may confront students learning progress. This paper shows how
the adviser can benefit from data mining techniques, namely decision trees
techniques. The C 4.5 algorithm is used as a method for building such trees.
The output is evaluated based on the accuracy measure, Kappa measure, and ROC
area. The difference between the registered and gained credit hours is
considered as the main attribute on which advisor can rely
