Student performance in university courses is of great concern to the higher education
managements where several factors may affect the performance. This paper is an attempt to
use the data mining processes, particularly classification, to help in enhancing the quality of
the higher educational system by evaluating student data to study the main attributes that
may affect the student performance in courses. For this purpose, the CRISP framework for
data mining is used for mining student related academic data. The classification rule
generation process is based on the decision tree as a classification method where the
generated rules are studied and evaluated. A system that facilitates the use of the generated
rules is built which allows students to predict the final grade in a course under study