  Educational Data Mining (EDM) is a developing discipline, concerned with
expanding the classical Data Mining (DM) methods and developing new methods for
discovering the data that originate from educational systems. Student
attendance in higher education has always been dealt with in a classical way,
educators rely on counting the occurrence of attendance or absence building
their knowledge about students as well as modules based on this count. This
method is neither credible nor does it necessarily provide a real indication of
a student performance. This study tries to formulate the extracted knowledge in
a way that guarantees achieving accurate and credible results. Student
attendance data, gathered from the educational system, were first cleaned in
order to remove any randomness and noise, then various attributes were studied
so as to highlight the most significant ones that affect the real attendance of
students. The next step was to derive an equation that measures the Student
Attendance Credibility (SAC) considering the attributes chosen in the previous
step. The reliability of the newly developed measure was then evaluated in
order to examine its consistency. Finally, the J48 DM classification technique
was utilized in order to classify modules based on the strength of their SAC
values. Results of this study were promising, and credibility values achieved
using the newly derived formula gave accurate, credible, and real indicators of
student attendance, as well as accurate classification of modules based on the
credibility of student attendance on those modules.
