Accurate prediction and early identification of student at-risk of attrition are of high concern
for higher educational institutions (HEIs). It is of a great importance not only to the students but
also to the educational administrators and the institutions in the areas of improving academic
quality and efficient utilisation of the available resources for effective intervention. However,
despite the different frameworks and models that various researchers have used across
institutions for predicting performance, only negligible success has been recorded in terms of
accuracy, efficiency and reduction of student attrition. This has been attributed to the
inadequate and selective use of variables for the predictive models. This paper presents a multidimensional
and holistic framework for predicting student academic performance and
intervention in HEIs. The purpose and functionality of the framework are to produce a
comprehensive, unbiased and efficient way of predicting student performance that its
implementation is based upon multi-sources data and database system. The proposed approach
will be generalizable and possibly give a prediction at a higher level of accuracy that
educational administrators can rely on for providing timely intervention to students.