出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Accurate prediction and early identification of student at-risk of attrition are of high concernfor higher educational institutions (HEIs). It is of a great importance not only to the students butalso to the educational administrators and the institutions in the areas of improving academicquality and efficient utilisation of the available resources for effective intervention. However,despite the different frameworks and models that various researchers have used acrossinstitutions for predicting performance, only negligible success has been recorded in terms ofaccuracy, efficiency and reduction of student attrition. This has been attributed to theinadequate and selective use of variables for the predictive models. This paper presents a multidimensionaland holistic framework for predicting student academic performance andintervention in HEIs. The purpose and functionality of the framework are to produce acomprehensive, unbiased and efficient way of predicting student performance that itsimplementation is based upon multi-sources data and database system. The proposed approachwill be generalizable and possibly give a prediction at a higher level of accuracy thateducational administrators can rely on for providing timely intervention to students.
