An integrated system framework for predicting students' academic performance in higher educational institutions

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 various models that 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 multi-dimensional and an integrated system framework that involves considerable learners’ input and engagement in predicting their 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. It makes use of student demographic and learning management system (LMS) data from the
institutional databases as well as the student psychosocial-personality (SPP) data from the survey collected from the student to predict performance. The proposed approach will be robust, generalizable, and possibly give a prediction at a higher level of accuracy that educational administrators can rely on for
providing timely intervention to students.