Olugbenga Adejo and Thomas Connolly, University of the West of Scotland, United Kingdom

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.

Prediction, Student performance, Higher education, Holistic framework.