期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2017
卷号：9
期号：3
页码：149
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 concern for highereducational institutions (HEIs). It is of a great importance not only to the students but also to theeducational administrators and the institutions in the areas of improving academic quality andefficient utilisation of the available resources for effective intervention. However, despite the differentframeworks 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 studentattrition. 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 considerablelearners’ input and engagement in predicting their academic performance and intervention in HEIs. Thepurpose and functionality of the framework are to produce a comprehensive, unbiased and efficient way ofpredicting student performance that its implementation is based upon multi-sources data and databasesystem. It makes use of student demographic and learning management system (LMS) data from theinstitutional databases as well as the student psychosocial-personality (SPP) data from the survey collectedfrom the student to predict performance. The proposed approach will be robust, generalizable, andpossibly give a prediction at a higher level of accuracy that educational administrators can rely on forproviding timely intervention to students.
