Predicting Student Performance using Cognitive and Non-cognitive Information

Abstract

Higher education is a privilege in developing countries like Pakistan where citizens are fighting even for getting basic education. In the past two decades, more and more students have started to enroll in IT and engineering related programs in Pakistan but a significant number of these students dropout before completing their degrees which results into loss of time, money and seats which could be offered to other deserving students. This problem demands university administrations and educators to device mechanisms through which student drop out rate can be controlled, if not totally eliminated. Besides financial support, one such mechanisms which can help in controlling drop outs is accurate prediction of student performance so that the students on the verge of failing could be identified and alarmed. This will help them in realising the efforts needed to show good academic performance. At present, the prediction methods use academic or cognitive records of students to predict their future performance. Although the non-cognitive and behavioral aspects are critical in improving student performance, their role in prediction is yet to be explored. In this research, an effort is made to improve student performance prediction by predicting performance through combined use of cognitive and noncognitive features. The result analysis of two different data sets has shown that by adding noncognitive variables in prediction, prediction accuracies increase using decision tree algorithm; however the addition does not play significant role in other techniques. The research also highlighted those individual cognitive features which might help students and educators to cater for drop outs.