Application of machine learning on student data for the appraisal of academic performance

Abstract

© 2018 IEEE. Education With the inclusion and integration of internet and digital learning Education 2. 0 brought tools in the different context of education. The use of social networking concepts such as chat rooms and the ever-growing student data have placed education on the brink of becoming one of the craters and users of Big Data. As such, this paper explores educational data mining techniques alongside some of the emerging learning analytics with the objective of gaining insight into some of the common learning behaviors among students. The task at hand embraces predictive analytics and it employs decision trees, neural networks, and Naïve Bayes algorithms to classify and cluster student learning patterns that can explain academic performance. Predictive analytics has emerged as one of the tools furthering adaptive learning among other lifechanging novelties. Nonetheless, integration of big data in academia is in its infancy although the western hemisphere is making progress towards the integration. Such progress will increase the relevance of data mining in education and this paper envisages to be among the first ones to address the applicability of machine learning in improving education. Hence, the objective of this paper is to develop predictive models based on the decision tree, neural network, and Naïve Bayes algorithms.