Artificial Neural Networks for Educational Data Mining in Higher Education: A Systematic Literature Review

Efforts to raise the bar of higher education so as to respond to dynamic societal/industry needs have led to a number of initiatives, including artificial neural network (ANN) based educational data mining (EDM) inclusive. With ANN-based EDM, humongous amount of student data in higher institutions could be harnessed for informed academic advisory that promotes adaptive learning for purposes of student retention, student progression, and cost saving. Mining students' data optimally requires predictive data mining tool and machine learning technique like ANN. However, despite acknowledging the capability of ANN-based EDM for efficiently classifying students' learning behavior and accurately predicting students' performance, the concept has received less than commensurate attention in the literature. This seems to suggest that there are gaps and challenges confronting ANN-based EDM in higher education. In this study, we used the systematic literature review technique to gauge the pulse of researchers from the viewpoint of modeling, learning procedure, and cost function optimization using research studies. We aim to unearth the gaps and challenges with a view to offering research direction to upcoming researchers that want to make invaluable contributions to this relatively new field. We analyzed 190 studies conducted in 2010–2018. Our findings reveal that hardware challenges, training challenges, theoretical challenges, and quality concerns are the bane of ANN-based EDM in higher education and offer windows of opportunities for further research. We are optimistic that advances in research along this direction will make ANN-based EDM in higher education more visible and relevant in the quest for higher education-driven sustainable development.