Machine Learning-based Predictive Analytics of Student Academic Performance in STEM Education

Abstract

Machine Learning (ML) is expected, in the near future, to provide various venues and effective tools to improve education in general, and Science-Technology-Engineering- Mathematics (STEM) education in particular. The Gartner Analytics Ascendancy Model requires the use of four types of data analytics to be considered comprehensive: descriptive, diagnostic, predictive and prescriptive data analytics. This paper presents the outcomes of a research and development project at Bradley University (Peoria, IL, USA) aimed at the setup and benchmarking of eight ML algorithms for predictive learning analytics, specifically, a prediction of student academic performance in a course. The analyzed and tested ML algorithms include linear regression, logistic regression, k- nearest neighbor classification, naive Bayes classification, artificial neural network regression and classification, decision tree classification, random forest classification, and support vector machine classification. Based on the obtained accuracy of the analyzed and tested ML algorithms, we have formulated a set of recommendations for faculty and practitioners in terms of selection, setup and utilization of ML algorithms in predictive analytics in STEM education. We also performed formative and summative surveys of undergraduate and graduate students in Computer Science and Computer Information Systems courses to understand their opinion about utilization of ML-based predictive analytics in education; a summary of obtained student feedback is presented in this paper.