Use of Machine Learning to Measure the Influence of Behavioral Factors on Academic Performance of Higher Education Students

Abstract

Quality of education and improvement of school achievement has been linked to students’ cognitive and behavioral factors. Several researchers have investigated the correlation between these factors and students’ academic performance. Particularly, it is assumed that behavioral factors, such as study habits and self-esteem, have a positive and high relationship with students’ academic achievement. However, research studies have shown a weak and inconsistent correlation level. In this article we present and discuss the results from two studies on the influence of study habits and self-esteem on the academic performance of 153 college freshman students. First, we analyzed the linear correlation between our target variables; similar to previous work, we found a weak positive relationship. Second, by using K-means, an unsupervised clustering algorithm, we created a set of student behavioral profiles: low, medium and high. We found that 80% of the students with a high level of self-esteem and study habits (high behavioral profile) obtained a good or outstanding academic performance; outperforming students within the medium and low behavioral profiles by a significantly margin