Early identification of at-risk students using iterative logistic regression

Abstract

Higher education institutions are faced with the challenge of low student retention rates and high number of dropouts. 41% of college students in United States do not finish their undergraduate degree program in six years, and 60% of them drop out in their first two years of study. It is crucial for universities and colleges to develop data-driven artificial intelligence systems to identify students at-risk as early as possible and provide timely guidance and support for them. However, most of the current classification approaches on early dropout prediction are unable to utilize all the information from historical data from previous cohorts to predict dropouts of current students in a few semesters. In this paper, we develop an Iterative Logistic Regression (ILR) method to address the challenge of early prediction. The proposed framework is able to make full use of historical student record and effectively predict students at-risk of failing or dropping out in future semesters. Empirical results evaluated on a real-wold dataset show significant improvement with respect to the performance metrics in comparison to other existing methods. The application enabled by this proposed method provide additional support to students who are at risk of dropping out of college.