Using Machine Learning to Identify At-risk Students in an Introductory Programming Course at a Two-year Public College

Abstract

In the United States, more than one-third of students enrolling in introductory computer science programming courses (CS101) do not succeed. To improve student success rates, this research team used supervised machine learning to identify students who are “at risk” of not succeeding in CS101 at a two-year public college. The resultant predictive model accurately identifies ≈99% of at-risk students in an out-of-sample test dataset. The programming instructor piloted the use of the model’s predictive factors as early alert triggers to intervene with individualized outreach and support across three course sections of CS101 in fall 2020. The outcome of this pilot study was a 23% increase in student success and a 7.3 percentage point decrease in the DFW rate. More importantly, this study identified academic, early alert triggers for CS101. The first two graded programs are of paramount importance for student success in the course.