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

Abstract

Nationally, more than one-third of the students who enroll in an introductory computer science programming course (CS1) do not succeed. To improve student success rates, super-vised machine learning is used to identify students who are “at risk” of not succeeding in CS1 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 course 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 CS1 in fall 2020. The outcome of this pilot study was a 23% increase in student success and a 7.3% decrease in the DFW rate (i.e. the percentage of students who receive a D, receive an F, or withdraw). More importantly, this study identified academic-based early alert triggers for CS1. The first two graded programs are of paramount importance for student success in this course.