Staying Ahead of the Curve: Early Prediction of Academic Probation among First-Year CS Students

Application processes in higher education, particu-larly in fields like Computer Science (CS), have become more competitive in recent years. Additionally, at the University of California, Irvine (U CI), 57 % of students who were admitted and enrolled in the CS program between Fall 2011 and Fall 2015 failed to maintain satisfactory academic standing and entered academic probation. Of these probation students, 41 % entered probation during their first year. The objective of this study is to explore the feasibility of using machine learning tools to identify the academic probation status of first-year CS students at an early stage, with the ultimate aim of providing timely support to those who are at the greatest risk of entering academic probation. We trained various classifiers on a dataset containing demo-graphic and academic performance variables for CS students at U CI, and performed feature analysis to identify the most influential variables. Since it is our priority to identify students who enter academic probation, our focus was on maximizing recall. The best-performing classifiers were Linear Support Vector Machines (recall of 0.838) and Logistic Regression (recall of 0.812). Our feature analysis revealed that ethnicity, student region, and having taken the Advanced Placement CS exam had a greater impact on the classification task than GPA and summer course enrollment. Future work could focus on expanding the dataset to include academic data available after enrollment to improve our current results. Our findings could also be applied to assess their impact in strategies for student retention in CS. Overall, this study demonstrates the potential for machine learning tools to assist in identifying at-risk students in CS, and highlights the importance of considering a range of demographic and academic variables in this process.