Purpose This paper aims to use a data-driven approach to identify the factors and metrics that provide the best indicators of academic attrition in the Korean language program at the Defense Language Institute Foreign Language Center. Design methodology approach This research develops logistic regression models to aid in the identification of at-risk students in the Defense Language Institute’s Korean language school. Findings The results from this research demonstrates that this methodology can detect significant factors and metrics that identify students at-risk. Additionally, this research shows that school policy changes can be detected using logistic regression models and stepwise regression. Originality value This research represents a real-world application of logistic regression modeling methods applied to the problem of identifying at-risk students for the purpose of academic intervention or other negative outcomes. By using logistic regression, the authors are able to gain a greater understanding of the problem and identify statistically significant predictors of student attrition that they believe can be converted into meaningful policy change.
