  Early identification of college dropouts can provide tremendous value for
improving student success and institutional effectiveness, and predictive
analytics are increasingly used for this purpose. However, ethical concerns
have emerged about whether including protected attributes in the prediction
models discriminates against underrepresented student groups and exacerbates
existing inequities. We examine this issue in the context of a large U.S.
research university with both residential and fully online degree-seeking
students. Based on comprehensive institutional records for this entire student
population across multiple years, we build machine learning models to predict
student dropout after one academic year of study, and compare the overall
performance and fairness of model predictions with or without four protected
attributes (gender, URM, first-generation student, and high financial need). We
find that including protected attributes does not impact the overall prediction
performance and it only marginally improves algorithmic fairness of
predictions. While these findings suggest that including protected attributes
is preferred, our analysis also offers guidance on how to evaluate the impact
in a local context, where institutional stakeholders seek to leverage
predictive analytics to support student success.
