  In this study, we examine a set of primary data collected from 484 students
enrolled in a large public university in the Mid-Atlantic United States region
during the early stages of the COVID-19 pandemic. The data, called Ties data,
included students' demographic and support network information. The support
network data comprised of information that highlighted the type of support,
(i.e. emotional or educational; routine or intense). Using this data set,
models for predicting students' academic achievement, quantified by their
self-reported GPA, were created using Chi-Square Automatic Interaction
Detection (CHAID), a decision tree algorithm, and cforest, a random forest
algorithm that uses conditional inference trees. We compare the methods'
accuracy and variation in the set of important variables suggested by each
algorithm. Each algorithm found different variables important for different
student demographics with some overlap. For White students, different types of
educational support were important in predicting academic achievement, while
for non-White students, different types of emotional support were important in
predicting academic achievement. The presence of differing types of routine
support were important in predicting academic achievement for cisgender women,
while differing types of intense support were important in predicting academic
achievement for cisgender men.
