Decision Tree-Based Predictive Models for Academic Achievement Using College Students' Support Networks
This work addresses predicting academic outcomes for college students using social network data, but it is incremental as it applies existing decision tree and random forest methods to a new dataset without major methodological advances.
The study tackled predicting college students' academic achievement (self-reported GPA) using support network data from 484 students during the COVID-19 pandemic, finding that different types of support (e.g., educational vs. emotional, routine vs. intense) were important predictors for different demographic groups such as White vs. non-White students and cisgender women vs. men.
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.