Decision Tree-Based Predictive Models for Academic Achievement Using College Students' Support Networks

TL;DR

This study uses decision tree models to predict college students' GPA based on their demographic and support networks, revealing different predictors for diverse student groups during the COVID-19 pandemic.

Contribution

It introduces a comparative analysis of decision tree and random forest models for predicting academic achievement using support network data.

Findings

Different support types predict GPA for White and non-White students.

Routine support predicts GPA for cisgender women, intense support for cisgender men.

Models show variable importance differs across student demographics.

Abstract

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.