JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1033 10.6339/21-JDS1033 Data Science in Action Decision Tree-Based Predictive Models for Academic Achievement Using College Students’ Support Networks FrazierAnthony1 SilvaJoethi2 MeilakRachel3 https://orcid.org/0000-0002-4960-7984 SahooIndranilsahooi@vcu.edu4∗ BrodaMichael5 ChanDavid6 Department of Mathematics, Weber State University, USA Department of Statistics, George Mason University, USA Department of Mathematics, Loyola Marymount University, USA Department of Statistical Sciences & Operations Research, Virginia Commonwealth University, USA Department of Foundations of Education, Virginia Commonwealth University, USA Department of Mathematics, Virginia Commonwealth University, USA Corresponding author. Email: sahooi@vcu.edu. 202330122021213557577 Supplementary Material

Supplemental material linked to the online version of the paper includes R codes implementing the CHAID and cforest algorithms and an example dataset used to demonstrate the codes.

159202127112021 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2023 Open access article under the CC BY license.

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.

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