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

Frazier, Anthony; Silva, Joethi; Meilak, Rachel; Sahoo, Indranil; Broda, Michael; Chan, David

doi:10.6339/21-JDS1033

Journal of Data Science

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

Volume 21, Issue 3 (2023): Special Issue: Advances in Network Data Science, pp. 557–577

Anthony Frazier Joethi Silva Rachel Meilak All authors (6)

https://doi.org/10.6339/21-JDS1033

Pub. online: 30 December 2021 Type: Data Science In Action

Open Access

Received
15 September 2021

Accepted
27 November 2021

Published
30 December 2021

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.

Supplementary material

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.

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2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.

Open access article under the CC BY license.

Keywords

cforest CHAID conditional inference trees egocentric network perceived social support support network

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