Identifying Prerequisite Courses in Undergraduate Biology Using Machine Learning

Lee, Youngjin

doi:10.6339/22-JDS1068

Journal of Data Science

Identifying Prerequisite Courses in Undergraduate Biology Using Machine Learning

Volume 21, Issue 4 (2023), pp. 745–760

Youngjin Lee

https://doi.org/10.6339/22-JDS1068

Pub. online: 20 October 2022 Type: Data Science In Action

Open Access

Received
6 May 2022

Accepted
16 September 2022

Published
20 October 2022

Abstract

Many undergraduate students who matriculated in Science, Technology, Engineering and Mathematics (STEM) degree programs drop out or switch their major. Previous studies indicate that performance of students in prerequisite courses is important for attrition of students in STEM. This study analyzed demographic information, ACT/SAT score, and performance of students in freshman year courses to develop machine learning models predicting their success in earning a bachelor’s degree in biology. The predictive model based on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) showed a better performance in terms of AUC (Area Under the Curve) with more balanced sensitivity and specificity than Logistic Regression (LR), K-Nearest Neighbor (KNN), and Neural Network (NN) models. An explainable machine learning approach called break-down was employed to identify important freshman year courses that could have a larger impact on student success at the biology degree program and student levels. More important courses identified at the program level can help program coordinators to prioritize their effort in addressing student attrition while more important courses identified at the student level can help academic advisors to provide more personalized, data-driven guidance to students.

Supplementary material

Supplementary Material

This includes the data file containing the training and test sets analyzed in the study, and all R code used in the analysis along with an explanatory README.txt file.

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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

attrition Educational Data Mining Learning Analytics STEM education student success

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