Early Prediction of First-Term Math Grades using Demographic and Survey Data.

This Work-In-Progress research paper presents the investigation of a decision tree model that was trained to predict engineering students' first-semester math performance using demographic and survey data. This is a small step in a larger project that will develop a predictive AI model that can identify students at risk of leaving engi-neering. Ultimately, we will pair a predictive model with an explanation method to identify targeted interventions that can be implemented within the first year of engineering school. Our findings from this project indicate that we may be able to successfully identify students at risk of low performance in first-semester math courses, and design effective individualized interventions.