|Title:||Fair Prediction of College-Student Success Using Multivariate Adaptive Regression Splines|
|Principal Investigator:||Anahideh, Hadis||Awardee:||University of Illinois, Chicago|
|Program:||Statistical and Research Methodology in Education–Early Career [Program Details]|
|Award Period:||2 years (03/1/2022 - 02/29/2024)||Award Amount:||$299,461|
|Type:||Methodological Innovation||Award Number:||R305D220055|
Co-Principal Investigator: Gandara, Denisa
The purpose of this project is to develop a new fair Multivariate Adaptive Regression Splines (MARS) statistical model to predict college-student success. MARS is a parsimonious non-parametric regression model that can identify useful input variables through a built-in feature-selection step when many potential variables are considered. MARS also renders an easily interpretable model, making it more helpful for use in higher education settings.
The research team will develop the model for this project by decorrelating sensitive and non-sensitive attributes and incorporating fairness as a constraint in the optimization step of the MARS algorithm. They will use a proactive auditing approach to assess and calibrate fairness of the model. After developing the model, the researchers will use it on data from the Education Longitudinal Study of 2002 (ELS:2002) and Integrated Postsecondary Education Data System (IPEDS) to evaluate its effectiveness at predicting students' likelihood of success. The researchers will also develop and post to Github open-source, user-friendly software in Python with a comprehensive user guide for researchers and education practitioners. The team will also disseminate their work at conferences and through peer-reviewed journal manuscripts.