"Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success"

Fourth-year doctoral student Renzhe Yu (right) and Catherine Kung, undergraduate student in Information and Computer Science and Yu’s mentee, published an article in the Proceedings of the Seventh ACM Conference on Learning @ Scale.

The title of the article is “Interpretable Models Do Not Compromise Accuracy or Fairness in Predicting College Success.”

Yu is specializing in Language, Literacy, and Technology. His research interests include learning analytics, learning sciences, instructional design, and computational modeling. He is focusing on advanced computational methods to model and decipher student learning processes from the rich data available in various digital learning environments, with the goal of improving instructional design based on such empirical evidence. Yu is advised by Professor Mark Warschauer.

​Abstract

​The presence of "big data" in higher education has led to the increasing popularity of predictive analytics for guiding various stakeholders on appropriate actions to support student success. In developing such applications, model selection is a central issue. As such, this study presents a comprehensive examination of five commonly used machine learning models in student success prediction. Using administrative and learning management system (LMS) data for nearly 2,000 college students at a public university, we employ the models to predict short-term and long-term academic success. Beyond the tradeoff between model interpretability and accuracy, we also focus on the fairness of these models with regard to different student populations. Our findings suggest that more interpretable models such as logistic regression do not necessarily compromise predictive accuracy. Also, they lead to no more, if not less, prediction bias against disadvantaged student groups than complicated models. Moreover, prediction biases against certain groups persist even in the fairest model. These results thus recommend using simpler algorithms in conjunction with human evaluation in instructional and institutional applications of student success prediction when valid student features are in place.