Grand Challenges: Using Machine Learning to Measure and Improve Equity in K-12 Mathematics Classrooms

Summary:

Persistent achievement gaps between different racial and ethnic groups are a stubborn feature of U.S. education systems. Recent advances in machine learning and natural language processing afford an unprecedented opportunity to support instruction in a way that can disrupt existing inequality. Building on a recent project that won a global education technology award, this proposed interdisciplinary study combines cutting-edge machine learning techniques, rich educational theory, and behavioral sciences to deliver an effective, affordable, and scalable mechanism to measure and improve equity-focused teaching practices in K-12 mathematics classrooms. Through a randomized controlled trial that evaluates the effectiveness of our tool and dedicated efforts to improve the performance and reduce bias in the machine learning technology used in our setting, we address the intersection of two grand challenges faced by our modern society — social and racial injustice and ethical, fair, and trustworthy technology.

Team Members: