Exploring Algorithmic Fairness and Potential Bias in K-12 Mathematics Adaptive Learning

Students in middle school and high school often use adaptive learning software as part of their math education experience. Adaptive learning software works by automatically measuring how much students have learned about the topic, as well as their learning process and experiences, and then adjusting the instruction accordingly. This project will investigate potential ways in which adaptive learning software might be biased against students from certain groups, and how such biases can be reduced. 

Adaptive learning offers an opportunity to provide high quality instruction that is personalized to the needs of individual learners, but little is known about who benefits most from adaptive learning technologies. This project will address this issue by observing and interviewing students who use adaptive math learning software to discover what aspects of their identity are most salient in the adaptive learning context. This project will then investigate possible algorithmic biases related to the identities that students express. Findings from the project will contribute to understanding of the most relevant aspects of student identity in adaptive learning contexts, and how those identities affect their learning experience. Finally, this project will address the biases that are identified, thereby providing a more equitable mathematics education experience for students.