Exploring Algorithmic Fairness and Potential Bias in K-12 Mathematics Adaptive Learning (2020–2023)
This NSF-funded project explores algorithmic bias in widely-used educational software. The unique focus of this project is that we use open-ended surveys and interviews with students and their teachers to measure student identity in the classroom at a very fine-grained level. For example, students might identify themselves by race, ethnicity, language, culture, religion, or many other dimensions that could related to how they interact with each other in the classroom and with the educational software they use. We then apply various methods to measure the bias of algorithms that are used to do things like automatically select which problem a student should work on next. Ultimately, we will explore bias-reduction methods based on these findings to see how much fairer algorithms can improve learning for students from all different backgrounds.
Advancing Computational Grounded Theory for Audiovisual Data from STEM Classrooms (2019–2022)
In this NSF-funded project, we are exploring mathematics classroom video and audio recordings with two approaches. In one approach we use machine learning methods to automatically extract keypoints (positions of parts of the body) from video and features from audio. We are using high-level summaries of these features to perform data-driven discovery of activities, instructional formats, and events in classrooms. In the other approach, expert annotators analyze the datasets with similar discovery goals. We then examine the convergence and validity of these two approaches, focusing especially on how computational methods can be developed to assist and expand on the abilities of researcher annotators.
Underrepresented Student Learning in Online Introductory STEM College Courses (2018–2021)
This IES-funded project focuses on discovering how students from demographic groups that are traditionally underrepresented in STEM (Science, Technology, Engineering, and Math) differ from their peers in terms of how they interact with online college STEM courses. For example, we are investigating differences in usage patterns for various course features (asking questions in discussion forums, retrying assignments, etc.) to determine which correlate with success, how usage differs across demographics, and how these differ across courses. Ultimately, this exploratory project will inform future work focused on experimentally testing hypotheses derived from observations in these online courses.
This project is in conjunction with the ILEARN group of researchers at UIUC.
Supporting Self-regulated Learning in Online Education via Automatically Personalized Interventions (2020–2021)
This is a one-year pilot data collection project funded by the Technology Innovation in Educational Research and Design (TIER-ED) initiative. The goal of this pilot is to demonstrate the feasibility of a new method for performing self-regulated learning (SRL) interventions. Specifically, SRL behaviors (such as re-reading materials before taking a quiz) are modeled jointly with student outcomes (such as learning a lot or not very much) via machine learning and model interpretability methods in a simple online learning environment designed to capture SRL behaviors. Interventions are then tailored for students to suit both their expected learning outcomes as well as the SRL behaviors that contribute to those outcomes.