Today’s guest is Dr. Qiwei He, an Adjunct Lecturer in the Department of Mathematics and Statistics at Georgetown University and a research scientist at ETS. With a background in linguistics, social sciences, and data-driven research, Dr. He focuses on revolutionizing education through innovative assessment methods. Her work explores how open-ended questions and process-based data can enhance our understanding of how students learn, collaborate, and solve problems.

In this episode, we explore Dr. He’s groundbreaking research in using process data and sequence mining to analyze how students approach problem-solving. Unlike traditional standardized tests, her methods capture the journey—not just the outcome—of how students think and strategize. This approach holds promise for providing personalized feedback to students and teachers, as well as improving curriculum design to foster critical thinking.

Dr. He also shares insights into collaborative problem-solving, a key 21st-century skill, and how it can be assessed using computer-based simulations. We discuss the challenges and opportunities of implementing machine learning in grading and educational policy, emphasizing the importance of validation and bias mitigation. Whether you’re an educator, student, or policy enthusiast, this episode offers a fresh perspective on harnessing technology to create more equitable and effective learning experiences.