Qing Cai is a doctoral student in the Social Research Methodology cluster at Berkeley School of Education. As a passionate psychomatrician-in-training and reading advocate, her work focuses on biliteracy, measurement and assessment of reading.
Qing’s research explores the challenges in reading development, with a particular emphasis on phonemic awareness and decoding skills. She employs a diverse set of analytical approaches--including Item Response Theory (IRT), Generalized Linear Mixed Models (GLMM), Bayesian Knowledge Tracing (BKT), machine learning techniques and generative AI--to better understand how children acquire decoding abilities and how educators can most effectively support this growth. Ultimately, her goal is to develop a data-driven leveling and intervention system that scaffolds decoding instruction and provides practical, equitable solutions to help all budding readers thrive.
Qing is also deeply committed to linguistic diversity and cultural awareness, believing these are essential for cultivating well-rounded, self-aware learners. Through her research, she aims to build evidence-based pathways to guide emergent bilinguals, fostering confidence and pride in their heritage languages while supporting their success in achieving biliteracy.
Before joining Berkeley, Qing gained valuable experience in project management and entrepreneurship, which has shaped her inherently project-based approach to research. Through her work, Qing hopes to bridge the gap between research and community, advancing literacy education that is both innovative and accessible to every child.
Specializations and Interests
Reading Measurement and Assessment; Biliteracy; Item Response Theory; Hierarchical Linear Modeling; Data Analysis
