Qing Cai is a doctoral student in the Social Research Methodology cluster at Berkeley School of Education. As a passionate researcher and reading advocate, her work focuses on bi-literacy, measurement and assessment of reading.
Qing’s research focuses on the challenges in reading comprehension, with a specific emphasis on decoding skills. She is interested in leveraging a range of methodologies, including Item Response Theory (IRT), Generalized Linear Mixed Models (GLMM), Bayesian Knowledge Tracing (BKT), and machine learning techniques, to understand how children develop decoding abilities and how educators can effectively support their progress. Her goal is to create an effective leveling and intervention system that improves decoding education and offers practical solutions to help budding readers thrive.
Qing is also deeply committed to the value of linguistic diversity and cultural awareness, believing that these principles play a crucial role in shaping well-rounded, self-aware individuals. Through her research, she seeks to gather evidence to guide emergent bilinguals, fostering confidence and pride in their home languages while ensuring their success in achieving biliteracy.
Before joining Berkeley, Qing gained valuable experience in project management and entrepreneurship, which has shaped her inherently project-based research approach. Through her work, Qing hopes to bridge the gap between research and community, making literacy education more accessible to every child.
Specializations and Interests
Reading Measurement and Assessment; Biliteracy; Item Response Theory; Hierarchical Linear Modeling; Data Analysis
