The Statistical Learning and Behavioral Modeling Lab (SLBM Lab) focuses on developing novel quantitative and computational approaches to address complex, real-world educational and psychological problems.

Our research follows two complementary directions:

• First, our primary goal is to advance quantitative methodology, including change point analysis, item response theory, cognitive diagnosis modeling, educational and psychological measurement, as well as modern machine learning and artificial intelligence methods.
• Second, the lab contributes to the design and development of research platforms that integrate quantitative methods with information technology to support data collection and methodological validation.

Currently, the lab has developed, and is continuing to develop, several research infrastructures, including a large-scale educational platform, an automated personalized learning platform, and ecological momentary assessment systems. These platforms serve as test beds for developing, evaluating, and refining quantitative methodologies, particularly modern AI methods that require large-scale, high-quality data.

By closely linking methodological innovation with platform implementation and empirical validation, the lab builds shared research infrastructure that supports interdisciplinary collaboration across psychology, education, data science, and related fields, facilitating cross-departmental and campus-wide partnerships.