Learning Analytics and Measurement in Behavioral Sciences (LAMBS)
Director: Ying (Alison) Cheng
Our lab is interested in measurement issues in psychological assessment and educational testing. We specialize in item response theory and its applications to large-scale testing programs. Specific topics of interest include detection of aberrant responses in low- and high-stakes assessments, computerized adaptive testing, cognitive diagnostic modeling, test/item bias, classification accuracy and consistency and so on.
In addition to developing new methodologies, we are very interested in applying advanced statistical methods to substantive areas, such as education research and health outcome research. For example, we develop diagnostic testing platforms and use machine learning methods to provide personalized feedback and targeted learning materials to students in real time.
Our lab is currently recruiting eligible members of the community to participate in research.
Exploring test mode effect (computerized adaptive testing vs. traditional test) on students' performance and anxiety
Participants will take a statistics test and questionnaires online.