Tuesday, October 1, 2024
2:00 - 4:00 PM (PDT) at Berkeley Way West 1207 and via Zoom
Open to GSE faculty, students, and community.
Request a zoom link from convenors@bear.berkeley.edu
Abstract:
Learning Progression describes the specific link between the waypoints of the constructs
while learners proceed in a content domain. Today I will talk about various ways to
investigate and possibly validate Learning Progressions. Possible methods include: (1)
Multidimensional IRT models (2) Structured Construct models using Change point analysis
and (3) Structured Mixture IRT models. Simulation studies were done to evaluate parameter
recovery and model misspecification. Data from The Assessing Data Modeling and Statistical
Reasoning (ADMSR) project will serve as a didactic example.
About the authors:
Yunting Liu is a third-year doctoral student in the Social Research Methodology (SRM)
cluster at UC Berkeley. Additionally, she serves as a Graduate Student Researcher (GSR) at
the BEAR Center, contributing to the Critical Reasoning for College Readiness (CR4CR)
project, with a focus on the Computational Thinking (CoT) strand. Her research interests are
centered around latent variables modeling, longitudinal modeling, and GenAI in education.