This research project will address fundamental statistical issues in Structured Latent Attribute Models (SLAMs). These models serve as a basis for developing diagnostic-based assessments in education, psychology, and other social and behavioral sciences. This CAREER award will advance the theoretical and computational development of these models. The project also will contribute to improved understanding of cognitive processes involved in cognitive assessments and learning sciences. From a societal perspective, the new methods will be applied to various educational studies. The project will develop a diagnostic-based learning tool to identify specific problems and difficulties that students encounter in STEM domains and provide students with useful feedback. The investigator will engage in K-12 educational outreach in collaboration with the University of Michigan's Museum of Natural History, participating in activities such as the Science Communication Fellows Program and the Science for Tomorrow Program. Both undergraduate and graduate students will be involved in the conduct of this research. Publicly available software also will be developed.

With the creation of increasingly rich diagnostic datasets, great challenges are posed on existing SLAM theories and techniques. This project will focus on a number of open questions. First, the project will address the fundamental identifiability issue of SLAMs. A new theoretical framework will be developed as a scaffold to show the identifiability results for SLAMs, which will provide practical guidelines for future diagnostic designs. Second, the project will develop novel procedures to address the challenge in the estimation of the latent structures in SLAMs with high-dimensional latent attributes. Third, the project will develop powerful and robust statistical inference procedures for SLAMs with high-dimensional latent attributes and observed covariates. In addition to theoretical and methodological developments, the project will provide practitioners with a computational tool to explore and expand the use of SLAMs.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Agency
National Science Foundation (NSF)
Institute
Division of Social and Economic Sciences (SES)
Application #
1846747
Program Officer
Cheryl Eavey
Project Start
Project End
Budget Start
2019-08-01
Budget End
2024-07-31
Support Year
Fiscal Year
2018
Total Cost
$182,288
Indirect Cost
Name
Regents of the University of Michigan - Ann Arbor
Department
Type
DUNS #
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109