Areas of Research
Statistical Machine Learning
Our current research focus is the multidisciplinary field of Computational Psychometrics which uses the methods of psychometrics, cognitive science, usability engineering, mathematical statistics, and statistical machine learning to address problems in understanding and identifying probabilistic models of human knowledge, human learning models, and human information processing.
The team is currently developing a computational psychometric model of student learning in classroom environments.
Recent Talks and Publications
- Fox, C. P. and Golden, R. M. Using cognitive diagnostic modeling to investigate learning taxonomy assumptions. Poster presented at the (Virtual) Society for Mathematical Psychology Conference, Summer 2020.
- Sudheesh, A. and Golden, R. M. Exploring temporal functional dependencies between latent skills in summative assessments. Poster presented at the (Virtual) International Meeting of the Psychometric Society, Summer 2020.
- Malaiya, R. and Golden, R. M. Simulation studies of item bias estimation. Poster presented at the (Virtual) International Meeting of the Psychometric Society, Summer 2020.
- Golden, R. M. New scalable misspecification tests for G-DINA models with many parameters. (Virtual) International Meeting of the Psychometric Society, Summer 2020. [talk]
- Golden, R. M. (2020). Statistical Machine Learning: A Unified Framework. CRC Press.