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Joe Olsen; Amy Adair; Janice Gobert; Michael Sao Pedro; Mariel O'Brien – Grantee Submission, 2022
Many national science frameworks (e.g., Next Generation Science Standards) argue that developing mathematical modeling competencies is critical for students' deep understanding of science. However, science teachers may be unprepared to assess these competencies. We are addressing this need by developing virtual lab performance assessments that…
Descriptors: Mathematical Models, Intelligent Tutoring Systems, Performance Based Assessment, Data Collection
Chengyu Cui; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap,…
Descriptors: Item Response Theory, Accuracy, Simulation, Psychometrics
Arenson, Ethan A.; Karabatsos, George – Grantee Submission, 2017
Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. We propose a simple and more flexible Bayesian nonparametric IRT model…
Descriptors: Bayesian Statistics, Item Response Theory, Nonparametric Statistics, Models