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The Fusion Model for Skills Diagnosis: Blending Theory With Practicality MCMC IRT

Author(s):
Hartz, Sarah; Roussos, Louis
Publication Year:
2008
Report Number:
RR-08-71
Source:
ETS Research Report
Document Type:
Report
Page Count:
57
Subject/Key Words:
Formative Assessment, Skills Diagnosis, Markov Chain Monte Carlo (MCMC), Fusion Model, Model Fit, Stepwise Algorithm, Item Response Theory (IRT), Simulation, Robustness (Statistics), Blocking, Q Matrix

Abstract

This paper presents the development of the fusion model skills diagnosis system (fusion model system), which can help integrate standardized testing into the learning process with both skills-level examinee parameters for modeling examinee skill mastery and skills-level item parameters, giving information about the diagnostic power of the test. The development of the fusion model system involves advancements in modeling, parameter estimation, model-fitting methods, and model-fit evaluation procedures, which are described in detail in the paper. To document the accuracy of the estimation procedure and the effectiveness of the model-fitting and model-fit evaluation procedures, this paper also presents a series of simulation studies. Special attention is given to evaluating the robustness of the fusion model system to violations of various modeling assumptions. The results demonstrate that the fusion model system is a promising tool for skills diagnosis that merits further research and development.

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