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The Fusion Model for Skills Diagnosis: Blending Theory With Practicality

Hartz, Sarah; Roussos, Louis
Publication Year:
Report Number:
ETS Research Report
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Subject/Key Words:
Blocking Formative Assessment Fusion Model Item Response Theory (IRT) Markov Chain Monte Carlo (MCMC) Model Fit Q Matrix Robustness (Statistics) Simulation Skills Diagnosis Stepwise Algorithm


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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