Using Mixed-Effects Modeling to Compare Different Grain-Sized Skill Models

Mingyu Feng, Neil Heffernan, Murali Mani, Cristina Heffernan

Most assessments, like the math subtest of the SAT or the GRE, are unidimensional, in that they treat all questions on the test as sampling a single underlying "skill." Can we predict state tests scores better if we tag the questions with fine-grained models the skills needed? Psychometricians don't do this presumably because they don't get better fitting model, for a variety of reasons. We are investigating if we can do better prediction with finer-grained skill models. Our result gave a confirmative answer to this question.

Subjects: 4. Cognitive Modeling

Submitted: May 16, 2006


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