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IRT Modeling of Tutor Performance to Predict End-of-Year Exam Scores

^* To whom correspondence should be addressed. E-mail: eayers{at}stat.cmu.edu.

	Abstract

Interest in end-of-year accountability exams has increased dramatically since the passing of the No Child Left Behind Act in 2001. With this increased interest comes a desire to use student data collected throughout the year to estimate student proficiency and predict how well they will perform on end-of-year exams. This article uses student performance on the Assistment System, an online mathematics tutor, to show that replacing percentage correct with an Item Response Theory estimate of student proficiency leads to better fitting prediction models. In addition, it uses other tutor performance metrics to further increase prediction accuracy. Prediction error bounds are also calculated to attain an absolute measure to which the models can be compared.

First published on May 23, 2008, doi:10.1177/0013164408318758

Educational and Psychological Measurement 2008;68:972.

A more recent version of this article appeared on December 1, 2008


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