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Can an Intelligent Tutoring System Predict Math Proficiency as Well as a Standardized Test

By Mingyu Feng, Joseph Beck, Neil Heffernan and Kenneth Koedinger

Abstract

Abstract. It has been reported in previous work that students ’ online tutoring data collected from intelligent tutoring systems can be used to build models to predict actual state test scores. In this paper, we replicated a previous study to model students ’ math proficiency by taking into consideration students’ response data during the tutoring session and their help-seeking behavior. To extend our previous work, we propose a new method of using students test scores from multiple years (referred to as cross-year data) for determining whether a student model is as good as the standardized test to which it is compared at estimating student math proficiency. We show that our model can do as well as a standardized test. We show that what we assess has prediction ability two years later. We stress that the contribution of the paper is the methodology of using student cross-year state test score to evaluate a student model against a standardized test.

Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.352.9813
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