The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Much of the power of a computer tutor comes from its ability to assess students. In some domains, including oral reading, assessing the proficiency of a student is a challenging task for a computer. Our approach for assessing student reading proficiency is to use data that a computer tutor collects through its interactions with a student to estimate his performance on a human-administered test of oral reading fluency. A model with data collected from the tutor's speech recognizer output correlated, within-grade, at 0.78 on average with student performance on the fluency test. For assessing students, data from the speech recognizer were more useful than student help-seeking behavior. However, adding help-seeking behavior increased the average within-grade correlation to 0.83. These results show that speech recognition is a powerful source of data about student performance, particularly for reading.

Notes

Associated Lab(s) / Group(s): Project LISTEN

Number of pages: 21

Text Reference

Joseph E. Beck, P. Jia, and Jack Mostow, "Automatically assessing oral reading fluency in a computer tutor that listens," Technology, Instruction, Cognition and Learning, Vol. 1, 2004, pp. 61 - 81.

BibTeX Reference

@article{Beck_2004_4990,
   author = "Joseph E Beck and P. Jia and Jack Mostow",
   title = "Automatically assessing oral reading fluency in a computer tutor that listens",
   journal = "Technology, Instruction, Cognition and Learning",
   pages = "61 - 81",
   year = "2004",
   volume = "1",
}

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