Assessing student proficiency in a Reading Tutor that listens

Joseph E. Beck, P. Jia, and Jack Mostow
Proceedings of the 9th International Conference on User Modeling, July, 2003, pp. 323 - 327.


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Abstract
This paper reports results on using data mining to extract useful variables from a database that contains interactions between the student and Project LISTEN's Reading Tutor. Our approach is to find variables we believe to be useful in the information logged by the tutor, and then to derive models that relate those variables to student's scores on external, paper-based tests of reading proficiency. Once the relationship between the recorded variables and the paper tests is discovered, it is possible to use information recorded by the tutor to assess the student's current level of proficiency. The major results of this work were the discovery of useful features available to the Reading Tutor that describe students, and a strong predictive model of external tests that correlates with actual test scores at 0.88.

Notes
Associated Lab(s) / Group(s): Project LISTEN
Associated Project(s): Project LISTEN\'s Reading Tutor
Number of pages: 5

Text Reference
Joseph E. Beck, P. Jia, and Jack Mostow, "Assessing student proficiency in a Reading Tutor that listens," Proceedings of the 9th International Conference on User Modeling, July, 2003, pp. 323 - 327.

BibTeX Reference
@inproceedings{Beck_2003_4658,
   author = "Joseph E Beck and P. Jia and Jack Mostow",
   title = "Assessing student proficiency in a Reading Tutor that listens",
   booktitle = "Proceedings of the 9th International Conference on User Modeling",
   pages = "323 - 327",
   month = "July",
   year = "2003",
}