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Using knowledge tracing to measure student reading proficiencies
J.E. Beck and J. Sison
Proceedings of the 7th International Conference on Intelligent Tutoring Systems, September, 2004.

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Abstract

Abstract. Constructing a student model for language tutors is a challenging task. This paper describes using knowledge tracing to construct a student model of reading proficiency and validates the model. We use speech recognition to assess a student's reading proficiency at a subword level, even though the speech recognizer output is at the level of words. Specifically, we estimate the student's knowledge of 80 letter to sound mappings, such as ch making the sound /K/ in "chemistry." At a coarse level, the student model did a better job at estimating reading proficiency for 47.2% of the students than did a standardized test designed for the task. Our model's estimate of the student's knowledge on individual letter to sound mappings is a significant predictor of whether he will ask for help on a particular word. Thus, our student model is able to describe student performance both at a coarse- and at a fine-grain size.

Notes

Associated lab/group: Project LISTEN

Number of pages: 11

Text Reference

J.E. Beck and J. Sison, "Using knowledge tracing to measure student reading proficiencies," Proceedings of the 7th International Conference on Intelligent Tutoring Systems, September, 2004.

BibTeX Reference

@inproceedings{Beck_2004_4991,
   author = "Joseph E Beck and J. Sison",
   title = "Using knowledge tracing to measure student reading proficiencies",
   booktitle = "Proceedings of the 7th International Conference on Intelligent Tutoring Systems",
   month = "September",
   year = "2004"
}