Affiliations: Machine Learning Department, School of Computer
Science, Carnegie Mellon University, Pittsburgh, PA 15213. USA.
E-mail: [email protected] | Language Technologies Institute, School of Computer
Science, Carnegie Mellon University, Pittsburgh, PA 15213. USA
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 and is statistically noisy.
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. Although
not quite as strong as the standardized test, our assessment method can provide
a report on the student at any time during the year and requires no break from
reading to administer. 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.