Using Knowledge Tracing in a Noisy Environment to Measure Student Reading Proficiencies

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 coarseand at a fine-grain size.