A Generative Model for Scoring Children’s Reading Comprehension

The use of speech technology in children’s reading assessment can help teachers to diagnose reading difficulties and plan appropriate interventions for a large number of students. We present a Bayesian Network model of student reading comprehension that can be used to estimate automatic scores for a child’s spoken answers to open-ended questions about a text. Through the use of features derived from language models capturing different degrees of comprehension, we found that on the TBALL dataset we could achieve 0.8 correlation with reference comprehension scores derived from teachers, exceeding the teachers’ own correlation with this same reference. This student model also proved to perform without bias due to a speaker’s native language, which was not the case for a comparable baseline method, nor for the teachers themselves.