A child's ability to understand text (reading comprehension) can greatly impact both their ability to learn in the classroom and their future contributions to society. Reading comprehension draws on oral language; behavioural measures of knowledge at the word and sentence levels have been shown to be related to children's reading comprehension. In this study, we examined the impact of word and sentence level text-features on children's reading comprehension. We built a predictive model that uses natural language processing techniques to predict the question-level performance of students on reading comprehension tests. We showed that, compared to a model that used measures of student knowledge and sub-skills alone, a model that used features of sentence complexity, lexical surprisal, rare word use, and general context improved prediction accuracy by more than four percentage points. Our subsequent analyses revealed that these features compensate for the shortcomings of each other and work together to produce maximal performance. This provides insight into how different characteristics of the text and questions can be used to predict student performance, leading to new ideas about how text and reading comprehension interact. Our work also suggests that using a combination of text features could support the adaptation of reading materials to meet student needs.