Ranking-based readability assessment for early primary children’s literature

Yi Ma1,  Eric Fosler-Lussier1,  Robert Lofthus2
1The Ohio State University, 2Xerox Corporation


Abstract

Determining the reading level of children’s literature is an important task for providing educators and parents with an appropriate reading trajectory through a curriculum. Automating this process has been a challenge addressed before in the computational linguistics literature, with most studies attempting to predict the particular grade level of a text. However, guided reading levels developed by educators operate at a more fine-grained level, with multiple levels corresponding to each grade. We find that ranking performs much better than classification at the fine-grained leveling task, and that features derived from the visual layout of a book are just as predictive as standard text features of level; including both sets of features, we find that we can predict the reading level up to 83% of the time on a small corpus of children’s books.