Vocabulary knowledge is crucial to literacy development and academic success. Previous research has shown learning the meaning of a word requires encountering it in diverse informative contexts. In this work, we try to identify "nutritious" contexts for a word--contexts that help students build a rich mental representation of the word's meaning. Using crowdsourced ratings of vocabulary contexts retrieved from the web, AVER learns models to score unseen contexts for unseen words. We specify the features used in the models, measure their individual informativeness, evaluate AVER's cross-validated accuracy in scoring contexts for unseen words, and compare its agreement with the human ratings against the humans' agreement with each other. The automated scores are not good enough to replace human ratings, but should reduce human effort by identifying contexts likely to be worth rating by hand, subject to a tradeoff between the number of contexts inspected by hand, and how many of them a human judge will consider nutritious. [For complete proceedings, see ED560503.]