With the rise of community-generated web content, the need for automatic characterization of resource quality has grown, particularly in the realm of educational digital libraries. We demonstrate how identifying concrete factors of quality for web-based educational resources can make machine learning approaches to automating quality characterization tractable. Using data from several previous studies of quality, we gathered a set of key dimensions and indicators of quality that were commonly identified by educators. We then performed a mixed-method study of digital library curation experts, showing that our characterization of quality captured the subjective processes used by the experts when assessing resource quality for classroom use. Using key indicators of quality selected from a statistical analysis of our expert study data, we developed a set of annotation guidelines and annotated a corpus of 1000 digital resources for the presence or absence of these key quality indicators. Agreement among annotators was high, and initial machine learning models trained from this corpus were able to identify some indicators of quality with as much as an 18% improvement over the baseline.