Valid and reliable measurements of teaching quality facilitate school-level decision-making and policies pertaining to teachers, but conventional classroom observations are costly, prone to rater bias, and hard to implement at scale. Using nearly 1,000 word-to-word transcriptions of 4th- and 5th-grade English language arts classes, we apply novel text-as-data methods to develop automated, objective measures of teaching to complement classroom observations. This approach is free of rater bias and enables the detection of three instructional factors that are well aligned with commonly used observation protocols: classroom management, interactive instruction, and teacher-centered instruction. The teacher-centered instruction factor is a consistent negative predictor of value-added scores, even after controlling for teachers' average classroom observation scores. The interactive instruction factor predicts positive value-added scores. [This study was funded by the Shultz Graduate Student Fellowship in Economic Policy from the Stanford Institute for Economic Policy Research, the Stanford Graduate School of Education Doctoral Student Award from the Technology for Equity in Learning Opportunities initiative, and a dissertation grant from the Stanford Freeman Spogli Institute.]