The project advances cyber-enabled, teacher analytics as a new genre of technology that provides automated feedback on teacher performance with the goal of improving teaching effectiveness and student achievement. The innovation aims to help teachers develop expertise on multiple dimensions of classroom communication and will be developed and tested in 9th grade classrooms in Western Pennsylvania. The team will first generate initial insights on how teacher discourse predicts student achievement via a re-analysis of large volumes (128 hours) of existing classroom audio. Next, they will design and iteratively refine hardware/software interfaces for efficient, flexible, scalable audio data collection by teachers. The data will be used to computationally model dimensions of effective discourse by combining linguistic, discursive, acoustic, and contextual analysis of audio with supervised and semi-supervised deep recurrent neural networks. The model-based estimates will be incorporated into an interactive analytic/visualization platform to promote data-driven reflective practice. After refinement via design studies, the impact of the innovation on instructional improvement and student literacy outcomes will be evaluated in a randomized control trial.