  Classroom discourse is a core medium of instruction - analyzing it can
provide a window into teaching and learning as well as driving the development
of new tools for improving instruction. We introduce the largest dataset of
mathematics classroom transcripts available to researchers, and demonstrate how
this data can help improve instruction. The dataset consists of 1,660 45-60
minute long 4th and 5th grade elementary mathematics observations collected by
the National Center for Teacher Effectiveness (NCTE) between 2010-2013. The
anonymized transcripts represent data from 317 teachers across 4 school
districts that serve largely historically marginalized students. The
transcripts come with rich metadata, including turn-level annotations for
dialogic discourse moves, classroom observation scores, demographic
information, survey responses and student test scores. We demonstrate that our
natural language processing model, trained on our turn-level annotations, can
learn to identify dialogic discourse moves and these moves are correlated with
better classroom observation scores and learning outcomes. This dataset opens
up several possibilities for researchers, educators and policymakers to learn
about and improve K-12 instruction. The dataset can be found at
https://github.com/ddemszky/classroom-transcript-analysis.
