The Annenberg Institute at Brown University offers this national working paper series to provide open access to high-quality papers from multiple disciplines and from multiple universities and research organizations on a wide variety of topics related to education. EdWorkingPapers focuses particularly on research with strong implications for education policy. EdWorkingPapers circulates papers prior to publication for comment and discussion; these papers have not gone through a peer review processes. Contributors can update papers to provide readers with the most up-to-date findings.
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Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource intensive in most educational contexts. We develop an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage teaching practice that supports dialogic instruction and makes students feel heard. We conduct a randomized controlled trial as part of an online computer science course, Code in Place (n=1,136 instructors), to evaluate the effectiveness of the feedback tool. We find that the tool improves instructors’ uptake of student contributions by 24% and present suggestive evidence that our tool also improves students’ satisfaction with the course. These results demonstrate the promise of our tool to complement existing efforts in teachers’ professional development.