@EdWorkingPaper{ai21-483,
title = "Can Automated Feedback Improve TeachersÕ Uptake of Student Ideas? Evidence From a Randomized Controlled Trial In a Large-Scale Online Course",
author = "Dorottya Demszky, Jing Liu, Heather C. Hill, Dan Jurafsky, Chris Piech",
institution = "Annenberg Institute at Brown University",
number = "483",
year = "2021",
month = "November",
URL = "http://www.edworkingpapers.com/ai21-483",
abstract = {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 instructorsÕ uptake of student contributions by 24% and present suggestive evidence that our tool also improves studentsÕ satisfaction with the course. These results demonstrate the promise of our tool to complement existing efforts in teachersÕ professional development.},
}