@EdWorkingPaper{ai23-759,
title = "M-Powering Teachers: Natural Language Processing Powered Feedback Improves 1:1 Instruction and Student Outcomes",
author = "Dorottya Demszky, Jing Liu",
institution = "Annenberg Institute at Brown University",
number = "759",
year = "2023",
month = "April",
URL = "http://www.edworkingpapers.com/ai23-759",
abstract = {Although learners are being connected 1:1 with instructors at an increasing scale, most of these instructors do not receive effective, consistent feedback to help them improved. We deployed M-Powering Teachers, an automated tool based on natural language processing to give instructors feedback on dialogic instructional practices —including their uptake of student contributions, talk time and questioning practices — in a 1:1 online learning context. We conducted a randomized controlled trial on Polygence, a re-search mentorship platform for high schoolers (n=414 mentors) to evaluate the effectiveness of the feedback tool. We find that the intervention improved mentors’ uptake of student contributions by 10%, reduced their talk time by 5% and improves student’s experi-ence with the program as well as their relative optimism about their academic future. These results corroborate existing evidence that scalable and low-cost automated feedback can improve instruction and learning in online educational contexts.},
}