M-powering teachers: natural language processing powered feedback improves 1:1 instruction and student outcomes

Conference paper

Dorottya Demszky, Jing Liu
ACM Conference on Learning, ACM, app, Copenhagen Denmark, 2023 Jul, pp. 59-69

DOI: 10.26300/s8xh-zp45

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APA Click to copy
Demszky, D., & Liu, J. (2023). M-powering teachers: natural language processing powered feedback improves 1:1 instruction and student outcomes. In ACM Conference on Learning (pp. 59–69). Copenhagen Denmark: ACM. https://doi.org/10.26300/s8xh-zp45

Chicago/Turabian Click to copy
Demszky, Dorottya, and Jing Liu. “M-Powering Teachers: Natural Language Processing Powered Feedback Improves 1:1 Instruction and Student Outcomes.” In ACM Conference on Learning, 59–69. Copenhagen Denmark: ACM, 2023.

MLA Click to copy
Demszky, Dorottya, and Jing Liu. “M-Powering Teachers: Natural Language Processing Powered Feedback Improves 1:1 Instruction and Student Outcomes.” ACM Conference on Learning, ACM, 2023, pp. 59–69, doi:10.26300/s8xh-zp45.

BibTeX Click to copy

@inproceedings{demszky2023a,
  title = {M-powering teachers: natural language processing powered feedback improves 1:1 instruction and student outcomes},
  year = {2023},
  month = jul,
  address = {Copenhagen Denmark},
  pages = {59-69},
  publisher = {ACM},
  school = {app},
  doi = {10.26300/s8xh-zp45},
  author = {Demszky, Dorottya and Liu, Jing},
  booktitle = {ACM Conference on Learning},
  howpublished = {},
  month_numeric = {7}
}

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 experience 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.

M-powering teachers: natural language processing powered feedback improves 1:1 instruction and student outcomes

Conference paper

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