Using Natural Language Processing to Support Equitable and Student-Centered Education

Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource-intensive in most educational contexts. I demonstrate ways in which natural language processing (NLP) can be used to address this gap and provide teachers with feedback in a scalable and effective way. As part of a case study, I introduce an automated tool based on NLP that provides teachers with feedback on their uptake of student contributions, a high-leverage teaching practice that supports dialogic instruction and makes students feel heard. This tool is based on our fully automated measure of uptake that we validate extensively by analyzing the linguistic phenomena it captures, such as questioning and elaboration, and by demonstrating its correlation with positive educational outcomes across three datasets of student-teacher interaction. We evaluate the effectiveness of our tool to improve teachers' uptake of student contributions by conducting a randomized controlled trial in an online computer science course, Code in Place (n=1,136 instructors). 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.

Bio: Dora is a PhD candidate in Linguistics at Stanford, advised by Dan Jurafsky. She works on developing natural language processing methods to support equitable and student-centered education. Her recent publications focus on analyzing the representation of historically marginalized groups in US history textbooks and on measuring and giving feedback to teachers on their uptake of student contributions in classrooms. Prior to her PhD, Dora received a BA summa cum laude from Princeton University in Linguistics with a minor in Computer Science.