Using natural language processing to support student-centered education
Abstract/Contents
- Abstract
- Language is central to education, being the core medium of instruction. Researchers and practitioners have long used manual methods to analyze instructional language like classroom discourse and instructional texts, with the goal of facilitating student-centered instruction. In this dissertation, I offer three studies demonstrating how natural language processing (NLP) can expand the possibilities for student-centered educational research and practice. First, I illustrate how computational methods can help analyze textbook content. Applying lexicons, word embeddings and topic models, I study the depiction of historically marginalized groups in U.S. history textbooks. I find that Latinx individuals are rarely discussed and African Americans are described with low agency and power. The quantification of these inequities can aid researchers, teachers, publishers and other stakeholders make informed decisions when developing and choosing textbooks. Second, I demonstrate how NLP can identify teachers' student-centered talk moves in classroom discourse. I develop an unsupervised machine learning measure for teachers' uptake of student contributions, a high-leverage teaching practice that supports dialogic instruction and makes students feel heard. I extensively validate this measure by analyzing the linguistic phenomena it captures, such as repetition and elaboration, and by demonstrating its correlation with positive educational outcomes across three datasets of student-teacher interaction. Third, I demonstrate how computational research on classroom discourse can be translated into improving educational practice. Building on my uptake measure, I develop a web application that provides teachers with consistent, individualized feedback on their uptake of student contributions. In a large-scale randomized controlled trial in an online computer science course, Code in Place (n=1,136 instructors), I find that the tool improves instructors' uptake of student contributions by 27% and I present suggestive evidence that it also improves students' satisfaction with the course and assignment completion. Together, these studies demonstrate the possibilities for natural language processing methods to measure and to improve key aspects of student-centered instruction.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Demszky, Dorottya |
|---|---|
| Degree supervisor | Jurafsky, Dan, 1962- |
| Thesis advisor | Jurafsky, Dan, 1962- |
| Thesis advisor | Hill, Heather C |
| Thesis advisor | Piech, Chris (Christopher) |
| Degree committee member | Hill, Heather C |
| Degree committee member | Piech, Chris (Christopher) |
| Associated with | Stanford University, Department of Linguistics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Dorottya Demszky. |
|---|---|
| Note | Submitted to the Department of Linguistics. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/wv809fw1100 |
Access conditions
- Copyright
- © 2022 by Dorottya Demszky
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...