Using Natural Language Processing to Support Student-Centered Education

Autor/in	Demszky, Dorottya
Titel	Using Natural Language Processing to Support Student-Centered Education
Quelle	(2022), (161 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Stanford University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	979-8-3719-4595-2
Schlagwörter	Hochschulschrift; Dissertation; Natural Language Processing; Student Centered Learning; Textbook Content; Evaluation Methods; Textbooks; United States History; Hispanic Americans; African Americans; Textbook Selection; Discussion (Teaching Technique); Artificial Intelligence; Educational Improvement; Feedback (Response); Computer Science; Student Satisfaction; Teacher Student Relationship + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Natürliche Sprache; Group work; Student-entered learning; Student-centred learning; Student centred learning; Schülerorientierter Unterricht; Schülerzentrierter Unterricht; Gruppenarbeit; Lehrbuchtext; Textbook; Text book; Schulbuch; Lehrbuch; Hispanic; Hispanoamerikaner; Afroamerikaner; Künstliche Intelligenz; Teaching improvement; Unterrichtsentwicklung; Informatik; Teacher student relationships; Lehrer-Schüler-Beziehung
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. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01