Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/9918
Title: How Does an Adaptive Dialog Based on Natural Language Processing Impact Students From Distinct Language Backgrounds?
Authors: Holtmann, Marlen
Gerard, Libby
Li, Weiying
Linn, Marcia C.
Riordan, Brian
Steimel, Ken
Keywords: Learning Sciences
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Holtmann, M., Gerard, L., Li, W., Linn, M. C., Riordan, B., & Steimel, K. (2023). How does an adaptive dialog based on natural language processing impact students from distinct language backgrounds?. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 1350-1353). International Society of the Learning Sciences.
Abstract: This study takes advantage of advances in Natural Language Processing (NLP) to build an idea detection model that can identify ideas grounded in students’ linguistic experiences. We designed adaptive, interactive dialogs for four explanation items using the NLP idea detection model and investigated whether they similarly support students from distinct language backgrounds. The curriculum, assessments, and scoring rubrics were informed by the Knowledge Integration (KI) pedagogy. We analyzed responses of 1,036 students of different language backgrounds taught by 10 teachers in five schools in the western United States. The adaptive dialog engages students from both monolingual English and multilingual backgrounds in incorporating additional relevant ideas into their explanations, resulting in a significant improvement in student responses from initial to revised explanations. The guidance supports students in both language groups to progress in integrating their scientific ideas.
Description: Short Paper
URI: https://doi.org/10.22318/icls2023.921177
https://repository.isls.org//handle/1/9918
Appears in Collections:ISLS Annual Meeting 2023

Files in This Item:
File SizeFormat 
ICLS2023_1350-1353.pdf237.5 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.