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Title: Explaining Thermodynamics: Impact of an Adaptive Dialog Based on a Natural Language Processing Idea Detection Model
Authors: Li, Weiying
Gerard, Libby
Lim-Breitbart, Jonathan
Bradford, Allison
Linn, Marcia C.
Riordan, Brian
Steimel, Kenneth
Keywords: Learning Sciences
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Li, W., Gerard, L., Lim-Breitbart, J., Bradford, A., Linn, M. C., Riordan, B., & Steimel, K. (2023). Explaining thermodynamics: Impact of an adaptive dialog based on a natural language processing idea detection model. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 1306-1309). International Society of the Learning Sciences.
Abstract: We explored how Natural Language Processing (NLP) adaptive dialogs that are designed following Knowledge Integration (KI) pedagogy elicit rich student ideas about thermodynamics and contribute to productive revision. We analyzed how 619 6-8th graders interacted with two rounds of adaptive dialog on an end-of-year inventory. The adaptive dialog significantly improved students’ KI levels. Their revised explanations are more integrated across all grades, genders, and prior thermodynamics experiences. The dialog elicited many additional ideas, including normative ideas and vague reasoning. In the first round, students refined their explanation to focus on their normative ideas. In the second round they began to elaborate their reasoning and add new normative ideas. Students added more mechanistic ideas about conductivity, equilibrium, and the distinction between how an object feels and its temperature after the dialog. Thus, adaptive dialogs are a promising tool for scaffolding science sense-making.
Description: Short Paper
Appears in Collections:ISLS Annual Meeting 2023

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