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Title: Explaining Thermodynamics: Impact of an Adaptive Dialog Based on a Natural Language Processing Idea Detection Model
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.  more » « less
Award ID(s):
2101669
NSF-PAR ID:
10510557
Author(s) / Creator(s):
; ; ; ; ; ;
Editor(s):
Blikstein, P; Van_Aalst, J; Kizito, R; Brennan, K
Publisher / Repository:
International Society of the Learning Sciences
Date Published:
Page Range / eLocation ID:
1306 to 1309
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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