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1. Designing an Adaptive Dialogue to Promote Science Understanding

   Gerard, L. ; Bichler, S. ; Bradford, A. ; Linn, M. C. ; Steimel, K. ; Riordan, B. ( January 2022 , Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022)

   Chinn, C. ; Tan, E. ; Chan, C. ; Kali, Y. (Ed.)

   We used Natural Language Processing (NLP) to design an adaptive computer dialogue that engages students in a conversation to reflect on and revise their written explanations of a science dilemma. We study the accuracy of the NLP idea detection. We analyze how 98 12-13 year-olds interacted with the dialogue as a part of a Diagnostic Inventory. We study students’ initial and revised science explanations along with their logged responses to the dialogue. The dialogue led to a high rate of student revision compared to prior studies of adaptive guidance. The adaptive prompt encouraged students to reflect on prior experiences, to consider new variables, and to raise scientific questions. Students incorporated these new ideas when revising their initial explanations. We discuss how these adaptive dialogues can strengthen science instruction. 

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2. Explaining Thermodynamics: Impact of an Adaptive Dialog Based on a Natural Language Processing Idea Detection Model

   https://doi.org/10.22318/icls2023.199424  

   Li, Weiying ; Gerard, Libby ; Lim-Breitbart, Jonathan ; Bradford, Allison ; Linn, Marcia C ; Riordan, Brian ; Steimel, Kenneth ( October 2023 , International Society of the Learning Sciences)

   Blikstein, P ; Van_Aalst, J ; Kizito, R ; Brennan, K (Ed.)

   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. 

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3. How do middle school students think about climate change?

   Bichler, S. ; Bradford, A. ; Riordan, B. ; Linn, M. C. ( January 2022 , Proceedings of the 16th International Conference of the Learning Sciences—ICLS 2022)

   Chinn, C ; Tan, E ; Chan, C ; Kali, Y. (Ed.)

   We use natural language processing (NLP) to train an automated scoring model to assess students’ reasoning on how to slow climate change. We use the insights from scoring over 1000 explanations to design a knowledge integration intervention and test it in three classrooms. The intervention supported students to distinguish relevant evidence, improving connections between ideas in a revised explanation. We discuss next steps for using the NLP model to support teachers and students in classrooms. 

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4. Improving Teacher Noticing of Students’ Science Ideas with a Dashboard

   Billings, Kelly ; Gerard, Libby ; Linn, Marcia C. ( January 2021 , Computersupported collaborative learning)

   de Vries, E. ; Hod, Y. ; Ahn, J. (Ed.)

   We explore how a Teacher Action Planner (TAP) that synthesizes student ideas impacts teacher noticing. The TAP uses Natural Language Processing (NLP) to detect student ideas in written explanations. We compared teacher noticing while using the TAP to noticing when reviewing student explanations. The TAP helped teachers deepen their analysis of student ideas. We did not see any impact on immediate instructional practice. We propose redesigns to the TAP to better connect noticing to instruction. 

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5. How Can Computational Modeling Help Students Shift Their Ideas Towards Scientifically Accurate Explanations?

   Fuhrmann, T. ; Wagh, A. ; Rosenbaum, L. ; Eloy, A. ; Wilkerson, M. ; Blikstein, P. ( January 2023 , Proceedings of the 2023 Annual Meeting of the International Society for the Learning Sciences)

   This paper explores how MoDa, an integrated computational modeling and data environment, enabled students to express their ideas about diffusion and shift them toward canonical ideas. Drawing on data from an 8-day unit with two 6th-grade science classes, we analyze students' utterances in presentations, drawings, and written responses to document their diverse ideas about diffusion We present three case studies to illustrate how engaging with computational modeling in MoDa and the unit around it enabled students to shift from non-canonical ideas towards more canonical explanations of diffusion. In particular, we identify three factors that helped in shifting students’ ideas: the availability of code blocks to represent a diverse range of ideas including non-canonical ones, consistent access to video data of the phenomenon, and model presentations to the whole class. The paper illustrates how a computational modeling tool and curriculum can make students' diverse ideas visible and shift them toward canonical explanations. 

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