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1. How Does an Adaptive Dialog Based on Natural Language Processing Impact Students From Distinct Language Backgrounds?

   https://doi.org/10.22318/icls2023.921177  

   Holtmann, Marlen ; Gerard, Libby ; Li, Weiying ; Linn, Marcia C ; Riordan, Brian ; Steimel, Ken ( October 2023 , International Society of the Learning Sciences)

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

   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. 

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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. 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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4. 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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5. The Lifespan and Impact of Students’ Ideas Shared During Classroom Science Inquiry

   Matuk, C. ( January 2019 , Computersupported collaborative learning)

   Sharing ideas can strengthen students’ science explanations. Yet, how to guide uses of peers’ ideas, and what the impacts of those ideas are on students’ learning, are open questions. We implemented a web-based cell biology unit with 116 grade 7 students, and explored how peers’ ideas are used during explanation building, and how prompts to draw on peers to either diversify or reinforce existing ideas impacted the quality of students’ written explanations. Among other findings, exchanging ideas with peers led to all students improving their explanation quality upon revision; and students prompted to diversify their ideas showed greater learning gains by the end of the unit, while students prompted to reinforce ideas, who used more peer-generated ideas in preparation to write their explanations, produced higher quality explanations. This study builds our understanding of the influence of peer ideas on learning, and offers insight into supporting students in engaging effectively with peers’ ideas. 

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