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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. A Comparison of Responsive and General Guidance to Promote Learning in an Online Science Dialog

   https://doi.org/10.3390/educsci14121383  

   Gerard, Libby; Linn, Marcia C; Holtmann, Marlen (December 2024, Education Sciences)

   Students benefit from dialogs about their explanations of complex scientific phenomena, and middle school science teachers cannot realistically provide all the guidance they need. We study ways to extend generative teacher–student dialogs to more students by using AI tools. We compare Responsive web-based dialogs to General web-based dialogs by evaluating the ideas students add and the quality of their revised explanations. We designed the General guidance to motivate and encourage students to revise their explanations, similar to how an experienced classroom teacher might instruct the class. We designed the Responsive guidance to emulate a student–teacher dialog, based on studies of experienced teachers guiding individual students. The analyses comparing the Responsive and the General condition are based on a randomized assignment of a total sample of 507 pre-college students. These students were taught by five different teachers in four schools. A significantly higher proportion of students added new accurate ideas in the Responsive condition compared to the General condition during the dialog. This research shows that by using NLP to identify ideas and assign guidance, students can broaden and refine their ideas. Responsive guidance, inspired by how experienced teachers guide individual students, is more valuable than General guidance. 

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3. Understanding of a law of science and its relation to science writing with automated feedback

   Kim, ChanMin; Puntambekar, Sadhana; Lee, Eunseo; Gnesdilow, Dana; Dey, Indrani; Cang, Xuesong; Wu, Siyu; & Passonneau, Rebecca (July 2023, Proceedings of the International Conference of Computer-Supported Collaborative Learning)

   Building causal knowledge is critical to science learning and scientific explanations that require one to understand the how and why of a phenomenon. In the present study, we focused on writing about the how and why of a phenomenon. We used natural language processing (NLP) to provide automated feedback on middle school students’ writing about an underlying principle (the law of conservation of energy) and its related concepts. We report the role of understanding the underlying principle in writing based on NLP-generated feedback. 

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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 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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