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Combining Natural Language Processing with Epistemic Network Analysis to Investigate Student Knowledge Integration within an AI Dialog.

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Title: Combining Natural Language Processing with Epistemic Network Analysis to Investigate Student Knowledge Integration within an AI Dialog.
Authors: Li, Weiying1 (AUTHOR) weiyingli@berkeley.edu, Chang, Hsin-Yi2 (AUTHOR) hychang@ntnu.edu.tw, Bradford, Allison1 (AUTHOR) allison_bradford@berkeley.edu, Gerard, Libby1 (AUTHOR) libbygerard@berkeley.edu, Linn, Marcia C.1 (AUTHOR) mclinn@berkeley.edu
Source: Journal of Science Education & Technology. Oct2025, Vol. 34 Issue 5, p980-993. 14p.
Subject Terms: *STEM education, *Cognitive structures, *Educational technology, *Cognitive development, Natural language processing, Photosynthesis, Energy transfer, Cell respiration
Abstract: In this study, we used Epistemic Network Analysis (ENA) to represent data generated by Natural Language Processing (NLP) analytics during an activity based on the Knowledge Integration (KI) framework. The activity features a web-based adaptive dialog about energy transfer in photosynthesis and cellular respiration. Students write an initial explanation, respond to two adaptive prompts in the dialog, and write a revised explanation. The NLP models score the KI level of the initial and revised explanations. They also detect the ideas in the explanations and the dialog responses. The dialog uses the detected ideas to prompt students to elaborate and refine their explanations. Participants were 196 8th-grade students at a public school in the Western United States. We used ENA to represent the idea networks at each KI score level for the revised explanations. We also used ENA to analyze the idea trajectories for the initial explanation, the two dialog responses, and the final explanation. Higher KI levels were associated with more links and increased frequency of mechanistic ideas in ENA representations. Representation of the trajectories suggests that the NLP adaptive dialog helped students who started with descriptive and macroscopic ideas to add more microscopic ideas. The dialog also helped students who started with partially linked ideas to keep linking the microscopic ideas to mechanistic ideas. We discuss implications for STEM teachers and researchers who are interested in how students build on their ideas to integrate their ideas. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Science Education & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Combining Natural Language Processing with Epistemic Network Analysis to Investigate Student Knowledge Integration within an AI Dialog.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Weiying%22">Li, Weiying</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> weiyingli@berkeley.edu</i><br /><searchLink fieldCode="AR" term="%22Chang%2C+Hsin-Yi%22">Chang, Hsin-Yi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hychang@ntnu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Bradford%2C+Allison%22">Bradford, Allison</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> allison_bradford@berkeley.edu</i><br /><searchLink fieldCode="AR" term="%22Gerard%2C+Libby%22">Gerard, Libby</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> libbygerard@berkeley.edu</i><br /><searchLink fieldCode="AR" term="%22Linn%2C+Marcia+C%2E%22">Linn, Marcia C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mclinn@berkeley.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Science+Education+%26+Technology%22">Journal of Science Education & Technology</searchLink>. Oct2025, Vol. 34 Issue 5, p980-993. 14p.
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  Data: *<searchLink fieldCode="DE" term="%22STEM+education%22">STEM education</searchLink><br />*<searchLink fieldCode="DE" term="%22Cognitive+structures%22">Cognitive structures</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Cognitive+development%22">Cognitive development</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Photosynthesis%22">Photosynthesis</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+transfer%22">Energy transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Cell+respiration%22">Cell respiration</searchLink>
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  Data: In this study, we used Epistemic Network Analysis (ENA) to represent data generated by Natural Language Processing (NLP) analytics during an activity based on the Knowledge Integration (KI) framework. The activity features a web-based adaptive dialog about energy transfer in photosynthesis and cellular respiration. Students write an initial explanation, respond to two adaptive prompts in the dialog, and write a revised explanation. The NLP models score the KI level of the initial and revised explanations. They also detect the ideas in the explanations and the dialog responses. The dialog uses the detected ideas to prompt students to elaborate and refine their explanations. Participants were 196 8th-grade students at a public school in the Western United States. We used ENA to represent the idea networks at each KI score level for the revised explanations. We also used ENA to analyze the idea trajectories for the initial explanation, the two dialog responses, and the final explanation. Higher KI levels were associated with more links and increased frequency of mechanistic ideas in ENA representations. Representation of the trajectories suggests that the NLP adaptive dialog helped students who started with descriptive and macroscopic ideas to add more microscopic ideas. The dialog also helped students who started with partially linked ideas to keep linking the microscopic ideas to mechanistic ideas. We discuss implications for STEM teachers and researchers who are interested in how students build on their ideas to integrate their ideas. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Science Education & Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Oct2025
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