PERC 2024 Abstract Detail Page
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| Abstract Title: | Assessing Student Scientific Argumentation Using Natural Language Processing |
|---|---|
| Abstract Type: | Contributed Poster Presentation |
| Abstract: | Scientific argumentation is an important science and engineering practice and a necessary 21st Century workforce skill. Due to the nature of large enrollment classes, it is difficult to individually assess students and provide feedback on their argumentation. The recent developments in Natural Language Processing (NLP) and Machine Learning (ML) may provide a solution. In this study we investigate methods using NLP and ML to assess and understand students argumentation. Specifically, we investigate the use of BERT (Bidirectional Encoder Representations from Transformers) versus a fine-tuned BERT to analyze student essays of argumentation after solving a problem in the recitation section of an introductory calculus-based physics course. We report on the performance of this model performed versus our fine-tuned model. |
| Session Time: | Poster Session 2 |
| Poster Number: | B87 |
Author/Organizer Information | |
| Primary Contact: |
Winter Rose Allen Department of Physics and Astronomy, Purdue University West Lafayettte, IN 47906 Phone: 8705043063 |
| Co-Author(s) and Co-Presenter(s) |
Carina M. Rebello, Department of Physics, Toronto Metropolitan University N. Sanjay Rebello, Department of Physics and Astronomy, Purdue University and Department of Curriculum and Instruction, Purdue University |
