Augmented Physics: Creating Interactive and Embedded Physics Simulations from Static Textbook Diagrams
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909283954720768 |
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| author | Gunturu, Aditya Wen, Yi Zhang, Nandi Thundathil, Jarin Kazi, Rubaiat Habib Suzuki, Ryo |
| author_facet | Gunturu, Aditya Wen, Yi Zhang, Nandi Thundathil, Jarin Kazi, Rubaiat Habib Suzuki, Ryo |
| contents | We introduce Augmented Physics, a machine learning-integrated authoring tool designed for creating embedded interactive physics simulations from static textbook diagrams. Leveraging recent advancements in computer vision, such as Segment Anything and Multi-modal LLMs, our web-based system enables users to semi-automatically extract diagrams from physics textbooks and generate interactive simulations based on the extracted content. These interactive diagrams are seamlessly integrated into scanned textbook pages, facilitating interactive and personalized learning experiences across various physics concepts, such as optics, circuits, and kinematics. Drawing from an elicitation study with seven physics instructors, we explore four key augmentation strategies: 1) augmented experiments, 2) animated diagrams, 3) bi-directional binding, and 4) parameter visualization. We evaluate our system through technical evaluation, a usability study (N=12), and expert interviews (N=12). Study findings suggest that our system can facilitate more engaging and personalized learning experiences in physics education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18614 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Augmented Physics: Creating Interactive and Embedded Physics Simulations from Static Textbook Diagrams Gunturu, Aditya Wen, Yi Zhang, Nandi Thundathil, Jarin Kazi, Rubaiat Habib Suzuki, Ryo Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning We introduce Augmented Physics, a machine learning-integrated authoring tool designed for creating embedded interactive physics simulations from static textbook diagrams. Leveraging recent advancements in computer vision, such as Segment Anything and Multi-modal LLMs, our web-based system enables users to semi-automatically extract diagrams from physics textbooks and generate interactive simulations based on the extracted content. These interactive diagrams are seamlessly integrated into scanned textbook pages, facilitating interactive and personalized learning experiences across various physics concepts, such as optics, circuits, and kinematics. Drawing from an elicitation study with seven physics instructors, we explore four key augmentation strategies: 1) augmented experiments, 2) animated diagrams, 3) bi-directional binding, and 4) parameter visualization. We evaluate our system through technical evaluation, a usability study (N=12), and expert interviews (N=12). Study findings suggest that our system can facilitate more engaging and personalized learning experiences in physics education. |
| title | Augmented Physics: Creating Interactive and Embedded Physics Simulations from Static Textbook Diagrams |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.18614 |