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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.06203 |
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| _version_ | 1866910441825894400 |
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| author | Fonteles, Joyce Davalos, Eduardo S., Ashwin T. Zhang, Yike Zhou, Mengxi Ayalon, Efrat Lane, Alicia Steinberg, Selena Anton, Gabriella Danish, Joshua Enyedy, Noel Biswas, Gautam |
| author_facet | Fonteles, Joyce Davalos, Eduardo S., Ashwin T. Zhang, Yike Zhou, Mengxi Ayalon, Efrat Lane, Alicia Steinberg, Selena Anton, Gabriella Danish, Joshua Enyedy, Noel Biswas, Gautam |
| contents | Investigating children's embodied learning in mixed-reality environments, where they collaboratively simulate scientific processes, requires analyzing complex multimodal data to interpret their learning and coordination behaviors. Learning scientists have developed Interaction Analysis (IA) methodologies for analyzing such data, but this requires researchers to watch hours of videos to extract and interpret students' learning patterns. Our study aims to simplify researchers' tasks, using Machine Learning and Multimodal Learning Analytics to support the IA processes. Our study combines machine learning algorithms and multimodal analyses to support and streamline researcher efforts in developing a comprehensive understanding of students' scientific engagement through their movements, gaze, and affective responses in a simulated scenario. To facilitate an effective researcher-AI partnership, we present an initial case study to determine the feasibility of visually representing students' states, actions, gaze, affect, and movement on a timeline. Our case study focuses on a specific science scenario where students learn about photosynthesis. The timeline allows us to investigate the alignment of critical learning moments identified by multimodal and interaction analysis, and uncover insights into students' temporal learning progressions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments Fonteles, Joyce Davalos, Eduardo S., Ashwin T. Zhang, Yike Zhou, Mengxi Ayalon, Efrat Lane, Alicia Steinberg, Selena Anton, Gabriella Danish, Joshua Enyedy, Noel Biswas, Gautam Artificial Intelligence Investigating children's embodied learning in mixed-reality environments, where they collaboratively simulate scientific processes, requires analyzing complex multimodal data to interpret their learning and coordination behaviors. Learning scientists have developed Interaction Analysis (IA) methodologies for analyzing such data, but this requires researchers to watch hours of videos to extract and interpret students' learning patterns. Our study aims to simplify researchers' tasks, using Machine Learning and Multimodal Learning Analytics to support the IA processes. Our study combines machine learning algorithms and multimodal analyses to support and streamline researcher efforts in developing a comprehensive understanding of students' scientific engagement through their movements, gaze, and affective responses in a simulated scenario. To facilitate an effective researcher-AI partnership, we present an initial case study to determine the feasibility of visually representing students' states, actions, gaze, affect, and movement on a timeline. Our case study focuses on a specific science scenario where students learn about photosynthesis. The timeline allows us to investigate the alignment of critical learning moments identified by multimodal and interaction analysis, and uncover insights into students' temporal learning progressions. |
| title | A First Step in Using Machine Learning Methods to Enhance Interaction Analysis for Embodied Learning Environments |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2405.06203 |