Exploring the Application of Visual Question Answering (VQA) for Classroom Activity Monitoring
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915474236768256 |
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| author | Vu, Sinh Trong Pham, Hieu Trung Nguyen, Dung Manh Hoang, Hieu Minh Le, Nhu Hoang Pham, Thu Ha Mai, Tai Tan |
| author_facet | Vu, Sinh Trong Pham, Hieu Trung Nguyen, Dung Manh Hoang, Hieu Minh Le, Nhu Hoang Pham, Thu Ha Mai, Tai Tan |
| contents | Classroom behavior monitoring is a critical aspect of educational research, with significant implications for student engagement and learning outcomes. Recent advancements in Visual Question Answering (VQA) models offer promising tools for automatically analyzing complex classroom interactions from video recordings. In this paper, we investigate the applicability of several state-of-the-art open-source VQA models, including LLaMA2, LLaMA3, QWEN3, and NVILA, in the context of classroom behavior analysis. To facilitate rigorous evaluation, we introduce our BAV-Classroom-VQA dataset derived from real-world classroom video recordings at the Banking Academy of Vietnam. We present the methodology for data collection, annotation, and benchmark the performance of the selected VQA models on this dataset. Our initial experimental results demonstrate that all four models achieve promising performance levels in answering behavior-related visual questions, showcasing their potential in future classroom analytics and intervention systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Application of Visual Question Answering (VQA) for Classroom Activity Monitoring Vu, Sinh Trong Pham, Hieu Trung Nguyen, Dung Manh Hoang, Hieu Minh Le, Nhu Hoang Pham, Thu Ha Mai, Tai Tan Computer Vision and Pattern Recognition Artificial Intelligence Classroom behavior monitoring is a critical aspect of educational research, with significant implications for student engagement and learning outcomes. Recent advancements in Visual Question Answering (VQA) models offer promising tools for automatically analyzing complex classroom interactions from video recordings. In this paper, we investigate the applicability of several state-of-the-art open-source VQA models, including LLaMA2, LLaMA3, QWEN3, and NVILA, in the context of classroom behavior analysis. To facilitate rigorous evaluation, we introduce our BAV-Classroom-VQA dataset derived from real-world classroom video recordings at the Banking Academy of Vietnam. We present the methodology for data collection, annotation, and benchmark the performance of the selected VQA models on this dataset. Our initial experimental results demonstrate that all four models achieve promising performance levels in answering behavior-related visual questions, showcasing their potential in future classroom analytics and intervention systems. |
| title | Exploring the Application of Visual Question Answering (VQA) for Classroom Activity Monitoring |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.22369 |