Date
Publisher
arXiv
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.
Study at a glance
- Question: Can artificial intelligence models accurately answer questions about classroom behavior using video recordings?
- Setting and data: Classroom video recordings from the Banking Academy of Vietnam.
- Method: Tests how well four AI models answer behavior-related questions about classroom videos (benchmarking).
Why it matters
Classroom behavior monitoring is critical for educational research, with significant implications for student engagement and learning outcomes. Visual Question Answering models offer promising tools for automatically analyzing complex classroom interactions from video recordings.
Key findings
- At the Banking Academy of Vietnam, four visual AI models achieved promising performance answering questions about classroom behaviors shown in video recordings.
- The visual AI models showed potential to automatically evaluate complex classroom interactions from recorded video.
- The evaluated tools demonstrated promise for use in future classroom analytics and student intervention systems.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
