SLBDetection-Net: Towards closed-set and open-set student learning behavior detection in smart classroom of K-12 education

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Through effective analysis of K-12 education students’ learning behaviors in the classroom, it is possible to greatly improve the interaction between teaching and learning, thereby enhancing the quality of education. However, current traditional analysis of student classroom behavior focuses on closed-set behavior detection in a single scenario. For complex and open real classroom environments, the challenge lies in obtaining meaningful representations of behaviors in small and densely populated complex scenes, while also achieving good performance in both closed-set and open-set environments. To address these challenges, this study introduces a novel method for detecting student learning behavior in both closed-set and open-set scenarios, termed SLBDetection-Net. This method focuses on accurately capturing learning behavior representation, with a specific emphasis on Multi-scale Focusing Key Information (MFKI). The study begins with designing a Learning Behavior-aware Attention (LBA) mechanism, dedicated to extracting key features of learning behaviors and capturing the complex characteristics of targets across different scales. Building on this attention mechanism, a backbone network feature encoder, LBA-Swin Transformer Block, is constructed to form the comprehensive SLBDetection-Net. The effectiveness of SLBDetection-Net is validated through rigorous testing and evaluation on real classroom scenario data of K-12 education, comparing its performance with the State-of-the-Art (SOTA) methods. The results demonstrate that SLBDetection-Net achieves a mean Average Precision (mAP) of 96.4% on the ClaBehavior dataset and 85.9% on the SCB dataset. These findings underscore the method’s significant advantages in enhancing detection precision and efficiency in both closed-set and open-set scenarios, thereby expanding the application scope of educational assessment frameworks. The source code of this study is publicly available at https://github.com/CCNUZFW/SLBDetection-Net.