System Analysis of the Learning Behavior Recognition System for Students in a Law Classroom: Based on the Improved SSD Behavior Recognition Algorithm

Abstract

Classroom teaching activities have always been the focus of research in the field of pedagogy. The main body of classroom teaching activities is students, and students’ classroom behavior status can reflect classroom efficiency to a certain extent, making it an important reference index for classroom quality assessment. With the rapid development of artificial intelligence, school education is gradually becoming more intelligent. At present, most of the classrooms are equipped with video equipment. These videos record the real behavior status of the students in the classroom. For example, by analyzing the data, combining artificial intelligence, deep learning, and other related technologies with education to develop behavioral intelligence, the analysis system has a certain positive effect on helping the reform of classroom education. This study proposes an improved SSD behavior recognition model. The network model is optimized and the model convergence speed is accelerated based on the RMSProp optimization algorithm Through a database of 2,500 images of five behaviors, including raising hands, sitting up, writing, sleeping, and playing with mobile phones, and using them as object detection datasets, we use the OpenCV library to extract frames from classroom screen recording videos as image data sources for student behavior recognition and face recognition. Finally, an improved method is proposed to change the virtual network to MobileNet and complete the fusion function. The results show that compared with the traditional SSD method, the improved model has a significantly improved effect in recognizing small objects and the recognition speed is not significantly reduced.

Multi-view and multi-scale behavior recognition algorithm based on attention mechanism

“…Guo ( 2022 ) used a database containing 2,500 images of five behaviors (e.g., raising hands, sitting up, writing, sleeping, and mobile phone usage) for object detection; they extracted frames from classroom screen recording videos using the OpenCV library, then transformed the virtual network into MobileNet to complete the fusion function. Compared with the traditional single-shot detector (SSD) method, their model more accurately recognized small objects with no significant decrease in recognition speed.…”

“…Early on, probabilistic and statistical methods (Yamato et al, 1992 ; Natarajan et al, 2010 ; Shi et al, 2011 ) were often utilized for behavior identification. Many innovative methods have since surfaced with the advent of deep learning, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) (Xu et al, 2014 ; Muhammad et al, 2021 ; Shi et al, 2023a , b ; Tian et al, 2023 ; Zhaowei et al, 2023 ), which have been extended to teaching behavior analysis tasks (Li et al, 2021 ; Lin et al, 2021 ; Xie et al, 2021 ; Zhao et al, 2021 ; Gu and Li, 2022 ; Guo, 2022 ).…”

Student Online Learning Behavior Characteristics Based on Multidimensional Cognitive Model

“…The analysis of students' learning behavior characteristics is an important means for educators to better understand students and improve the quality and effectiveness of teaching [1][2][3]. Teachers can analyze students' learning characteristics by observing their classroom participation, questioning, group discussions, and other behaviors during classroom teaching.…”

Retracted: System Analysis of the Learning Behavior Recognition System for Students in a Law Classroom: Based on the Improved SSD Behavior Recognition Algorithm

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”