Abstract
In basic education, timely and accurate grasp of students’ classroom learning status can provide real-time information reference and overall evaluation for teachers and managers, which has a very important educational application value. At present, a lot of information technology is applied in the analysis of classroom student behavior state, and the state analysis technology based on a classroom video has the characteristics of strong timeliness, wide dimension, and large capacity, which is especially suitable for the analysis and acquisition of students’ classroom state, and attracts the attention of major educational technology companies. However, the current student state acquisition technology based on video analysis lacks large scenes and has low practicability, and finally, the video-based student classroom behavior state analysis technology mainly focuses on a single behavior feature, which cannot fully reflect the student’s classroom behavior state. In view of the above problems, this study introduces the face recognition algorithm based on a student classroom video and its implementation process, improves the hybrid face detection model based on a traditional model, and proposes the neural network algorithm of student expression recognition based on a visual transformer. The experimental results show that the proposed algorithm based on students' classroom videos can effectively detect students’ attention and emotional state in class.
Highlights
It has always been difficult for teachers and administrators to keep track of all students’ classroom learning
Video images are first captured by the camera, and the data are input into the algorithm to identify, record, and analyze the students’ expressions, head posture, and other explicit actions, and the current classroom behavior state of the students is given
Haar-like face features that have been trained in OpenCv open source visual library were selected in the experiment, and these features were applied to the AdaBoost cascade algorithm for face detection, and the classroom head-up rate of students was calculated by detecting the number of faces, and the average classroom head-up rate of students in a fixed time was obtained
Summary
It has always been difficult for teachers and administrators to keep track of all students’ classroom learning. School administrators do not consider students’ performance in class and only rely on students’ scores and leaders to check the classroom situation. Such unilateral evaluation of teachers’ teaching quality is not accurate, nor can it help teachers to understand the real learning situation of students. Erefore, in this case, the analysis of the classroom teaching process of students listening to the status of education is crucial [4]. Erefore, the combination of statistics and analysis of students’ “head-up rate” in class and intelligent algorithm analysis of students’ emotional state can judge students’ class concentration to a certain extent, helping teachers effectively improve classroom teaching efficiency [7] With the popularity of electronic devices such as smartphones and tablet computers, a large number of “phubbers” have emerged in classroom teaching [6]. erefore, the combination of statistics and analysis of students’ “head-up rate” in class and intelligent algorithm analysis of students’ emotional state can judge students’ class concentration to a certain extent, helping teachers effectively improve classroom teaching efficiency [7]
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