Abstract
The “flipped classroom” teaching paradigm not only follows the cognitive rules of the learners, but it also subverts and reverses the standard classroom teaching process. Problem-oriented, teacher-led, student-centered, and mixed teaching approaches are the key teaching methods in the flipped classroom teaching model, which focuses on students’ procedural knowledge acquisition and critical thinking training. There are a lot of studies on the specific practice path of the “flipped classroom” teaching style right now, but there are not many on the learning involvement of college English students in this approach. According to studies, the level of student participation in classroom learning is the most important factor limiting the efficiency of teaching. The lack of research in this subject greatly limits the “flipped classroom” teaching model’s ability to improve college English classroom teaching quality. The degree of engagement between teachers and students, the enthusiasm of students in class, and the competence of teachers to educate are all reflected in student conduct in the classroom. Understanding and evaluating the behaviors and activities of students in the classroom are helpful in determining the state of students in the classroom, as well as improving the flipped classroom teaching technique and quality. As a result, the convolutional neural network is used to recognize student behavior in the classroom. The loss function of VGG-16 has been enhanced, the distance inside the class has been lowered, the distance between classes has been increased, and the recognition accuracy has improved. Accurate recognition of classroom behavior is beneficial in developing methods to improve teaching quality.
Highlights
The flipped classroom teaching model [1,2,3] has grown into a magnificent landscape of education and teaching reform [4] as a new teaching model [5,6,7]
The main contributions of this paper are as follows: (1) This paper proposes a teaching quality promotion model for college English flipped classroom based on the assistance of convolutional neural network, which can improve the teaching method of flipped classroom and improve the quality of flipped classroom teaching
The student behavior recognition algorithm based on the ResNet network [28] first trains on the ImageNet data set, uses transfer learning to apply the ResNet network to student behavior recognition, and realizes the recognition of student behaviors such as looking left and right, raising hands, standing, and sleeping
Summary
The flipped classroom teaching model [1,2,3] has grown into a magnificent landscape of education and teaching reform [4] as a new teaching model [5,6,7]. There are some studies that reflect students’ abnormal behavior in the examination room These studies can be classified as classroom behavior recognition because they are behavior recognition in a classroom setting. The student behavior recognition algorithm based on the ResNet network [28] first trains on the ImageNet data set, uses transfer learning to apply the ResNet network to student behavior recognition, and realizes the recognition of student behaviors such as looking left and right, raising hands, standing, and sleeping. Simonyan and Zisserman [29] proposed a dual-stream CNN algorithm for human behavior recognition. This algorithm trains two CNN classifiers, one CNN mainly extracts optical flow features, and the other CNN extracts RGB image information, and the two classifiers fusion of features. Feichtenhofer et al [30] proposed a new spatiotemporal structure based on the dual-stream architecture, which has a new convolutional fusion layer and a spatial fusion layer, which can better extract human behavior characteristics
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