Classroom learning behavior is an important factor affecting students' classroom learning effectiveness. Analyzing students' classroom behavior and exploring its influence on learning effectiveness can provide an important basis for classroom teaching evaluation and form effective feedback information and teaching guidance. Traditional observation of students' classroom behavior is often recorded and marked manually by teachers, which is labor-intensive, subjective and inaccurate. Artificial intelligence computer vision technology brings the possibility of automated annotation of students' classroom behaviors. In this paper, artificial intelligence computer vision recognition technology is applied to the traditional observation of students' classroom behaviors. The classroom learning behaviors of 25 students in a class at a university are used as research samples to test their learning effects, and then deep learning computer vision technology is used to automatically label each classroom learning behavior of different students, and correlation analysis and regression analysis are used to explore the relationship between different classroom learning behaviors and learning effects. It was found that 1) positive learning behaviors have a positive impact on the learning effect and help to improve the classroom learning effect, while negative learning behaviors have a negative impact on the learning effect, 2) note-taking behavior has a more obvious effect on the learning effect than listening to the lecture, and looking at the phone is more likely to significantly reduce the learning effect of students than drifting off. In response to the results, this paper puts forward corresponding improvement measures and suggestions. From the perspectives of both students and teachers, so as to improve students' classroom learning effect, this paper provides an important basis and reference suggestions for classroom teaching evaluation and students' classroom behavior analysis.
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The Impact of Classroom Learning Behavior on Learning Outcomes: A Computer Vision Study
Semantic Scholar · Computer Science · 2023
Abstract
Classroom learning behavior is an important factor affecting students' classroom learning effectiveness. Analyzing students' classroom behavior and exploring its influence on learning effectiveness can provide an important basis for classroom teaching evaluation and form effective feedback information and teaching guidance. Traditional observation of students' classroom behavior is often recorded and marked manually by teachers, which is labor-intensive, subjective and inaccurate. Artificial intelligence computer vision technology brings the possibility of automated annotation of students' classroom behaviors. In this paper, artificial intelligence computer vision recognition technology is applied to the traditional observation of students' classroom behaviors. The classroom learning behaviors of 25 students in a class at a university are used as research samples to test their learning effects, and then deep learning computer vision technology is used to automatically label each classroom learning behavior of different students, and correlation analysis and regression analysis are used to explore the relationship between different classroom learning behaviors and learning effects. It was found that 1) positive learning behaviors have a positive impact on the learning effect and help to improve the classroom learning effect, while negative learning behaviors have a negative impact on the learning effect, 2) note-taking behavior has a more obvious effect on the learning effect than listening to the lecture, and looking at the phone is more likely to significantly reduce the learning effect of students than drifting off. In response to the results, this paper puts forward corresponding improvement measures and suggestions. From the perspectives of both students and teachers, so as to improve students' classroom learning effect, this paper provides an important basis and reference suggestions for classroom teaching evaluation and students' classroom behavior analysis.