Research on Behavior Analysis of Real-Time Online Teaching for College Students Based on Head Gesture Recognition

Abstract

The accessibility of online teaching makes it popular in various teaching scenarios. Most of these researches about online interaction mainly focuse on the communication network stability, facial expression/gesture recognition algorithm and statistical description analysis, and its expertise mainly comes from the fields of computer technology and algorithm engineering. The evaluation and analysis of the effect of online teaching is an important test for the adaptability of such tools in the field of education. However, from the perspective of teachers, there is still a lack of literature on data interpretation after the application of this technology. An experiment based on real online teaching was carried out in this paper. This study uses image recognition technology to process video and extract five kinds of head movement data from dozens of student samples, and then develop statistical description interpretation. Some novel and interesting conclusions indicate that diversified behaviors occurred in real-time online learning. This study obtained data of five high-frequency online learning behaviors, including blinking, yawning, nodding, shaking head and leaving. These behaviors are related to learning state and time. Teaching features, students’ personal characteristics and learning environment have a comprehensive impact on online learning behaviors. The result provides a basis for personalized learning and teaching scheme design in the future. It also helps to enrich online teaching evaluation methods and accelerate the construction of online education framework and rules.

Keywords

• Real-time online teaching
• head gesture recognition
• deep learning algorithm
• experiment