Abstract
Online teaching is gradually spreading widely, but there are problems in online teaching evaluation, such as lack of reference index, students’ learning state feedback being hard to get in real-time. To improve online teaching quality and evaluation, a classroom behavior analysis and evaluation system based on deep learning face recognition technology is proposed. This system conducts the model training and tests on the deep learning development framework (TensorFlow). It implements the frame image processing through the Mask R-CNN network, extracts the skeleton and the angle direction information to establish the vector, thus carries out the classroom behavior recognition and analysis, and serves as the appraisal important index. The behavior analysis is made and the results show that the recognition rate is high and the learning situation feedback is given in real time. The experiment shows that the system has certain robustness and high accuracy in the real scene. It is convenient for classroom teaching management and implementation, and is helpful to improve the teaching quality.
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Zhao, X., Chen, C., Li, Y. (2021). Implementation of Online Teaching Behavior Analysis System. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1452. Springer, Singapore. https://doi.org/10.1007/978-981-16-5943-0_32
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DOI: https://doi.org/10.1007/978-981-16-5943-0_32
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