Research on Classroom Teaching Behavior Analysis and Evaluation System Based on Deep Learning Face Recognition Technology

Abstract

With the continuous enrichment of educational resources, how to analyze and evaluate classroom teaching behavior has become one of the important indicators to measure teaching quality. Based on this, this article builds a classroom teaching behavior analysis and evaluation system based on deep learning face recognition technology, and conducts professional course classroom behavior analysis, from three perspectives: the concentration of the student’s side face, the concentration of the student’s head down, and the concentration of the eyes. Make judgments. The experimental results show that face recognition technology based on deep learning can effectively judge students’ classroom behavior and facilitate teaching management and implementation.

Development and implementation of computer emotion recognition software based on deep learning

“…The multi-layer deep learning model can well extract the relevant features of human face emotions and then identify them, which improves the accuracy of recognition to a certain extent and proves the effectiveness of the algorithm in this paper. In addition, the platform has achieved good results in the actual open testing process, and the accuracy of face recognition has reached 62.5% [10].…”

Emotion Recognition for Improving Online Learning Environments: A Systematic Review of the Literature

“…In contrast, the other eye is not visible in a fixed head position. c) Low Concentration: Low concentration is determined by periodic movement of the head and eye for longer [22]. The determination of low concentration using the Viola-Jones algorithm is concrete as it readily determines that students concentrate more on the background activities than lectures.…”

“…It was argued that simplifying head and eye responses in class can sufficiently undermine the effectiveness of SERS [23]. However, studies have shown that the primary engagement attributes displayed by students for attention are their heads and eyes despite their characteristics, which may be introverted or extroverted [22]. Therefore, the efficiency of the Viola-Jones algorithm and LBP should be extensively accredited in the modern education system.…”

“…Engagement of students and teachers in online learning environments was significantly tampered with increased barriers in communication. However, the emotional recognition system especially empowered the affective computing method to enhance engagement among students and teachers [22]. Teachers were increasingly aware of their student's behavior, which enabled them to extensively finalize their approach toward the learning environment.…”

Intelligent Music Chorus Recognition and Evaluation System Based on Deep Learning

“…The traditional machine learning algorithms do not provide us with answers. From the technical level, the problems facing traditional speech recognition technology are mainly reflected in these aspects of [9][10]: (1) In terms of feature extraction. Traditional speech feature extraction methods require experts to study the nature of acoustic features.…”