Learning Behavior Analysis in Classroom Based on Deep Learning

Abstract

In this work, we study learning behavior analysis for automatic evaluation of the classroom teaching. We define five classroom learning behaviors including listen, fatigue, hand-up, sideways and read-write, and construct a class-room learning behavior dataset named as ActRec-Classroom, which includes five categories with 5,126 images in total. With the aid of convolutional neural network (CNN), we propose a classroom learning behavior analysis system framework. Firstly, Faster R-CNN is used to detect human body. Then OpenPose is used to extract key points of human skeleton, faces and fingers. Finally, a CNN based classifier is designed for action recognition. Extensive experiments validate the proposed system. The validation accuracy reaches 92.86% on average, and it meets the need of learning behavior analysis in the real classroom teaching environment.