基于深度学习的学习行为感知研究

Research on Learning Behavior Perception Based on Deep Learning

文中探讨了如何利用深度学习技术解决在线教育中的学生情绪识别问题,首先介绍了卷积神经网络的结构和训练过程,然后介绍了AffectNet数据集的特点,接着详细描述了CNN在人脸识别和情绪识别中的应用,以及模型训练和评估方法。实验结果表明,在使用AffectNet数据集进行情绪识别的实验中,CNN模型可以实现较高的准确率、精确率、召回率和F1分数,达到了预期的效果,有望应用于智能教育领域,以提高课堂教学效果。

This paper discusses how to use deep learning technology to solve the problem of student emotion recognition in online education.This paper first introduces the structure and training process of convolutional neural networks,then introduces the characteristics of AffectNet dataset,and then describes the application of CNN in facial recognition and emotion recognition in detail,as well as model training and evaluation methods.The experimental results show that in the experiment of emotion recognition using AffectNet dataset,the CNN model can achieve high accuracy,recall rate and F1 score,and achieve the expected effect.It is expected to be applied in the field of intelligent education to improve classroom teaching effect.