基于微表情识别的线上学习指导系统设计研究
Research on the Design of an Online Learning Guidance System Based on Micro-Expression Recognition
李世豪 1袁德成 1梁国利1
作者信息
- 1. 沈阳化工大学信息工程学院,辽宁沈阳 110142
- 折叠
摘要
采用基于深度学习算法识别线上学习状态的各种微表情,选出6种在线学习中出现的强度较高的情感状态,设计和训练深度学习卷积神经网络模型,测试6种情感状态的准确率.结果表明:基于深度学习神经网络模型对与学习进程相关的情感识别具有较高的准确率和较短的响应时间,能够实时反映线上学习者的情感状态变化.在此基础上结合大数据技术建立学习评价反馈机制,可以优化教学进程,提高学习者的学习效率.
Abstract
The deep learning algorithm was used to identify various micro-expressions of online learn-ing states,that is,6 emotional states with high intensity in online learning were selected,the convolutional neural network model of deep learning was designed and trained,and the accuracy of 6 emotional states was tested.The results show that the neural network model based on deep learning has high accuracy and short response time for emotion recognition related to learning process,and can reflect the change of on-line learners'emotional state in real time.Further,it can be combined with big data technology to establish learning evaluation and feedback mechanism,optimize teaching process,and improve learning efficiency of learners.
关键词
线上学习/情感计算/深度学习神经网络/学习情感Key words
online learning/emotional state/deep learning neural network/learning emotion引用本文复制引用
出版年
2024
NETL
NSTL
万方数据