Automatic Academic Confusion Recognition In Online Learning Based On Facial Expressions
Automatic Academic Confusion Recognition In Online Learning Based On Facial Expressions
复制标题
基于面部表情的在线学习学术混乱自动识别
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Weigang Lu
中科院分区：
文献类型：
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作者：
Zheng Shi;Ya Zhang;Cunling Bian;Weigang Lu
学业困惑是最常见的学业情绪之一，及时识别和解决困惑有助于提高学习效果。在研究中，一个模型，以识别基于面部表情的在线学习中的学业混淆。该模型主要包括三个部分：混淆实验、图像预处理和识别方法比较。首先，设计了一组在线学习中的混淆诱导实验。然后对图像进行预处理，提高识别效果。第三部分将方向梯度直方图（HOG）、局部二值模式（LBP）、支持向量机（SVM）和卷积神经网络（CNN）相结合，形成HOG-SVM、LBP-SVM、CNN和CNN-SVM四种分类方法，并进行了应用。实验结果表明，大部分方法都能有效地检测出学生的学业困惑，其中CNN-SVM的预测性能最好，平均准确率为0.938。本研究提出的模型可以为教学辅助系统的学习者情感建模提供技术支持。
Academic confusion is one of the most common academic emotions, timely identification and resolution of confusion are helpful to improve learning effect. In the research, a model is developed to identify the academic confusion in online learning based on facial expressions. The model mainly includes three parts: confusion-inducing experiments, image preprocessing, and recognition methods comparing. Firstly, a set of confusion-inducing experiments in online learning are designed. Then the images are preprocessed to improve the recognition effect. In the third part, Histogram of Oriented Gradient (HOG), Local Binary Patterns (LBP), Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are combined to form four methods of HOG-SVM, LBP-SVM, CNN and CNN-SVM, which are employed. The experimental results show that most of the methods can effectively detect students’ academic confusion, and the CNN-SVM has the best predictive performance with an average accuracy of 0.938. The Model proposed in this study can provide a technical support for learner emotional modeling of teaching assistant systems.