Department of Faculty Affairs & Faculty Development, Xi'an Technological University, Xi'an, Shaanxi 710072, China.
Comput Intell Neurosci. 2022 Jul 12;2022:4880560. doi: 10.1155/2022/4880560. eCollection 2022.
With the popularization of standardized classrooms in colleges and universities, it is possible to collect video data of students' class status through the camera device in the classroom. With abundant video data sources, it is easy to obtain big data of students' class status images. Unstructured video big data is a topic worthy of research in improving teaching quality. First, the current teaching ability of teachers in colleges and universities is investigated, and its problems are found. Then, the You Only Look Once (YOLO) network in the object detection network is mainly studied. The deficiencies in the network structure are further explored and optimized. It is used in real classroom scenarios as well as on student expression detection problems. Finally, the proposed scheme is tested. The test results show that at present, 20% and 38% of teachers in higher vocational colleges think that they are "dissatisfied" with their classroom teaching and practical guidance ability. And 38% of teachers wanted to improve the bad situation. The accuracy of the proposed model for student expression detection is higher than that of faster-region convolutional neural network and mask-region convolutional neural network by more than 8%, higher than the YOLO v3 model by more than 4%, and higher than YOLO v3 Tiny model above 6%. The proposed model provides some ideas for the application of deep learning technology in the improvement of teachers' teaching ability.
随着高校标准化教室的普及，可以通过教室中的摄像设备采集学生课堂状态的视频数据。拥有丰富的视频数据源，很容易获得学生课堂状态图像的大数据。非结构化视频大数据是提高教学质量值得研究的课题。首先，调查高校教师当前的教学能力，发现其存在的问题。然后，主要研究目标检测网络中的 You Only Look Once（YOLO）网络，进一步探索和优化网络结构。将其应用于真实的课堂场景以及学生表情检测问题中。最后，对提出的方案进行测试。测试结果表明，目前，20%和 38%的高职院校教师认为他们对自己的课堂教学和实践指导能力“不满意”。而 38%的教师希望改善这种糟糕的情况。与更快区域卷积神经网络和掩模区域卷积神经网络相比，所提出的学生表情检测模型的准确性要高出 8%以上，与 YOLO v3 模型相比高出 4%以上，与 YOLO v3 Tiny 模型相比高出 6%以上。所提出的模型为深度学习技术在提高教师教学能力方面的应用提供了一些思路。
