利用计算机视觉分析在线教育中的学习者专注度与参与度
Learner Attentiveness and Engagement Analysis in Online Education Using Computer Vision
AI总结：
本文提出基于计算机视觉与卷积神经网络的在线学习专注度分析系统，在 DAiSEE 上实现多类别多输出分类并输出综合专注指数，经实验优于现有方法。
摘要：
近年来，在线教育和视频会议平台的使用经历了大幅增长。由于虚拟教室的范围有限，教师在授课时可能难以实时分析学习者的注意力和理解情况。在数字化教育模式中，如果能有一种自动化反馈机制，让教师随时了解学习者的专注状态，将大有裨益。本研究提出了一种新颖的基于计算机视觉的方法，用于分析和量化在线学习场景中学习者的专注度、参与度以及其他情感状态。本研究在公开数据集 DAiSEE 上利用卷积神经网络开发了一种多类别、多输出分类方法。在该分类模型之上，研究还开发了一种基于机器学习的算法，可输出学习者的综合专注度指数。此外，本文提出了一条端到端流程，通过处理学习者的实时视频流，向教师提供有关学习者的详细专注度分析。通过将所提方法的实验结果与以往方法进行比较，结果表明，该方法在专注度检测方面优于现有最先进方法。所提出的系统是一套全面、实用且实时的解决方案，具有可部署性和易用性。实验结果也证明了该系统在衡量学习者专注度方面的有效性。
英文摘要：
In recent times, online education and the usage of video-conferencing platforms have experienced massive growth. Due to the limited scope of a virtual classroom, it may become difficult for instructors to analyze learners' attention and comprehension in real time while teaching. In the digital mode of education, it would be beneficial for instructors to have an automated feedback mechanism to be informed regarding learners' attentiveness at any given time. This research presents a novel computer vision-based approach to analyze and quantify learners' attentiveness, engagement, and other affective states within online learning scenarios. This work presents the development of a multiclass multioutput classification method using convolutional neural networks on a publicly available dataset - DAiSEE. A machine learning-based algorithm is developed on top of the classification model that outputs a comprehensive attentiveness index of the learners. Furthermore, an end-to-end pipeline is proposed through which learners' live video feed is processed, providing detailed attentiveness analytics of the learners to the instructors. By comparing the experimental outcomes of the proposed method against those of previous methods, it is demonstrated that the proposed method exhibits better attentiveness detection than state-of-the-art methods. The proposed system is a comprehensive, practical, and real-time solution that is deployable and easy to use. The experimental results also demonstrate the system's efficiency in gauging learners' attentiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
