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Automatic Emotion Detection as a Teaching Aid in Online Knowledge Assessment

机译:在线知识评估中的教学辅助自动情绪检测

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In this paper, we present an initial study of possibilities of applying Artificial Intelligence (AI) and computer vision-based approaches aimed to improve and alleviate the process of conducting knowledge assessment over the Microsoft Teams platform. We did that by developing a deep-neural-network-based system which is able to locate faces and predict emotions based on students’ facial expressions. The system was evaluated on videos recorded during an online assessment of the ability of students to train and deploy deep learning solutions using Python and TensorFlow. We present results of this evaluation and show that, although the accuracy of our algorithm is limited at frame level, as we optimized for computational performance, it provides sufficient information to identify key changes on students’ behavior, which should be brought to the teachers’ attention.
机译:在本文中,我们提出了应用人工智能(AI)和基于计算机视觉的方法的初步研究,旨在提高和缓解对微软团队平台进行知识评估的过程。 我们通过开发基于深度网络的基于网络的系统来实现这一点,该系统能够找到基于学生的面部表情的面孔和预测情绪。 在在线评估学生使用Python和Tensorflow的能力培训和部署深度学习解决方案的能力期间,对系统进行了评估。 我们提出了该评估的结果,并表明,尽管我们的算法的准确性受到帧级的限制,但我们针对计算性能优化,它提供了足够的信息来识别学生行为的关键变化,应该为教师带来。 注意力。

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