Video (used as a form of examination or homework) as an efficient approach for examining students’ abilities is drawing increasing attention in the education field. How to assess video assignments effectively and accurately has become a significant topic in academia. This work proposes a method based on a multi-channel CNN-LSTM hybrid architecture to extract and classify image features such as students’ actions and expressions, as well as audio features such as speech rates and pauses in the video assignments, and then conducts a two-category assessment of “qualified” or “unqualified”. Additionally, build this system in a cloud computing environment as a Cloud-based Intelligent Evaluation Service application could provide universal service to meet the needs of multiple teaching units. The proposed method is shown to be feasible and effective through experiments.

中文翻译：

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CIES：使用CNN-LSTM网络的基于云的视频作业智能评估服务

视频（作为考试或家庭作业的一种形式）作为一种检查学生能力的有效方法正在引起教育界的越来越多的关注。如何有效，准确地评估视频任务已成为学术界的重要课题。这项工作提出了一种基于多通道CNN-LSTM混合体系结构的方法，用于提取和分类图像特征（例如学生的动作和表情）以及音频特征（例如语音速率和视频作业中的停顿），然后进行分类对“合格”或“不合格”进行两类评估。此外，该系统在云计算环境中构建，因为基于云的智能评估服务应用程序可以提供通用服务，以满足多个教学单位的需求。