This paper describes a system developed to help University students get more from their online lectures, tutorials, laboratory and other live sessions. We do this by logging their attention levels on their laptops during live Zoom sessions and providing them with personalised video summaries of those live sessions. Using facial attention analysis software we create personalised video summaries composed of just the parts where a student's attention was below some threshold. We can also factor in other criteria into video summary generation such as parts where the student was not paying attention while others in the class were, and parts of the video that other students have replayed extensively which a given student has not. Attention and usage based video summaries of live classes are a form of personalised content, they are educational video segments recommended to highlight important parts of live sessions, useful in both topic understanding and in exam preparation. The system also allows a Professor to review the aggregated attention levels of those in a class who attended a live session and logged their attention levels. This allows her to see which parts of the live activity students were paying most, and least, attention to. The Help-Me-Watch system is deployed and in use at our University in a way that protects student's personal data, operating in a GDPR-compliant way.
翻译:本文描述了一个为帮助大学生从在线讲座、辅导、实验室和其他现场课程中获得更多关注而开发的系统。我们这样做的方法是在现场直播课时记录他们对膝上型电脑的注意程度,并向他们提供这些现场课的个人化视频摘要。我们使用面部关注分析软件,制作个人化视频摘要,其中仅包含学生关注程度低于某种临界值的部分。我们还可以将其他标准纳入视频摘要制作中,例如学生不关注而其他班级学生不关注的部分,以及其他学生广泛重播的部分视频。基于现场课的注意和使用视频摘要是个人化内容的一种形式,它们是建议突出现场课重要部分的教育视频部分,对专题理解和考试准备都有用。该系统还允许教授审查参加现场会议并记录其关注程度的班级学生的总体关注程度。这样她就能看到现场活动的学生对哪些部分给予最多、最少的关注。“帮助-M-观察”系统是在我们的大学安装和使用一种保护学生个人数据的方式,从而保护学生的GDP数据。