Attention Based Video Summaries of Live Online Zoom Classes
Hyowon Lee, Mingming Liu, Hamza Riaz, Navaneethan Rajasekaren, Michael, Scriney, Alan F. Smeaton

TL;DR
This paper presents a system that logs student attention during live Zoom classes and generates personalized video summaries highlighting segments where attention was low, aiding learning and review.
Contribution
It introduces a novel attention-based video summarization method for live online classes, incorporating personalized and aggregate attention data while ensuring GDPR compliance.
Findings
System successfully generates personalized summaries based on attention levels.
Professors can review aggregated attention data to identify key lecture segments.
The system is deployed and operational at a university.
Abstract
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…
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Taxonomy
TopicsVideo Analysis and Summarization · Multimedia Communication and Technology · Image Retrieval and Classification Techniques
