Gaze-based Notetaking for Learning from Lecture Videos
Cuong Nguyen and Feng Liu
Computer Science Department, Portland State University
Abstract

Taking notes has been shown helpful for learning. This activity, however, is not well supported when learning from watching lecture videos. The conventional video interface does not allow users to quickly locate and annotate important content in the video as notes. Moreover, users sometimes need to manually pause the video while taking notes, which is often distracting. In this paper, we develop a gaze-based system to assist a user in notetaking while watching lecture videos. Our system has two features to support notetaking. First, our system integrates offline video analysis and online gaze analysis to automatically detect and recommend key content from the lecture video as notes. Second, our system provides adaptive video playback control that automatically reduces the video playback speed or pauses it while a user is taking notes to minimize the user's effort in controlling video. Our study shows that our system enables users to take notes more easily and with better quality than the traditional video interface.

Paper
Cuong Nguyen and Feng Liu. Gaze-based Notetaking for Learning from Lecture Videos
ACM CHI 2016. PDF  

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Demo Video 
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Acknowledgment
This video demo uses a source clip from YouTube user Bitcoin Course under a Creative Commons license. This video is narrated by Amy Burdette. This work was supported by NSF IIS-1321119 and CNS-1218589.