Gaze based Notetaking for Learning from Lecture Videos
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 highlight key content from the lecture video for notetaking. Second, our system provides adaptive video 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.
CHI