FitVid: Towards Development of Responsive and Fluid Video Content Adaptation

Abstract

Video lecture content is increasingly consumed in mobile environments with varying screen sizes. However, most video content originally designed for desktop is not readable and digestible on small screens. We developed a computational pipeline that automatically adapts learning video content to a smaller screen by segmenting and resizing the in-video elements. We present FitVid, a video interface that provides both the pipeline-generated content adaptation and user-controlled direct manipulation of the in-video elements to fit their own needs. FitVid also provides customized content adaptation based on learners’ manipulation log. In the user study (N=24) we find that FitVid significantly improves learning experience with increased concentration and readability. We further discuss design implications for responsive and customized video content adaptation.