FitVid: Responsive and Flexible Video Content Adaptation

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Mobile video-based learning attracts many learners with its mobility and ease of access. However, most lectures are designed for desktops. Our formative study reveals mobile learners' two major needs: more readable content and customizable video design. To support mobile-optimized learning, we present FitVid, a system that provides responsive and customizable video content. Our system consists of (1) an adaptation pipeline that reverse-engineers pixels to retrieve design elements (e.g., text, images) from videos, leveraging deep learning with a custom dataset, which powers (2) a UI that enables resizing, repositioning, and toggling in-video elements. The content adaptation improves the guideline compliance rate by 24% and 8% for word count and font size. The content evaluation study (n=198) shows that the adaptation significantly increases readability and user satisfaction. The user study (n=31) indicates that FitVid significantly improves learning experience, interactivity, and concentration. We discuss design implications for responsive and customizable video adaptation.
Publisher
Association for Computing Machinery
Issue Date
2022-05-02
Language
English
Citation

2022 CHI Conference on Human Factors in Computing Systems, CHI 2022

DOI
10.1145/3491102.3501948
URI
http://hdl.handle.net/10203/298872
Appears in Collection
CS-Conference Papers(학술회의논문)
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