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arXiv · 2502.03767

CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos

Yuanhao Zhang·Yumeng Wang·Xiyuan Wang·Changyang He·Chenliang Huang·Xiaojuan Ma
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Abstract

Danmaku, a system of scene-aligned, time-synced, floating comments, can augment video content to create 'collective knowledge'. However, its chaotic nature often hinders viewers from effectively assimilating the collective knowledge, especially in knowledge-intensive science videos. With a formative study, we examined viewers' practices for processing collective knowledge and the specific barriers they encountered. Building on these insights, we designed a processing pipeline to filter, classify, and cluster danmaku, leading to the development of CoKnowledge - a tool incorporating a video abstract, knowledge graphs, and supplementary danmaku features to support viewers' assimilation of collective knowledge in science videos. A within-subject study (N=24) showed that CoKnowledge significantly enhanced participants' comprehension and recall of collective knowledge compared to a baseline with unprocessed live comments. Based on our analysis of user interaction patterns and feedback on design features, we presented design considerations for developing similar support tools.

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Yuanhao Zhang, Yumeng Wang, Xiyuan Wang, Changyang He, Chenliang Huang, Xiaojuan Ma. 2025-02-06. CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos. https://doi.org/10.1145/3706598.371368

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