CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918149390073856 |
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| author | Zhang, Yuanhao Wang, Yumeng Wang, Xiyuan He, Changyang Huang, Chenliang Ma, Xiaojuan |
| author_facet | Zhang, Yuanhao Wang, Yumeng Wang, Xiyuan He, Changyang Huang, Chenliang Ma, Xiaojuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos Zhang, Yuanhao Wang, Yumeng Wang, Xiyuan He, Changyang Huang, Chenliang Ma, Xiaojuan Human-Computer Interaction 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. |
| title | CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.03767 |