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Restructuring Unstructured Video Resources for Collaborative Learning and Work

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Association for Computing Machinery


Online video resources have been ubiquitously used for purposes ranging from personal interests to occupation needs. And rich social features provide valuable affordances for collaborative learning and work. However, open access and social nature of content production and sharing exacerbate problems such as information credibility and overload concerns, which may reduce learning or work performances. To efficiently amplify their informative and social power, my studies propose to restructure video resources and associated social communications to facilitate collaborations from the aspects of content production and organization. More specifically, my research design novel social interaction experience to scaffold users to contribute high-quality content and develop scalable computational pipelines to reorganize existing resources.


Liao, Jingxian (2023): Restructuring Unstructured Video Resources for Collaborative Learning and Work. Companion Proceedings of the 2023 ACM International Conference on Supporting Group Work. DOI: 10.1145/3565967.3571757. Association for Computing Machinery. pp. 60–62. Hilton Head, SC, USA