Building Community Knowledge In Online Competitions

Autor: Ruijia Cheng, Mark Zachry
Rok vydání: 2020
Předmět:
Zdroj: Proceedings of the ACM on Human-Computer Interaction. 4:1-22
ISSN: 2573-0142
DOI: 10.1145/3415250
Popis: Knowledge building is a prevalent feature in open online systems, but it is challenging to motivate participants to contribute and to maintain quality in the participants' contributions. Open online competitions, where participants compete for prizes with knowledge artifacts, offer a potential design model for online systems to incentivize community knowledge building activities. However, while there is evidence that participants contribute to public knowledge and share it during competitions, it remains unclear how and why they do so. In this study, through interviews with 14 participants in Kaggle Competitions, we investigate participants' motivation, practices, and challenges when contributing to community knowledge under a competitive structure. We find that competitive mechanisms impact expert and beginner participants very differently in their public knowledge building behaviors. Experts contribute to shared knowledge in order to compete for reputation, while they tend to form their own niches and only share knowledge artifacts that are abstract and not usable by less experienced participants. Beginners are often driven away from contributing to shared knowledge because of their vulnerable social image. We leverage Scardamalia's framework for Knowledge Building Communities to discuss the different challenges and opportunities that competitive design brings to expert and beginner participants. We offer design implications for effectively implementing competitive mechanisms that could benefit both expert and beginner participants in future knowledge building systems.
Databáze: OpenAIRE