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<p>Wiki-based collaborative writing has been widely adopted in education to facilitate collaborative learning. However, research shows that students adopt different collaboration patterns during wiki-based collaborative writing, and that collaboration patterns may vary in how they enhance learning. It is thus critical to understand the relationship between the nature of collaboration and learning. This study examined 95 university English as foreign language (EFL) learners’ performances in wiki-based collaborative writing projects. Analyses of the student survey and interview responses and students’ archived performances on the wiki platform showed that they adopted three collaboration approaches, and that the approach featuring high equality and mutuality was associated with the greatest enjoyment of, perceived learning from, and positive attitudes towards collaborative writing. The study further found that equality and mutuality exert different impacts on different learning outcomes, and that labour division and peer contribution during collaboration determine the learning potential of collaborative writing for EFL students.</p>

Federated Learning (FL) enables participants to "share'' their sensitive local data in a privacy preserving manner and collaboratively build machine learning models. In order to sustain long-term participation by high quality data owners (especially if they are businesses), FL systems need to provide suitable incentives. To design an effective incentive scheme, it is important to understand how FL participants respond under such schemes. This paper proposes FedGame, a multi-player game to study how FL participants make action selection decisions under different incentive schemes. It allows human players to role-play under various conditions. The decision-making processes can be analyzed and visualized to inform FL incentive mechanism design in the future.

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