Gaming is a domain of profound skill development. Players’ digital traces create data that track the development of skill from novice to expert levels. We argue that existing work, although promising, has yet to take advantage of the potential of game data for understanding skill acquisition, and that to realize this potential, future studies can use the fit of formal learning curves to individual data as a theoretical anchor. Learning-curve analysis allows learning rate, initial performance, and asymptotic performance to be separated out, and so can serve as a tool for reconciling the multiple factors that may affect learning. We review existing research on skill development using data from digital games, showing how such work can confirm, challenge, and extend existing claims about the psychology of expertise. Learning-curve analysis provides the foundation for direct experiments on the factors that affect skill development, which are necessary for a cross-domain cognitive theory of skill. We conclude by making recommendations for, and noting obstacles to, experimental studies of skill development in digital games.

游戏是一个高度技能发展的领域。玩家的数字轨迹创建了跟踪从新手到专家级别技能发展的数据。我们认为，现有工作虽然很有前景，但尚未利用游戏数据的潜力来理解技能获取，为了实现这一潜力，未来的研究可以使用正式学习曲线与个人数据的拟合作为理论锚。学习曲线分析允许将学习率、初始性能和渐近性能分离出来，因此可以作为协调可能影响学习的多个因素的工具。我们使用来自数字游戏的数据回顾了现有的技能发展研究，展示了这些工作如何确认、挑战和扩展现有的关于专业心理学的主张。学习曲线分析为直接实验影响技能发展的因素提供了基础，这对于跨领域的技能认知理论是必要的。最后，我们对数字游戏技能发展的实验研究提出建议并指出障碍。