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A process for designing algorithm-based personalized gamification

机译：设计基于算法的个性化游戏的过程

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摘要

Personalization is an upcoming trend in gamification research, with several researchers proposing that gamified systems should take personal characteristics into account. However, creating good gamified designs is effort intensive as it is and tailoring system interactions to each user will only add to this workload. We propose machine learning algorithm -based personalized content selection to address a part of this problem and present a process for creating personalized designs that allows automating a part of the implementation. The process is based on Deterding's 2015 framework for gameful design, the lens of intrinsic skill atoms, with additional steps for selecting a personalization strategy and algorithm creation. We then demonstrate the process by implementing personalized gamification for a computer-supported collaborative learning environment. For this demonstration, we use the gamification user type hexad for personalization and the heuristics for effective design of gamification for overall design. The result of the applied design process is a context-aware, personalized gamification ruleset for collaborative environments. Lastly, we present a method for translating gamification rulesets to machine-readable classifier algorithm using the CN2 rule inducer.
机译：个性化是游戏化研究中即将出现的趋势，一些研究人员提出，游戏化系统应考虑个人特征。但是，创建良好的游戏化设计实际上是很费力的，为每个用户量身定制系统交互只会增加此工作量。我们提出了基于机器学习算法的个性化内容选择，以解决此问题的一部分，并提出了一种创建个性化设计的过程，该过程可实现部分实现的自动化。该过程基于Deterding的2015年游戏设计框架（内在技能原子的镜头）以及选择个性化策略和算法创建的其他步骤。然后，我们通过为计算机支持的协作学习环境实施个性化游戏化来演示该过程。在此演示中，我们使用游戏化用户类型hexad进行个性化，并使用启发式方法进行游戏化的有效设计以进行总体设计。应用设计过程的结果是针对协作环境的上下文感知，个性化游戏规则集。最后，我们提出了一种使用CN2规则诱导程序将游戏化规则集转换为机器可读分类器算法的方法。

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