Serious games are highly interactive systems which can therefore capture large amounts of player interaction data. This data can be analyzed to provide a deep insight into the effect of the game on its players. However, traditional techniques to assess players of serious games make little use of interaction data, relying instead on costly external questionnaires. We propose an evidence-based process to improve the assessment of players by using their interaction data. The process first combines player interaction data and traditional questionnaires to derive and refine game learning analytics variables, which can then be used to predict the effects of the game on its players. Once the game is validated, and suitable prediction models have been built, the prediction models can be used in large-scale deployments to assess players solely based on their interactions, without the need for external questionnaires. We briefly describe two case studies where this combination of traditional questionnaires and data mining techniques has been successfully applied. The evidence-based assessment process proposed radically simplifies the deployment and application of serious games in real class settings.

严肃的游戏是高度互动的系统，因此可以捕获大量玩家互动数据。可以对这些数据进行分析，以深入了解游戏对玩家的影响。但是，用于评估严肃游戏玩家的传统技术很少使用互动数据，而是依靠昂贵的外部调查表。我们提出了一个基于证据的流程，以通过使用玩家的交互数据来改善玩家的评估。该过程首先将玩家互动数据和传统调查表结合起来，以推导和完善游戏学习分析变量，然后将其用于预测游戏对其玩家的影响。验证游戏并建立合适的预测模型后，预测模型可用于大规模部署中，仅根据参与者的互动来评估参与者，而无需外部调查表。我们简要介绍了两个案例研究，其中传统问卷调查和数据挖掘技术的这种结合已成功应用。提出的基于证据的评估过程从根本上简化了在真实课堂环境中严肃游戏的部署和应用。