Game mechanics encode a computer game’s underlying principles as their internal rules. These game rules consist of information relevant to a specific learning content in the case of a serious game. This paper describes an approach to predict the learning effect of computer games by analyzing the structure of the provided game mechanics. In particular, we utilize the Gamified Knowledge Encoding model to predict the learning effects of playing the computer game Kerbal Space Program (KSP). We tested the correctness of the prediction in a user study evaluating the learning effects of playing KSP. Participants achieved a significant increase in knowledge about orbital mechanics during their first gameplay hours. In the second phase of the study, we assessed KSP’s applicability as an educational tool and compared it to a traditional learning method in respect to the learning outcome. The results indicate a highly motivating and effective knowledge learning. Also, participants used KSP to validate complex theoretical spaceflight concepts.

游戏机制将计算机游戏的基本原理编码为内部规则。在严肃的游戏中，这些游戏规则包含与特定学习内容相关的信息。本文介绍了一种通过分析所提供游戏机制的结构来预测计算机游戏学习效果的方法。特别是，我们利用游戏化知识编码模型来预测玩计算机游戏Kerbal Space Program（KSP）的学习效果。我们在评估玩KSP的学习效果的用户研究中测试了预测的正确性。在最初的游戏时间里，参与者对轨道力学的知识有了显着的增长。在研究的第二阶段，我们评估了KSP作为一种教育工具的适用性，并将其与传统的学习方法进行了学习比较。结果表明，这是一种高度激励和有效的知识学习。此外，参与者还使用KSP来验证复杂的理论航天概念。