Towards an Empirically Based Gamification Pattern Language using Machine Learning Techniques

The ineffectiveness of many gamification projects can be attributed to wrong decisions made during the conceptual design phase, especially in the selection of game design elements. This paper introduces a data driven method of creating a gamification pattern language similar to software design patterns to help gamification designers select such elements. Thanks to modern machine learning technologies such a pattern language can be based on a comprehensive empirical analysis to assess the actual use of game design elements in games. This paper is the first report on an ongoing research project that has been carried out since the beginning of 2017 in cooperation with the German Games Archive to extract game design elements from more than 30,000 board games using machine learning techniques. Initial tests based on support vector classification and 4,000 games show that game design elements can be reliably identified with accuracy rates between 80 and 90%.