In order for techniques from Model Driven Engineering to be accepted at large by the game industry, it is critical that the effectiveness and efficiency of these techniques are proven for game development. There is no lack of game design models, but there is no model that has surfaced as an industry standard. Game designers are often reluctant to work with models: they argue these models do not help them design games and actually restrict their creativity. At the same time, the flexibility that model driven engineering allows seems a good fit for the fluidity of the game design process, while clearly defined, generic models can be used to develop automated design tools that increase the development’s efficiency.
For over thirty years, there has been a discussion about the effectiveness of educational games in comparison to traditional learning materials. To help further this discussion, we aim to understand ‘how educational games work’ by formalising (and visualising) the educational and motivational aspects of such games. We present a model that focuses on the relationship between three different aspects: user properties, game mechanics, and learning objectives. In two example cases, we have demonstrated how the model can be used to analyse existing games and their game/instructional design, and suggest possible improvements in both motivational and educational aspects based on the model. As such, we introduce a novel approach to analysing educational games and, by inference, a novel design process for designing more effective educational games.
The growing availability of data offers plenty of opportunities for data driven innovation of business models for SMEs like interactive media companies. However, SMEs lack the knowledge and processes to translate data into attractive propositions and design viable data-driven business models. In this paper we develop and evaluate a practical method for designing data driven business models (DDBM) in the context of interactive media companies. The development follows a design science research approach. The main result is a step-by-step approach for designing DDBM, supported by pattern cards and game boards. Steps consider required data sources and data activities, actors and value network, revenue model and implementation aspects. Preliminary evaluation shows that the method works as a discussion tool to uncover assumptions and make assessments to create a substantiated data driven business model.
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