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We introduce the Adaptation and Assessment (TwoA) component, an open-source tool for serious games, capable of adjusting game difficulty to player skill level. Technically, TwoA is compliant with the RAGE (Horizon 2020) game component architecture, which offers seamless portability to a variety of popular game development platforms. Conceptually, TwoA uses a modified version of the Computer Adaptive Practice algorithm. Our version offers two improvements over the original algorithm. First, the TwoA improves balancing of player's motivation and game challenge. Second, TwoA reduces the selection bias that may arise for items of similar difficulty by adopting a fuzzy selection rule. These improvements are validated using multi-agent simulations.

Software assets are key output of the RAGE project and they can be used by applied game developers to enhance the pedagogical and educational value of their games. These software assets cover a broad spectrum of functionalities – from player analytics including emotion detection to intelligent adaptation and social gamification. In order to facilitate integration and interoperability, all of these assets adhere to a common model, which describes their properties through a set of metadata. In this paper the RAGE asset model and asset metadata model is presented, capturing the detail of assets and their potential usage within three distinct dimensions – technological, gaming and pedagogical. The paper highlights key issues and challenges in constructing the RAGE asset and asset metadata model and details the process and design of a flexible metadata editor that facilitates both adaptation and improvement of the asset metadata model.