Over the last decade, serious games have become accepted educational tools and the idea of using the great strength of modern computer games for educational purposes experienced a significant boost. From an educational perspective, computer games offer a promising approach to make learning more engaging, satisfying, and probably more effective.
However, playing experience and learning motivation are fragile assets; to be enjoyable, a computer game must be balanced well, meaning the game must match an individual player’s playing preferences, playing styles, and playing capabilities in a suitable way in order to too one-sided gameplay. An appropriate adaptation is of crucial importance in order to reach and maintain fun and enjoyment on the one hand and effective, successful learning on the other hand.
The starting point of an educationally suitable adaptation and good game-balancing is to equip the game with and understanding of the learning domain, aspects and characteristics of the player and, in particular, an understanding about what is going on in the game, for example, motivational states or learning performance. Thus, seamless user performance assessment is a major research topic. It is not a trivial to assess and interpret activity data coming from the game in an unobtrusive manner in order not to harm the gaming experience and perhaps ‘flow’ and requires intelligent technologies.
A recent trend in educational technology is educational data mining (EDM) and learning analytics (LA). The fundamental idea of learning analytics is not new, in essence, the aim is using as much information about learners as possible to understand the meaning of the data in terms of the learners’ strengths, abilities, knowledge, weakness, learning progress, attitudes, and social networks with the final goal of providing the best and most appropriate personalized support.
At this point educational adaptation, game balancing, seamless assessment and EDM/LA meet. New educational technologies leverage the potential of serious games and increase their educational depth.
RAGE features not only assets to realize theory-driven, non-invasive competence and learning diagnostics as well as a general activity tracker and analytics asset. RAGE opens a direct link to interpret in-game activities on a granular level to identify competence development processes, competence gaps, to give detailed recommendations for future learning steps, and – not least – provides a sound basis for in-game adaptations and game balancing. Another strong option is to use RAGE’s API functions to directly connect games to powerful external learning analytics platforms such as Lea’s Box.
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Dörner, R., Göbel, St., Kickmeier-Rust, M. D., Masuch, M., & Zweig, K. (2016). Entertainment Computing and Serious Games. Berlin: Springer.
Kickmeier-Rust, M. D. (2011). Educationally adaptive and beyond: Balanced games for balanced learning. In S. Göbel & J. Wiemeyer (Eds.), Serious Games – Theory, Technology & Practise (pp. 24-27). Proceedings of GameDays 2011, September 12-13, 2011, Darmstadt Germany.
Wiemeyer, J., Kickmeier-Rust, M. D., & Steiner, C. M. (2016). Performance Assessment in Serious Games. In R. Dörner, S. Göbel, W. Effelsberg, and J. Wiemeyer (Eds.), Serious Games: Foundations, Concepts and Practice (pp. 273-302). Berlin: Springer.
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