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 (or engaging at all), 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, in turn, is to equip the game with an 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 come together. New educational technologies leverage the potential of serious games and increase their educational depth.
The EU project LEA’s BOX (www.leas-box.eu) has recently launched a handy learning analytics web platform that allows linking multiple sources of educationally relevant data, conduct LA and EDM computations, display the results and feed them back in form of Open Learner Models. The web platform includes also a simple use case for using games or gamfications with LA services.