Learning and Play Styles in Open-Ended Games for Learning

This research deals with the analysis of learning behavior and play styles in games for learning. In authentic and complex game-based learning environments, also known as open-ended games, everyone has methods and preferences of their own for solving problems. It is important to know how learners approach problems and how their learning progresses in order to support them well and to provide an individual and effective learning experience for every learner. This research describes how individual learning behavior is analyzed and what consequences such analyses have on the design of innovative games for learning purposes.
In the analysis of learning behavior, the focus lies on cognitive styles which describe behaviors in problem-solving and decision-making environments. There is a special style that has only recently been investigated in classroom environments or in online learning courses with multiple choice questions, namely the impulsive and the reflective style (I/R). Impulsive people tend to react much faster than reflective ones but make more mistakes in their choices. In classic learning environments, impulsive learners are trained to re-think and change their behavior into a more reflective approach since the impulsive behavior is considered a weak behavior. However, in game-based learning environments, impulsive behavior is supported by letting learners try out several possibilities without severe consequences if mistakes are made.
This approach is applied in the strategy and simulation game “Hortus”. It is a new game, specifically developed for the analysis of impulsive and reflective behavior. The game is played online and is strongly related to games like Sim City or Civilization that are very popular as classroom games. Its main distinction from those games is that it is somewhat simplified in order to properly analyze learning behavior. The commercial games are yet too complex for testing our approach. The analysis is conducted with a mixed method. The majority of user data is collected implicitly through the online game. The results are to provide quantitative information regarding learning behavior. Qualitative methods are to reveal user information that cannot be collected implicitly. This qualitative method is based on think-aloud protocols.
Depending on the respective play style, individual learning progress is led in a totally different direction than that intended by the designers. Therefore, it is crucial to know how people might play the game and be aware of different behaviors. According to the results of this research, a new kind of game for learning is planned that adjusts specific elements in the game system in order to lead to the intended learning goals.