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Serious games, i.e. video games with purposes other than pure entertainment, are becoming increasingly important both in education and training environments. Considerable investments are being made and the return is already noticeable in the growing number and influence of serious games, such as Hazmat: Hotzone, Virtual U, Food Force, RescueSim, Ship Simulator and others.

Serious games are becoming increasingly established, but they are still coming of age in terms of player experience. Most serious games are developed ad-hoc and lack sound theoretical foundations, which leads to a number of drawbacks: they are predictable, impersonal and limited by stereotyped training scenarios. In particular, serious games should be designed to prevent (i) training modules from following rigid patterns, (ii) unattractive and predictable game-play, (iii) little advantage being taken of user data collected throughout the game and, worst of all, (iv) little knowledge being employed to guide the course of the game. For example, it would not be effective if all medical trainees in a certain course would have to follow the same timed procedures, in the same standard scenarios, independently of their personal skills or difficulties; and it would be (pedagogically) even worse if their final scores could not be traced back to particular in-game moments. Trainees might just learn how to play the game, instead of how to think and act in similar scenarios.

Many researchers agree that serious games have to become more challenging, unpredictable and user-centric, to be fully embraced as an effective way of knowledge transfer. To prevent the shortcomings above,  serious games should include virtual scenarios that adapt to what and how players need to learn in a given context. This scenario adaptivity should benefit players, by providing them with more flexible challenges and a broader range of (pedagogically) meaningful ways to solve them.

With my research, I expect to contribute with a methodology for supporting the creation of such adaptive virtual scenarios. This methodology should focus on adapting scenarios to: what players need to learn, how they should learn it and what did they failed to learn. We argue that Instructors (or Trainers) already possess this specific knowledge and, as so, are in a privileged position to steer scenario adaptivity to the expected benefits. With our methodology, the Instructor will use his knowledge on what should be learned by a specific player, to automatically generate virtual scenarios that are suited to player characteristics and learning goals. The Instructor will create in-game situations where objects and events adjust, in real-time, to the player performance and the way he should be learning. After a game session, the Instructor will assess on players performance and that evaluation will be used to re-generate game scenarios where players can play again, focusing on what they failed to learn.

My aim is to embed such knowledge into game worlds and objects, which will become more meaningful in different ways of adapting to benefit players. My research focus on supporting such virtual worlds that are enriched with meaning (or semantics) about learning goals, player performance, game decisions and assessment evaluations.


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