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In-game player performance assessment is especially important for serious games, but it has seldom been considered in academic research in games and simulations. In particular, there is no work on combining game adaptivity with assessment since both fields have been tackling different problems. However, some results point to interesting challenges that indicate a promising role for game adaptivity in assessment.

Chen and Michael have already identified the main challenges that assessment in serious games is facing. Most traditional methods for assessment are not accurate enough for serious games, since they are inspired by the simple feedback mechanisms used in their entertainment counterparts. Identifying and reflecting on mistakes and decisions is especially important when considering serious games. As such, for assessment to improve the player experience, log information and teachers / instructors knowledge should be fully explored and, in same way, converted to assessment information that can be incorporated back in the game, to guide its course.

So far, research in assessment for serious games has been mainly centered on After Action Review (AAR) methods. However, results already demonstrate that the direction identified by Chen contains a lot of potential. AAR systems for military simulations are already being used in innovative ways they were not designed to, not only for assessing past behavior, but especially for planning new future training exercises. In these systems, real-time in-game and AAR assessment information establish an emergent domain culture that could allow the co-creation of future game scenarios. Assessment information could be better explored and even incorporated to potentially influence content in serious games.

There is typically a lot of valuable information in game logs and emerging from AAR sessions, in serious games. This information is far from being fully explored by the game itself, to improve game-play. This happens because logs usually offer an enormous amount of unstructured game data that is therefore difficult to interpret and use. Moreover, AAR information emerges to engage communication between trainees and their instructors and it is not incorporated back in the game. Using this information as a source to guide adaptivity seems a promising, unexplored area. Assessment information can became valuable not only per se, but also to improve game-play, both while playing and in future interactions.

The challenges ahead indicate multiple research directions on what and how to adapt. On the one hand, assessment information could be used to re-generate ”try again” game scenarios, adapted and focused on what the players failed on the previous session. So, offering a re-generated game scenario could simultaneously allow a better understanding of what went wrong, and better opportunities to succeed. Work on this direction should tackle, for example, customized content creation, e.g. adjusted to better achieve a learning goal. On the other hand, on-line adaptivity can also be influenced by assessment methods. Game scenarios, and even intelligent agents, could adapt to the assessment of how players are deviating from these purposes. More than a matter of measuring player performance, it would involve interpreting how that performance is being achieved. One example would be to adapt the game because the player is succeeding in learning, but in a slow pace (instead of because he is just performing too good or too bad).

As an important note, researching the relations between adaptivity and assessment seems to be limited to the serious games domain. We still need plenty of human expert knowledge to make sense of the correct assessment information, either during or after game time.

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