Performance metrics for serious games: Will the (real) expert please step forward?

The literature on human training performance has long attested to the behavioral differences between experts and novices, in which `competency´ is a demonstrable attribute based on a person´s course of action in problem solving. The advances in technology have made it possible to trace players´ actions and behaviors (as user-generated data) within an online serious gaming environment for performance assessment purposes. In this study, we introduce string similarity as a performance metric to identify likely-experts among a group of unknown performers (mixture of novices and experts) according to their in-game course of action in problem solving. Our findings indicate that string similarity is both viable and potentially useful as the first performance metric for Serious Games Analytics (SEGA).