Performance Metrics for Serious Games Will The (Real) Expert Please Step Forward?

摘要

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).