Measuring the (dis-)similarity between expert and novice behaviors as serious games analytics
The behavioral differences between expert and novice performance is a well-studied area in training literature. Advances in technology have made it possible to trace players actions and behaviors within an online gaming environment as user-generated data for performance assessment. In this study, we introduce the use of string similarity to differentiate likely-experts from a group of unknown performers (mixture of novices and experts) based on how similar their in-game actions are to that of experts. Our findings indicate that string similarity is viable as an empirical assessment method to differentiate likely-experts from novices and potentially useful as the first performance metric for Serious Games Analytics (SEGA).
string similarity performance metrics serious games analytics (sega) information trails serious games assessment