Predicting expert–novice performance as serious games analytics with objective-oriented and navigational action sequences

Abstract

Previous research differentiated expert vs. novice performances based on how (dis)similar novices’ action sequences were from that of the expert’s by way of similarity measures. Action sequences were coded using an ‘objective-oriented’ (or task-based) approach based on the sequence of objectives/tasks completed in-game. Findings from these studies suggest that the task-based similarity measures is a better predictor than (a) distance traversed, and (b) time (of completion).In this study, we suggested an alternative method to code action sequences of experts and novices by way of a ‘navigational’ (or tile-based) approach. We divided a game-map into grids/tiles of different sizes to facilitate tracing of the path traversed by players in game and proceeded to test the effect of grid sizes on differentiating between experts and novices. We further compared the two different action sequence coding approaches and their abilities to measure players’ competency improvement in serious games. The results of the study showed that the size of game grids does matter, and that both task-based and tile-based action sequence coding approaches are useful for serious games analytics.