Learning curve analysis in a large-scale, drill-and-practice serious math game: Where is learning support needed?

Abstract

This paper applies data-driven methods to understand learning and derives game design insights in a large-scale, drill-and-practice game: Spatial Temporal (ST) Math. In order for serious games to thrive we must develop efficient, scalable methods to evaluate games against their educational goals. Learning models have matured in recent years and have been applied across e-learning platforms but they have not been used widely in serious games. We applied empirical learning curve analyses to ST Math under different assumptions of how knowledge components are defined in the game and map to game contents. We derived actionable game design feedback and educational insights regarding fraction learning. Our results revealed cases where students failed to transfer knowledge between math skills, content, and problem representations. This work stresses the importance of designing games that support students’ comprehension of math concepts, rather than the learning of content- and situation-specific skills to pass games.