This study presents a logistic model of knowledge growth to investigate the differences between performance and learning in serious games. In contrast with common performance assessment approaches the model takes into account the learning from failures. Monte-Carlo simulations of the model show that performance metrics systematically overestimate the player’s actual learning at early stages in a game and underestimate these at the end. Three evaluation metrics describing the progression, efficacy and efficiency of learning show how these differences depend on the players’ knowledge growth capacities and their success rates in the game. Results from the model when applied to a real serious game are consistent with those from Monte-Carlo simulations. The significance of the study goes beyond the particular details of this study in that it extends and complements the field of educational research with novel computational models and modelling methodologies.
|Number of pages||22|
|Journal||International Journal of Technology Enhanced Learning|
|Publication status||Published - 22 Jan 2022|