Simulating serious games: a discrete-time computational model based on cognitive flow theory

This paper presents a computational model for simulating how people learn from serious games. While avoiding the combinatorial explosion of a games micro-states, the model offers a meso-level pathfinding approach, which is guided by cognitive flow theory and various concepts from learning sciences. It extends a basic, existing model by exposing discrete-time evolution, allowing for failure, drop-out, and revisiting of activities, and accounting for efforts made and time spent on tasks, all of which are indispensable elements of gaming. Three extensive simulation studies are presented involving over 10,000 iterations across a wide range of game instances and player profiles for demonstrating model stability and empirical admissibility. The model can be used for investigating quantitative dependences between relevant game variables, gain deeper understanding of how people learn from games, and develop approaches to improving serious game design.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains