An Analysis of Mode Transition Model form Play to Learning with Mote Carlo Simulation

It is widely recognized empirically that children acquire various kinds of knowledge and skill thorough play but the mechanism of transition from play to learning has not yet been clarified theoretically. The objective of our study is to analyze mode transition model from play-mode to learning-mode with Monte Carlo simulation. To accomplish this objective, we introduce an agent-model with several parameters such as reward of play and interaction with other agents. An agent chooses one of two play states with certain probability. The key results of our study are as follows; (1) Mode transition from play-mode to learning-mode occurs at an earlier stage while reward of play is getting strong. This fact means successfully that playing can turn rapidly into learning if children can get more skill or knowledge through play. (2) Mode transition from play-mode to learning-mode occurs at an earlier stage while interaction with other agents is getting strong. This fact suggests successfully that playing with friends can turn more rapidly into learning than playing alone. (3) The term of interaction with other agents makes greater contribution to mode transition form play-mode to learning-mode than the term of reward of play where interaction constant J is equal to 1.0