A Learning-Embedded Attributed Petri Net to Optimize Student Learning in a Serious Game

Abstract

Serious games (SGs) are a practice of growing importance due to their high potential as an educational tool for augmented learning. However, little effort has been devoted to address student learning optimization in an SG from a systematic point of view. This article tackles this challenge by developing a learning-embedded attribute Petri net (LAPN) model to represent game flow and student learning decision-makings. The dynamics of learner behaviors in game are then addressed through the incorporation of learning mechanisms (i.e., reinforcement learning (RL) and random forest classification) into the Petri net model for knowledge reasoning and learning. Finally, an algorithm based on LAPN is proposed, aiming to guide learners to achieve a faster and better solution to problem-solving in game. The benefit of the proposed model and algorithm is then demonstrated in the SG <i>Gridlock</i>.