A Learning-Embedded Attributed Petri Net to Optimize Student Learning in a Serious Game
A Learning-Embedded Attributed Petri Net to Optimize Student Learning in a Serious Game
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DOI：
10.1109/tcss.2021.3132355
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发表时间：
2023-06
影响因子：
5
通讯作者：
Jing Liang;Ying Tang;Ryan Hare;Ben Wu;Feiyue Wang
中科院分区：
文献类型：
--
作者：
Jing Liang;Ying Tang;Ryan Hare;Ben Wu;Feiyue Wang
严肃游戏（SG）因其作为增强学习的教育工具的巨大潜力而变得越来越重要。然而，很少有人致力于从系统的角度解决 SG 中学生学习的优化问题。本文通过开发学习嵌入属性 Petri 网 (LAPN) 模型来表示游戏流程和学生学习决策来应对这一挑战。然后，通过将学习机制（即强化学习 (RL) 和随机森林分类）纳入用于知识推理和学习的 Petri 网模型来解决游戏中学习者行为的动态问题。最后提出了一种基于LAPN的算法，旨在引导学习者在游戏中更快更好地解决问题。然后在 SG Gridlock 中证明了所提出的模型和算法的优点。
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 Gridlock.