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

Bibliographic Data

ID22106937
AuthorsJoleen Liang (0000-0001-9145-8276, Macau University of Science and Technology), Ying Tang (0000-0002-1745-3685, Qingdao Academy of Intelligent Industries), Ryan Hare (0000-0003-3318-2034, Rowan University), Ben Wu (0009-0005-6260-8999, Rowan University), Fei‐yue Wang (0000-0001-9185-3989, Chinese Academy of Sciences)
Year2023
Volume10
Issue3
Pages869-877
Publication date2023-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2021.3132355
OpenAlexW4206538855
LanguageEN
Citations received2
References cited31

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

Distributed computing · Gridlock · Machine learning · Petri net · Reinforcement learning · Business Process Modeling and Analysis · Computer Science · Innovative Teaching and Learning Methods · Intelligent Tutoring Systems and Adaptive Learning · Artificial Intelligence

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