Player Modeling and Adaptation Methods Within Adaptive Serious Games

Hare, Ryan; Tang, Ying

doi:10.1109/tcss.2022.3203926

Cited by 8 publications

(5 citation statements)

References 67 publications

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“…By soliciting feedback, the NPC gains information about the effectiveness of learning materials and about a learner's difficulties. The collected information will be the basis to create what is called a student/player model [6], [7]. By analyzing the data to determine the reason behind previous learning mistakes or success in the past, this type of intelligence helps gain insights into how learners will behave in the future.…”

Section: Tutor Npcmentioning

confidence: 99%

“…For example, a systematic approach from the basic structure to models creating physical/virtual space to parallel intelligence for decision-makings is required to provide meaningful and enjoyable learning experiences in Metaverse. As reported in two recent surveys [6], [7] any learning environment with adaptation capability must be equipped with both "eyes" to track learners' actions and a "brain" to make decisions on how to dynamically adjust its elements or provide assistance.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

Hare

Tang

2023

IEEE Trans. Syst. Man Cybern, Syst.

Self Cite

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Metaverse has gained increasing interest in education, with much of literature focusing on its great potential to enhance both individual and social aspects of learning. However, little work has been done to address the systems and technologies behind providing meaningful Metaverse learning. This article proposes a technical framework to address this research gap, where a hierarchical multiagent reinforcement learning approach with experience sharing is developed to augment the intelligence of nonplayer characters in Metaverse learning for personalization. The utility and benefits of the proposed framework and methodologies are demonstrated in Gridlock, a Metaverse learning game, as well as through extensive simulations.

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Section: Tutor Npcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

Hare

Tang

2023

IEEE Trans. Syst. Man Cybern, Syst.

Self Cite

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“…Machine learning techniques can be useful for personalization and adaptation. Some authors have reviewed intelligent adaptation methods used in serious games for education, finding that the most used were rule-based, Bayesian, fuzzy, and reinforcement learning [ 42 ]. Another related technique to personalization in serious games was incorporating dynamics of learner behaviors as learning attributes in a Petri net model for knowledge reasoning and learning [ 43 ].…”

Section: Related Workmentioning

confidence: 99%

Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal

González-González

Muñoz-Cruz

Toledo-Delgado

et al. 2023

Sensors

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A key factor for successfully implementing gamified learning platforms is making students interact with the system from multiple digital platforms. Learning platforms that try to accomplish all their objectives by concentrating all the interactions from users with them are less effective than initially believed. Conversational bots are ideal solutions for cross-platform user interaction. In this paper, an open student–player model is presented. The model includes the use of machine learning techniques for online adaptation. Then, an architecture for the solution is described, including the open model. Finally, the chatbot design is addressed. The chatbot architecture ensures that its reactive nature fits into our defined architecture. The approach’s implementation and validation aim to create a tool to encourage kids to practice multiplication tables playfully.

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“…Some reviews cover multiple domains and investigate specific technologies or methodologies. For instance, Hare and Tang [38] categorized player modeling and game adaptation methods in serious games for higher education. Chavez and Bayona [39] selected articles published between 1999 and 2017 across various fields of study to identify the characteristics of virtual reality technology and its impact on the learning process.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of the Design of Serious Games for Innovative Learning: Augmented Reality, Virtual Reality, or Mixed Reality?

Lee,

Wei,

Chui

et al. 2024

Electronics

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The recent integration of educational technologies and emerging learning approaches into education systems has been driven largely by the pandemic. This paper conducts a systematic review and delves into the new wave of research on serious games designed for innovative learning using augmented reality (AR), virtual reality (VR), and mixed reality (MR). The review was referenced to the review protocol, PRISMA 2020. Using the Scopus Database with a time filter from 2007 to 2023 (27 July), we searched 329 articles and shortlisted 273 relevant studies. Notably, European countries contributed the most (62.9%) to this research area. Among the most frequent keywords, VR (90.9%) was commonly used in AR/VR/MR, while e-learning (95.3%) was among the popular innovative learning approaches. Further research studies are needed to employ AR and MR technologies, as well as other innovative learning approaches, to enable performance evaluation and comparison of various educational technologies and learning approaches. We conducted an in-depth analysis of the relevant studies and their basic characteristics. Additionally, we introduced 15 essential and recently published AR/VR/MR standards to ensure better reliability, quality, and safety of architectures, systems, products, services, and processes. To facilitate performance evaluation and analysis, we surveyed 15 recently published benchmark education datasets. This review suggested four future research directions, including multisensory experiences, generative artificial intelligence, personalization and customization, and real-time interaction.

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Player Modeling and Adaptation Methods Within Adaptive Serious Games

Cited by 8 publications

References 67 publications

Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

Hierarchical Deep Reinforcement Learning With Experience Sharing for Metaverse in Education

Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal

A Systematic Review of the Design of Serious Games for Innovative Learning: Augmented Reality, Virtual Reality, or Mixed Reality?

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