Evaluation of an AI-assisted Adaptive Educational Game System
- Conference
- Location
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Newark, New Jersey
- Publication Date
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April 22, 2022
- Start Date
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April 22, 2022
- End Date
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April 23, 2022
- Tagged Topic
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Diversity
- Page Count
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10
- DOI
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10.18260/1-2--40052
- Permanent URL
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https://peer.asee.org/40052
- Download Count
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206
Paper Authors
Ying Tang Rowan University
Ying Tang received the B.S. and M.S. degrees from the Northeastern University, P. R. China, in 1996 and 1998, respectively, and Ph.D degree from New Jersey Institute of Technology, Newark, NJ, in 2001. She is currently a Professor of Electrical and Computer Engineering (ECE) at Rowan University, Glassboro, NJ. Her research interests include virtual reality and augmented reality, artificial intelligence, and modeling and scheduling of computer-integrated systems. Dr. Tang is very active in adapting and developing pedagogical methods and materials to enhance engineering education. Her most recent educational research includes the collaboration with Tennessee State University and local high schools to infuse cyber-infrastructure learning experience into the pre-engineering and technology-based classrooms, the collaboration with community colleges to develop interactive games in empowering students with engineering literacy and problem-solving, the integration of system-on-chip concepts across two year Engineering Science and four year ECE curricula, and the implementation of an educational innovation that demonstrates science and engineering principles using an aquarium. Her work has resulted in over 100 journal and conference papers and book chapters.
Ryan Hare Rowan University
Ryan Hare received his B.S. in Electrical and Computer Engineering from Rowan University in 2019. He is currently pursuing his Ph.D. in Electrical and Computer Engineering at Rowan University. His current research focus is applying artificial intelligence methods to create enhanced educational systems and improve student learning. Further interests include serious games, intelligent tutoring systems, adaptive or intelligent educational systems, and leveraging student data to enhance learning.
Abstract
As education continues to expand, both in outreach and in content, so too does the need for automated systems that can augment a student’s educational process. This work builds on prior developments of a gamified adaptive tutoring system that automates and personalizes a student’s learning process without instructor intervention. Our personalized learning system uses an augmented petri net graph structure to track student progress through the game, allowing us to enable or disable paths based on a student’s performance. As an intelligent component, our system uses reinforcement learning agents to adaptively adjust system behavior based on student performance with the goal of optimizing student learning. The end result is a fully integrated game system that can measure student performance using integrated tests, leveraging that information to adjust game content, address learner misconceptions, and lead to a faster and more effective learning session. As part of continued research, we present data from pilot and comparison testing of our implemented game system.
With our comparison testing, we show that the game provides greater educational utility for students compared to a standard lab. To verify improved educational utility, we present results from content tests given pre- and post-intervention. We further verify the game system's educational utility through an example case of the game adaptation, showing the full process of adapting to a student and providing educational assistance. By sharing our testing and verification, we demonstrate the effectiveness of our intelligent educational game system. In addition, we provide developmental insights for other researchers in this area who seek to implement or improve their own systems.
Tang, Y., & Hare, R. (2022, April), Evaluation of an AI-assisted Adaptive Educational Game System Paper presented at 2022 Spring ASEE Middle Atlantic Section Conference, Newark, New Jersey. 10.18260/1-2--40052
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