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Evaluation of a Game-Based Personalized Learning System

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2021 ASEE Virtual Annual Conference Content Access


Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

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Paper Authors


Ying Tang Rowan University

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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.

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Ryan Hare Rowan University

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Ryan Hare received his B.S. in Electrical and Computer Engineering from Rowan University in 2019. He is currently pursuing his M.S. in Electrical and Computer Engineering at Rowan University. His current research focus is applying machine learning and games to enhance student education, particularly in STEM fields.

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Modern classroom settings require integrating many students of varying backgrounds and mental levels into the same educational process. Ideally, each student should receive personalized support that is tailored to their specific learning style. However, with limited resources and time available to educators and teaching facilities, personalized support is often infeasible. To address this issue, this project focuses on a learning system that uses artificially intelligent agents to provide students with personalized feedback and support. To further engage students, the system is built on top of an existing narrative game environment called Gridlock. Gridlock provides students with a narrative game experience that focuses on creating a traffic light controller to teach students the basics of sequential digital logic design, a core component in both Computer Engineering and Computer Sciences. Gridlock was chosen as it already implements several metacognitive strategies designed to promote student learning, thus giving a solid foundation to build the learning support system on top of. This paper reports the continued results from development and testing of the updated Gridlock system. In testing the game system, students in Introduction to Digital Systems courses and Computer Architecture courses at Rowan University utilized the game as a supplementary tool to assist them with lab work. The overall goal of the improved game system is to improve student comprehension and classroom results. Additionally, the finished system will be fully automated, requiring no intervention from instructors or researchers. Assessments of the effectiveness of the game system will be shown through the following: 1. Student surveys. 2. Relevant course tests administered to students. 3. Student lab work performance. 4. Focus group interviews.

Tang, Y., & Hare, R. (2021, July), Evaluation of a Game-Based Personalized Learning System Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--37108

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