TY - GEN
T1 - A Graph-based Approach for Adaptive Serious Games
AU - Hare, Ryan
AU - Patel, Nidhi
AU - Tang, Ying
AU - Patel, Pankti
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In higher education, researchers have recently focused on exploring automated, personalized instructional systems to enhance students' learning experiences. Motivated by this, we propose a personalized instructional system using a straightforward graph system to offer an educational game that is effective for students and intuitive for developers. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated with our system would both detect player errors and provide personalized assistance to direct a player in the direction of a correct solution. To verify system performance, we present comparison testing on a group of students engaging in the game both with and without AI. Students who played the AI-assisted game showed an average 20% decrease in time needed and average 58% decrease in actions taken to complete the game.
AB - In higher education, researchers have recently focused on exploring automated, personalized instructional systems to enhance students' learning experiences. Motivated by this, we propose a personalized instructional system using a straightforward graph system to offer an educational game that is effective for students and intuitive for developers. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated with our system would both detect player errors and provide personalized assistance to direct a player in the direction of a correct solution. To verify system performance, we present comparison testing on a group of students engaging in the game both with and without AI. Students who played the AI-assisted game showed an average 20% decrease in time needed and average 58% decrease in actions taken to complete the game.
UR - http://www.scopus.com/inward/record.url?scp=85145353347&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145353347&partnerID=8YFLogxK
U2 - 10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9928013
DO - 10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9928013
M3 - Conference contribution
AN - SCOPUS:85145353347
T3 - Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022
BT - Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022
A2 - Fortino, Giancarlo
A2 - Gravina, Raffaele
A2 - Guerrieri, Antonio
A2 - Savaglio, Claudio
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Dependable, Autonomic and Secure Computing, 20th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing, 2022 IEEE International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022
Y2 - 12 September 2022 through 15 September 2022
ER -