TY - GEN
T1 - A survey on data-driven approaches in educational games
AU - Hooshyar, Danial
AU - Lee, Chanhee
AU - Lim, Heuiseok
N1 - Funding Information:
This work was supported by the ICT R&D program of MSIP/IITP [grant number 2016(B0101-16-0340)]. Development of distribution and diffusion service technology through individual and collective Intelligence to digital contents.
PY - 2017/2/13
Y1 - 2017/2/13
N2 - Open-ended educational systems such as games have been broadly under investigation in recent years due to their potential in making learning enjoyable and offering the adaptive pedagogy of intelligent tutoring systems. The most important challenge in building such systems is to predict individual behavior which results in better understanding of the learning process. Model-based methods are a standard way to learn individual behavior in highly-structured systems. However, these methods heavily rely on expert domain knowledge. Since adaptive educational games may create a huge space of actions, applying model-based approaches in these systems are very difficult. In order to counter this difficulty, researchers utilize data-driven methods that are not dependent on expert domain knowledge to learn a subject's behavior based on a history of user interactions. Due to the fact that the potential of applying data-driven approaches in adaptive educational games is still missing, the goal of this report is to cater a survey in the area in order to ease comprehending the state of the art.
AB - Open-ended educational systems such as games have been broadly under investigation in recent years due to their potential in making learning enjoyable and offering the adaptive pedagogy of intelligent tutoring systems. The most important challenge in building such systems is to predict individual behavior which results in better understanding of the learning process. Model-based methods are a standard way to learn individual behavior in highly-structured systems. However, these methods heavily rely on expert domain knowledge. Since adaptive educational games may create a huge space of actions, applying model-based approaches in these systems are very difficult. In order to counter this difficulty, researchers utilize data-driven methods that are not dependent on expert domain knowledge to learn a subject's behavior based on a history of user interactions. Due to the fact that the potential of applying data-driven approaches in adaptive educational games is still missing, the goal of this report is to cater a survey in the area in order to ease comprehending the state of the art.
KW - data-driven approaches
KW - educational games
KW - user modeling formatting
UR - http://www.scopus.com/inward/record.url?scp=85016230539&partnerID=8YFLogxK
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U2 - 10.1109/ICSITech.2016.7852650
DO - 10.1109/ICSITech.2016.7852650
M3 - Conference contribution
AN - SCOPUS:85016230539
T3 - Proceeding - 2016 2nd International Conference on Science in Information Technology, ICSITech 2016: Information Science for Green Society and Environment
SP - 291
EP - 295
BT - Proceeding - 2016 2nd International Conference on Science in Information Technology, ICSITech 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Science in Information Technology, ICSITech 2016
Y2 - 26 October 2016 through 27 October 2016
ER -