TY - GEN
T1 - Modeling students' flow experience through data logs in gamified educational systems
AU - Oliveira, Wilk
AU - Isotani, Seiji
AU - Pastushenko, Olena
AU - Hruska, Tomas
AU - Hamari, Juho
N1 - Funding Information:
The authors would like to thank the grant provided by São Paulo Research Foundation (FAPESP), Project: 2018/07688-1.
Publisher Copyright:
© 2021 IEEE.
jufoid=70613
PY - 2021
Y1 - 2021
N2 - User modeling in gamified educational systems is a contemporary challenge. In particular, modeling the students' flow experience (i.e., challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, total concentration on the task at hand, sense of control, loss of self-consciousness, transformation of time, and autotelic experience) during a gamified system usage is highly challenging. It is because measurement' instruments usually are invasive, removing the users from the flow experience and/or cannot be applied massively (e.g., participant observation, questionnaires or electroencephalogram). We faced this challenge by conducting a data-driven study (N = 23), where we used a robust statistical method (i.e., partial least squares path modeling) to model the students' flow experience, based on their interaction data (e.g., number of mouse clicks) in a gamified educational system. The main results indicate a relationship between the interaction logs and four flow experience dimensions. Our finds contribute to the area of gamified educational systems, through the students' flow experience modeling. Finally, based on our results, we also provided a series of recommendations for future studies.
AB - User modeling in gamified educational systems is a contemporary challenge. In particular, modeling the students' flow experience (i.e., challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, total concentration on the task at hand, sense of control, loss of self-consciousness, transformation of time, and autotelic experience) during a gamified system usage is highly challenging. It is because measurement' instruments usually are invasive, removing the users from the flow experience and/or cannot be applied massively (e.g., participant observation, questionnaires or electroencephalogram). We faced this challenge by conducting a data-driven study (N = 23), where we used a robust statistical method (i.e., partial least squares path modeling) to model the students' flow experience, based on their interaction data (e.g., number of mouse clicks) in a gamified educational system. The main results indicate a relationship between the interaction logs and four flow experience dimensions. Our finds contribute to the area of gamified educational systems, through the students' flow experience modeling. Finally, based on our results, we also provided a series of recommendations for future studies.
KW - Flow experience
KW - Flow Theory
KW - Gamified educational systems
KW - Students'experience
KW - User modeling
U2 - 10.1109/ICALT52272.2021.00037
DO - 10.1109/ICALT52272.2021.00037
M3 - Conference contribution
AN - SCOPUS:85114901222
T3 - IEEE International Conference on Advanced Learning Technologies
SP - 97
EP - 101
BT - Proceedings - IEEE 21st International Conference on Advanced Learning Technologies, ICALT 2021
A2 - Chang, Maiga
A2 - Chen, Nian-Shing
A2 - Sampson, Demetrios G
A2 - Tlili, Ahmed
PB - IEEE
T2 - IEEE International Conference on Advanced Learning Technologies
Y2 - 12 July 2021 through 15 July 2021
ER -