TY - GEN
T1 - Does Mouse Click Frequency Predict Students' Flow Experience?
AU - Muramatsu, Pedro Kenzo
AU - Oliveira, Wilk
AU - Oyibo, Kiemute
AU - Hamari, Juho
N1 - Publisher Copyright:
© 2023 IEEE Computer Society. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Designing educational systems able to lead students into flow experience is a contemporary challenge, especially given the positive relationship between flow experience and learning. However, an important challenge within the field of learning analytics is evaluating the students' flow experience during the use of educational systems. In general, such evaluation is conducted using invasive methods (e.g., electroencephalogram, and eye trackers) and cannot be massively applied. To face this challenge, following the trend of utilizing behavioral data produced by users to identify their experience when using different types of systems, in our study, we evaluated the applicability of employing one single type of behavior data (i.e., mouse click frequency) as an exclusive metric to model and to predict students' flow experience. By conducting two data-driven studies (N1 = 25 | N2 = 101), we identified that the mouse click frequency on its own is not able to predict the flow experience. Our study contributes to the field of learning analytics confirming that it is not possible to predict students' flow experience only with mouse click frequency and paving the way for new studies that use different behavior data to predict students' flow experience.
AB - Designing educational systems able to lead students into flow experience is a contemporary challenge, especially given the positive relationship between flow experience and learning. However, an important challenge within the field of learning analytics is evaluating the students' flow experience during the use of educational systems. In general, such evaluation is conducted using invasive methods (e.g., electroencephalogram, and eye trackers) and cannot be massively applied. To face this challenge, following the trend of utilizing behavioral data produced by users to identify their experience when using different types of systems, in our study, we evaluated the applicability of employing one single type of behavior data (i.e., mouse click frequency) as an exclusive metric to model and to predict students' flow experience. By conducting two data-driven studies (N1 = 25 | N2 = 101), we identified that the mouse click frequency on its own is not able to predict the flow experience. Our study contributes to the field of learning analytics confirming that it is not possible to predict students' flow experience only with mouse click frequency and paving the way for new studies that use different behavior data to predict students' flow experience.
M3 - Conference contribution
AN - SCOPUS:85152136580
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 1281
EP - 1290
BT - Proceedings of the 56th Annual Hawaii International Conference on System Sciences, HICSS 2023
A2 - Bui, Tung X.
PB - Hawaii International Conference on System Sciences
T2 - Hawaii International Conference on System Sciences
Y2 - 3 January 2023 through 6 January 2023
ER -