TY - GEN
T1 - Towards automatically detecting whether student is in flow
AU - Lee, Po Ming
AU - Jheng, Sin Yu
AU - Hsiao, Tzu-Chien
PY - 2014
Y1 - 2014
N2 - Csikszentmihalyi's flow theory states the components (e.g., balance between skill and challenge) that lead to an optimal state (referred to as flow state, or under flow experience) of intrinsic motivation and personal experience. Recent research has begun to validate the claims stated by the theory and extend the provided statements to the design of pedagogical interactions. To incorporate the theory in a design, automatic detector of flow is required. However, little attention has been drawn to this filed, and the detection of flow is currently still dominated by using surveys. Hence, within this paper, we present an automated detector which is able to identify the students that are in flow. This detector is developed using a step regression approach, with data collected from college students learning linear algebra from a step-based tutoring system.
AB - Csikszentmihalyi's flow theory states the components (e.g., balance between skill and challenge) that lead to an optimal state (referred to as flow state, or under flow experience) of intrinsic motivation and personal experience. Recent research has begun to validate the claims stated by the theory and extend the provided statements to the design of pedagogical interactions. To incorporate the theory in a design, automatic detector of flow is required. However, little attention has been drawn to this filed, and the detection of flow is currently still dominated by using surveys. Hence, within this paper, we present an automated detector which is able to identify the students that are in flow. This detector is developed using a step regression approach, with data collected from college students learning linear algebra from a step-based tutoring system.
KW - Educational Data Mining
KW - Flow Theory
KW - Intelligent Tutoring System
KW - Student Modeling
UR - http://www.scopus.com/inward/record.url?scp=84958539907&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-07221-0_2
DO - 10.1007/978-3-319-07221-0_2
M3 - Conference contribution
AN - SCOPUS:84958539907
SN - 9783319072203
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 11
EP - 18
BT - Intelligent Tutoring Systems - 12th International Conference, ITS 2014, Proceedings
PB - Springer Verlag
T2 - 12th International Conference on Intelligent Tutoring Systems, ITS 2014
Y2 - 5 June 2014 through 9 June 2014
ER -