TY - GEN
T1 - Face forward
T2 - 18th International Conference on Artificial Intelligence in Education, AIED 2017
AU - Stewart, Angela
AU - Bosch, Nigel
AU - Chen, Huili
AU - Donnelly, Patrick
AU - D’Mello, Sidney
N1 - Funding Information:
This research was supported by the National Science Foundation (NSF) (DRL 1235958 and IIS 1523091). Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Attention is key to effective learning, but mind wandering, a phenomenon in which attention shifts from task-related processing to task-unrelated thoughts, is pervasive across learning tasks. Therefore, intelligent learning environments should benefit from mechanisms to detect and respond to attentional lapses, such as mind wandering. As a step in this direction, we report the development and validation of the first student-independent facial feature-based mind wandering detector. We collected training data in a lab study where participants self-reported when they caught themselves mind wandering over the course of completing a 32.5 min narrative film comprehension task. We used computer vision techniques to extract facial features and bodily movements from videos. Using supervised learning methods, we were able to detect a mind wandering with an F1 score of.390, which reflected a 31% improvement over a chance model. We discuss how our mind wandering detector can be used to adapt the learning experience, particularly for online learning contexts.
AB - Attention is key to effective learning, but mind wandering, a phenomenon in which attention shifts from task-related processing to task-unrelated thoughts, is pervasive across learning tasks. Therefore, intelligent learning environments should benefit from mechanisms to detect and respond to attentional lapses, such as mind wandering. As a step in this direction, we report the development and validation of the first student-independent facial feature-based mind wandering detector. We collected training data in a lab study where participants self-reported when they caught themselves mind wandering over the course of completing a 32.5 min narrative film comprehension task. We used computer vision techniques to extract facial features and bodily movements from videos. Using supervised learning methods, we were able to detect a mind wandering with an F1 score of.390, which reflected a 31% improvement over a chance model. We discuss how our mind wandering detector can be used to adapt the learning experience, particularly for online learning contexts.
KW - Attention aware interfaces
KW - Mind wandering
UR - http://www.scopus.com/inward/record.url?scp=85022187659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85022187659&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-61425-0_30
DO - 10.1007/978-3-319-61425-0_30
M3 - Conference contribution
AN - SCOPUS:85022187659
SN - 9783319614243
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 359
EP - 370
BT - Artificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
A2 - Andre, Elisabeth
A2 - Hu, Xiangen
A2 - Rodrigo, Ma. Mercedes T.
A2 - du Boulay, Benedict
A2 - Baker, Ryan
PB - Springer
Y2 - 28 June 2017 through 1 July 2017
ER -