TY - GEN
T1 - Evaluating training with cognitive state sensing technology
AU - Craven, Patrick L.
AU - Tremoulet, Patrice D.
AU - Barton, Joyce H.
AU - Tourville, Steven J.
AU - Dahan-Marks, Yaela
PY - 2009
Y1 - 2009
N2 - Five different training techniques (classroom, video, game-based, computer-based, and simulator) were compared using neurophysiological measurements. The best performance was displayed by individuals in the classroom and video conditions. These participants also displayed the lowest levels of cognitive workload and the highest levels of engagement. The poorest performance on the training was exhibited by individuals in the computer-based and game conditions. These participants also displayed the highest levels of cognitive workload, the lowest levels of engagement, and computer-based had the highest levels of drowsiness. As expected, the testing phases of the training had the highest levels of workload. In general, engagement dropped and distraction increased during the training phase when the material was first presented to participants. However, participants who could keep engagement high during this period performed better. This suggests that mental state monitoring during training could help provide a mechanism for alleviating distraction and inattention and boost training efficacy.
AB - Five different training techniques (classroom, video, game-based, computer-based, and simulator) were compared using neurophysiological measurements. The best performance was displayed by individuals in the classroom and video conditions. These participants also displayed the lowest levels of cognitive workload and the highest levels of engagement. The poorest performance on the training was exhibited by individuals in the computer-based and game conditions. These participants also displayed the highest levels of cognitive workload, the lowest levels of engagement, and computer-based had the highest levels of drowsiness. As expected, the testing phases of the training had the highest levels of workload. In general, engagement dropped and distraction increased during the training phase when the material was first presented to participants. However, participants who could keep engagement high during this period performed better. This suggests that mental state monitoring during training could help provide a mechanism for alleviating distraction and inattention and boost training efficacy.
UR - http://www.scopus.com/inward/record.url?scp=77952003484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952003484&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02812-0_67
DO - 10.1007/978-3-642-02812-0_67
M3 - Conference contribution
AN - SCOPUS:77952003484
SN - 364202811X
SN - 9783642028113
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 585
EP - 594
BT - Foundations of Augmented Cognition
T2 - 5th International Conference on Foundations of Augmented Cognition, FAC 2009, Held as Part of HCI International 2009
Y2 - 19 July 2009 through 24 July 2009
ER -