TY - GEN
T1 - Affect dynamics in military trainees using vMedic
T2 - 18th International Conference on Artificial Intelligence in Education, AIED 2017
AU - Ocumpaugh, Jaclyn
AU - Andres, Juan Miguel
AU - Baker, Ryan
AU - DeFalco, Jeanine
AU - Paquette, Luc
AU - Rowe, Jonathan
AU - Mott, Bradford
AU - Lester, James
AU - Georgoulas, Vasiliki
AU - Brawner, Keith
AU - Sottilare, Robert
N1 - Funding Information:
This research was supported by grant by the Army Research Lab #W911NF-13-2-0008.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - The role of affect in learning has received increasing attention from AIED researchers seeking to understand how emotion and cognition interact in learning contexts. The dynamics of affect over time have been explored in a variety of research environments, allowing researchers to determine the extent to which common patterns are captured by hypothesized models. This paper present an analysis of affect dynamics among learners using vMedic, which teaches combat medicine protocols as part of the military training at West Point, the United States Military Academy. In doing so, we seek both to broaden the variety of learning contexts being explored in order better understand differences in these patterns and to test the theoretical predictions on the development of affect over time.
AB - The role of affect in learning has received increasing attention from AIED researchers seeking to understand how emotion and cognition interact in learning contexts. The dynamics of affect over time have been explored in a variety of research environments, allowing researchers to determine the extent to which common patterns are captured by hypothesized models. This paper present an analysis of affect dynamics among learners using vMedic, which teaches combat medicine protocols as part of the military training at West Point, the United States Military Academy. In doing so, we seek both to broaden the variety of learning contexts being explored in order better understand differences in these patterns and to test the theoretical predictions on the development of affect over time.
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U2 - 10.1007/978-3-319-61425-0_20
DO - 10.1007/978-3-319-61425-0_20
M3 - Conference contribution
AN - SCOPUS:85022221777
SN - 9783319614243
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 238
EP - 249
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 -