TY - GEN
T1 - Annotation vs. Virtual tutor
T2 - 18th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2019
AU - Lee, Hyeopwoo
AU - Kim, Hyejin
AU - Monteiro, Diego Vilela
AU - Goh, Youngnoh
AU - Han, Daseong
AU - Liang, Hai Ning
AU - Yang, Hyun Seung
AU - Jung, Jinki
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In this paper we present a comparative study of visual instructions in Immersive Virtual Reality (IVR), i.e., annotation (ANN) that employs 3D texts and objects for instructions and virtual tutor (TUT) that demonstrates a task with a 3D character. The comparison is based on three tasks, maze escape (ME), stretching exercise (SE), and crane manipulation (CM), defined by the types of a unit instruction. We conducted an automated evaluation of user's memory recall performances (recall time, accuracy, and error) by mapping a sequence of user's behaviors and events as a string. Results revealed that ANN group showed significantly more accurate performance (1.3 times) in ME and time performance (1.64 times) in SE than TUT group, while no statistical main difference was found in CM. Interestingly, although ANN showed statistically shorter execution time, the recalling time pattern of TUT group showed a steep convergence after initial trial. The results can be used in the field in terms of informing designers of IVR on what types of visual instruction are best for different task purpose.
AB - In this paper we present a comparative study of visual instructions in Immersive Virtual Reality (IVR), i.e., annotation (ANN) that employs 3D texts and objects for instructions and virtual tutor (TUT) that demonstrates a task with a 3D character. The comparison is based on three tasks, maze escape (ME), stretching exercise (SE), and crane manipulation (CM), defined by the types of a unit instruction. We conducted an automated evaluation of user's memory recall performances (recall time, accuracy, and error) by mapping a sequence of user's behaviors and events as a string. Results revealed that ANN group showed significantly more accurate performance (1.3 times) in ME and time performance (1.64 times) in SE than TUT group, while no statistical main difference was found in CM. Interestingly, although ANN showed statistically shorter execution time, the recalling time pattern of TUT group showed a steep convergence after initial trial. The results can be used in the field in terms of informing designers of IVR on what types of visual instruction are best for different task purpose.
KW - Computer aided instruction
KW - Evaluation
KW - Human-computer interaction
KW - Virtual reality
KW - Visual guidance
UR - https://www.scopus.com/pages/publications/85078265712
U2 - 10.1109/ISMAR.2019.00030
DO - 10.1109/ISMAR.2019.00030
M3 - Conference Proceeding
AN - SCOPUS:85078265712
T3 - Proceedings - 2019 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2019
SP - 318
EP - 327
BT - Proceedings - 2019 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 October 2019 through 18 October 2019
ER -