TY - GEN
T1 - Evaluating the Benefits of the Immersive Space to Think
AU - Lisle, Lee
AU - Chen, Xiaoyu
AU - Edward Gitre, J. K.
AU - North, Chris
AU - Bowman, Doug A.
N1 - Publisher Copyright: © 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - Sensemaking with large multimedia dataset is a cognitively intensive task that requires analysts to understand the underlying stories that the dataset tells. Often, analysts use tools in order to offload cognition as well as convey their new understanding of the dataset; however, existing tools are limited by their underlying technologies. We have proposed a novel virtual reality tool to support sensemaking called the Immersive Space to Think (IST). IST can aid the process of analyzing multimedia data, but it remains unproven whether IST improves sensemaking performance over a traditional desktop setting. In a study performed over six weeks, one participant used both IST and traditional methods of sensemaking with a dataset of 100 text documents of transcribed survey responses from World War 2 soldiers to perform historical analysis. The participant was asked guided questions that produced three essays with their understanding of the data. After conducting a blind evaluation of the participant's interpretation of the data, a team of three experts in historical analysis concluded that the essays written with IST displayed a better understanding of the dataset. Furthermore, the participant gave positive feedback on IST, and also suggested possible improvements.
AB - Sensemaking with large multimedia dataset is a cognitively intensive task that requires analysts to understand the underlying stories that the dataset tells. Often, analysts use tools in order to offload cognition as well as convey their new understanding of the dataset; however, existing tools are limited by their underlying technologies. We have proposed a novel virtual reality tool to support sensemaking called the Immersive Space to Think (IST). IST can aid the process of analyzing multimedia data, but it remains unproven whether IST improves sensemaking performance over a traditional desktop setting. In a study performed over six weeks, one participant used both IST and traditional methods of sensemaking with a dataset of 100 text documents of transcribed survey responses from World War 2 soldiers to perform historical analysis. The participant was asked guided questions that produced three essays with their understanding of the data. After conducting a blind evaluation of the participant's interpretation of the data, a team of three experts in historical analysis concluded that the essays written with IST displayed a better understanding of the dataset. Furthermore, the participant gave positive feedback on IST, and also suggested possible improvements.
UR - https://www.scopus.com/pages/publications/85085366483
U2 - 10.1109/VRW50115.2020.00073
DO - 10.1109/VRW50115.2020.00073
M3 - Conference contribution
T3 - Proceedings - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020
SP - 331
EP - 337
BT - Proceedings - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020
Y2 - 22 March 2020 through 26 March 2020
ER -