TY - GEN
T1 - Confidence Estimation Using Machine Learning in Immersive Learning Environments
AU - Tao, Yudong
AU - Coltey, Erik
AU - Wang, Tianyi
AU - Alonso, Miguel
AU - Shyu, Mei Ling
AU - Chen, Shu Ching
AU - Alhaffar, Hadi
AU - Elias, Albert
AU - Bogosian, Biayna
AU - Vassigh, Shahin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - As the development of Virtual Reality and Augmented Reality (VR/AR) technology rapidly advances, learning in an artificial immersive environment becomes increasingly feasible. Such emerging technology not only facilitates and promotes an efficient learning process, but also reduces the cost of access to learning materials and environments. Current research mainly focuses on the development of immersive learning environments and the adaptive learning methods based on interactions between trainees and the environment. However, valuable human biometric data available in immersive environments, such as eye gaze and controller pose, have not been explored and utilized to help understand the affective state of the trainees. In this paper, we propose a machine-learning based research framework to estimate trainees' confidence about their decisions in immersive learning environments. Using this framework, we designed an experiment to collect biometric data from a multiple-choice question and answer session in an immersive learning environment. This includes collecting answers from 10 participants on 35 questions and their self-reported confidence in their answers. A Long Short-Term Memory neural network model was used to analyze the data and estimate the confidence with 85.6% accuracy.
AB - As the development of Virtual Reality and Augmented Reality (VR/AR) technology rapidly advances, learning in an artificial immersive environment becomes increasingly feasible. Such emerging technology not only facilitates and promotes an efficient learning process, but also reduces the cost of access to learning materials and environments. Current research mainly focuses on the development of immersive learning environments and the adaptive learning methods based on interactions between trainees and the environment. However, valuable human biometric data available in immersive environments, such as eye gaze and controller pose, have not been explored and utilized to help understand the affective state of the trainees. In this paper, we propose a machine-learning based research framework to estimate trainees' confidence about their decisions in immersive learning environments. Using this framework, we designed an experiment to collect biometric data from a multiple-choice question and answer session in an immersive learning environment. This includes collecting answers from 10 participants on 35 questions and their self-reported confidence in their answers. A Long Short-Term Memory neural network model was used to analyze the data and estimate the confidence with 85.6% accuracy.
KW - confidence estimation
KW - deep neural network
KW - immersive environment
KW - immersive learning
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85092135997&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092135997&partnerID=8YFLogxK
U2 - 10.1109/MIPR49039.2020.00058
DO - 10.1109/MIPR49039.2020.00058
M3 - Conference contribution
AN - SCOPUS:85092135997
T3 - Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
SP - 247
EP - 252
BT - Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020
Y2 - 6 August 2020 through 8 August 2020
ER -