Edward Kim and Alton Sturgis and Zachary Pardos and Kyle Cui and James Hu and Yunzhong Xiao and Boxi Fu and Daniel He and Issac Gonzalez and Alberto L. Sangiovanni-Vincentelli and Sanjit A. Seshia and Björn Hartmann

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2024-16

April 17, 2024

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-16.pdf

Virtual reality (VR) is used to train psychomotor skills for domains both within VR, e.g. games, and beyond VR, e.g. sports and healthcare. Although it is a common practice to employ variations of tasks to train psychomotor skills, how to algorithmically predict psychomotor skill acquisition given the task variations, or a distribution, has not been investigated. To address this problem, we derive and adapt ideas from intelligent tutoring systems (ITS), a sub-field of learning sciences. We formally model and generate task distributions with physical constraints that are designed by instructors using a probabilistic programming language. We investigate the effectiveness of Bayesian knowledge tracing (BKT) from ITS to predict psychomotor skill acquisition. Our algorithm sequentially sample a task from a probabilistic program, generates it in VR, and updates the BKT prediction using the performance of a user on the task. We conduct a between subject study that compares BKT to self-prediction of skill acquisition. Our study shows that the experimental condition outperforms the control, and BKT contributes to much more consistent learning outcomes than self-prediction.

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BibTeX citation:

@techreport{Kim:EECS-2024-16,
    Author= {Kim, Edward and Sturgis, Alton and Pardos, Zachary and Cui, Kyle and Hu, James and Xiao, Yunzhong and Fu, Boxi and He, Daniel and Gonzalez, Issac and Sangiovanni-Vincentelli, Alberto L. and Seshia, Sanjit A. and Hartmann, Björn},
    Title= {Task Distribution Aware Psychomotor Skill Training with Probabilistic Programs and Bayesian Knowledge Tracing in Virtual Reality},
    Year= {2024},
    Month= {Apr},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-16.html},
    Number= {UCB/EECS-2024-16},
    Abstract= {Virtual reality (VR) is used to train psychomotor skills for domains both within VR, e.g. games, and beyond VR, e.g. sports and healthcare. Although it is a common practice to employ variations of tasks to train psychomotor skills, how to algorithmically predict psychomotor skill acquisition given the task variations, or a distribution, has not been investigated. To address this problem, we derive and adapt ideas from intelligent tutoring systems (ITS), a sub-field of learning sciences. We formally model and generate task distributions with physical constraints that are designed by instructors using a probabilistic programming language. We investigate the effectiveness of Bayesian knowledge tracing (BKT) from ITS to predict psychomotor skill acquisition. Our algorithm sequentially sample a task from a probabilistic program, generates it in VR, and updates the BKT prediction using the performance of a user on the task. We conduct a between subject study that compares BKT to self-prediction of skill acquisition. Our study shows that the experimental condition outperforms the control, and BKT contributes to much more consistent learning outcomes than self-prediction.},
}

EndNote citation:

%0 Report
%A Kim, Edward 
%A Sturgis, Alton 
%A Pardos, Zachary 
%A Cui, Kyle 
%A Hu, James 
%A Xiao, Yunzhong 
%A Fu, Boxi 
%A He, Daniel 
%A Gonzalez, Issac 
%A Sangiovanni-Vincentelli, Alberto L. 
%A Seshia, Sanjit A. 
%A Hartmann, Björn 
%T Task Distribution Aware Psychomotor Skill Training with Probabilistic Programs and Bayesian Knowledge Tracing in Virtual Reality
%I EECS Department, University of California, Berkeley
%D 2024
%8 April 17
%@ UCB/EECS-2024-16
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2024/EECS-2024-16.html
%F Kim:EECS-2024-16