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Title: Real-time individualized training vectors for experiential learning.

Abstract

Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
1010417
Report Number(s):
SAND2011-0166
TRN: US201108%%434
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; EVALUATION; FEEDBACK; LEARNING; DATA; PERFORMANCE; TRAINING; VECTORS

Citation Formats

Willis, Matt, Tucker, Eilish Marie, Raybourn, Elaine Marie, Glickman, Matthew R., and Fabian, Nathan. Real-time individualized training vectors for experiential learning.. United States: N. p., 2011. Web. doi:10.2172/1010417.
Willis, Matt, Tucker, Eilish Marie, Raybourn, Elaine Marie, Glickman, Matthew R., & Fabian, Nathan. Real-time individualized training vectors for experiential learning.. United States. doi:10.2172/1010417.
Willis, Matt, Tucker, Eilish Marie, Raybourn, Elaine Marie, Glickman, Matthew R., and Fabian, Nathan. Sat . "Real-time individualized training vectors for experiential learning.". United States. doi:10.2172/1010417. https://www.osti.gov/servlets/purl/1010417.
@article{osti_1010417,
title = {Real-time individualized training vectors for experiential learning.},
author = {Willis, Matt and Tucker, Eilish Marie and Raybourn, Elaine Marie and Glickman, Matthew R. and Fabian, Nathan},
abstractNote = {Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.},
doi = {10.2172/1010417},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jan 01 00:00:00 EST 2011},
month = {Sat Jan 01 00:00:00 EST 2011}
}

Technical Report:

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