Real-time individualized training vectors for experiential learning.

Willis, Matt ; Tucker, Eilish Marie ; Raybourn, Elaine Marie ; Glickman, Matthew R. ; Fabian, Nathan

Sandia National Laboratories

关键词: Training;    Feedback;    99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Evaluation;    Learning;   

DOI  :  10.2172/1010417
RP-ID  :  SAND2011-0166
RP-ID  :  AC04-94AL85000
RP-ID  :  1010417

美国|英语

来源: UNT Digital Library

【 摘 要 】

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.