Accession Number : AD1030414


Title :   Machine Learning for Education: Learning to Teach


Descriptive Note : Technical Report


Corporate Author : MASSACHUSETTS INST OF TECH LEXINGTON LEXINGTON United States


Personal Author(s) : Gombolay,Matthew C ; Jensen,Reed ; Son,Sung-Hyun


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1030414.pdf


Report Date : 01 Dec 2016


Pagination or Media Count : 12


Abstract : Training time is a costly, scarce resource across domains such as commercial aviation, healthcare, and military operations. In the context of military applications, serious gaming the training warfighters through immersive, real-time environments rather than traditional classroom lectures offers benefits to improve training not only in its hands-on development and application of knowledge, but also in data analytics via machine learning. In this paper, we explore an array of machine learning techniques and how they can be utilized to improve training. First, we investigate the concept of discovery: learning how warfighters utilize their training tools and develop military strategies within their training environment. Second, we develop methods for improving warfighter education: learning to predict performance, identify player disengagement, and recommend lesson plans.


Descriptors :   computer simulations , education , algorithms , probability , machine learning , artificial neural networks , computational science , artificial intelligence , behaviors , GENERATIVE MODELS


Subject Categories : Cybernetics
      Psychology


Distribution Statement : APPROVED FOR PUBLIC RELEASE