Machine Learning Techniques for Analyzing Training Behavior in Serious Gaming

Matthew C. Gombolay, Reed Jensen, Sung-Hyun Son

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 of 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 that allow teachers to visualize the degree to which training objectives are reflected in actual play. 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 machine learning techniques that could assist teachers by automatically predicting player performance, identifying player disengagement, and recommending personalized lesson plans. These methods could potentially provide teachers with insight to assist them in developing better lesson plans and tailored instruction for each individual student.

External IDs:dblp:journals/tciaig/GombolayJS19