Making a Case for Machine Perception of Trainee Affect to Aid Learning and Performance in Embedded Virtual Simulations.

摘要

For our purposes, machine perception is defined as the ability of a computer-based training system to sense the behavior and affective state (e.g. mood or emotions) of trainees and interpret whether they are engaged, bored, frustrated, confused or even hostile during the training process. This paper puts forward the notion that the maturation of machine perception of trainee affect is critically important to optimizing learning for individuals and teams in embedded virtual simulations and other isolated training environments. Embedded training applications within operational platforms (e.g. tanks, aircraft, ships and individual Warfighting systems) continue to be explored today in many NATO countries (e.g. United States, Germany and the Netherlands). The lack of human tutors within operational platforms limits the understanding of each trainee's affective state and the completeness of the trainee model, the representation of the trainee's state within intelligent tutoring systems. Tutor technology is currently not sufficiently mature to provide accurate, portable, affordable, passive and effective sensing and interpretation of the trainee's affective state and limits the adaptability and effectiveness of the instruction in today's embedded training systems. This paper rationalizes the need for machine perception of affect in future embedded virtual simulations.