Toward a real-time model-based training system

Abstract

This article describes the development of a real-time model-based training system that provides adaptive ''over-the-shoulder'' (OTS) instructions to trainees as they learn to perform an Anti-Air Warfare Coordinator (AAWC) task. The long-term goal is to develop a system that will provide real-time instructional materials based on learners' actions, so that eventually the initial set of instructions on a task can be strengthened, complemented, or overridden at different stages of training. The training system is based on the ACT-R architecture, which serves as the theoretical background for the cognitive model that monitors the learning process of the trainee. An experiment was designed to study the impact of OTS instructions on learning. Results showed that while OTS instructions facilitated short-term learning, (a) they took time away from the processing of current information, (b) their effects tended to decay rapidly in initial stages of training, and (c) their effects on training diminished when the OTS instructions were proceduralized in later stages of training. A cognitive model that learned from both the 0953-5438/$ -see front matter Ó (W.-T. Fu). www.elsevier.com/locate/intcomInteracting with Computers 18 (2006) 1215-1241 upfront and OTS instructions was created and provided good fits to the learning and performance data collected from human participants. Our results suggest that to fully capture the symbiotic performance between humans and intelligent training systems, it is important to closely monitor the learning process of the trainee so that instructional interventions can be delivered effectively at different stages of training. We proposed that such a flexible system can be developed based on an adaptive cognitive model that provides real-time predictions on learning and performance.

A Neuroadaptive Cognitive Model for Dealing With Uncertainty in Tracing Pilots' Cognitive State

“…Alternatively, leaner models would need to emulate information search in the face of epistemic uncertainty. A number of methods have been used to reduce epistemic uncertainty in modeling individual behavior in complex tasks, such as pre‐test scores as predictors (Rehling et al, 2004), model‐tracing (Fu et al, 2006), inserting physiological data on user's workload into the model (Putze, Schultz, & Propper, 2015), and dynamic adjustment of parameters with pre‐computed lookup tables (Fisher, Walsh, Blaha, Gunzelmann, & Veksler, 2016).…”

“…Just as intelligent training systems (Fu et al, 2006), cognitive assistance on the flight deck can be designed in reference to models based on a cognitive architecture. Consequences of an overheard or ignored message for pilots' performance can be anticipated with the help of a cognitive pilot model.…”

“…In the present paper, a modeling concept is proposed that is able to explain uncertainty in single instances of missed alerts by representing individual pilots' behavior. In the fashion of Putze et al (2015), we extend the idea of model‐tracing (Fu et al, 2006) by incorporating physiological data. Whereas Putze et al (2015) integrate physiological data to model architectural differences, that is, occupying cognitive resources with a dummy model to model workload, the concept in this paper focuses on modeling knowledge differences (Taatgen, 1999) due to unprocessed auditory messages.…”

Applied cognition and training research to address emerging military requirements

“…Despite the fact that there have been interesting successes in this area (cf. [8]), there are a host of issues such as display design [9], timing and content of feedback [10], and trust [11] which are far from solved.…”

A neurocognitive model for predicting the fate of individual memories

“…A number of approaches in cognitive science and education attempt to use computer models to algorithmically tailor information presentation during study to the needs of individual learners (Smallwood, 1962;Atkinson, 1972;Fu et al, 2006;Ritter, Anderson, Koedinger, & Corbett, 2007;Pavlik & Anderson, 2008;Lindsey, Mozer, Cepeda, & Pashler, 2009;Rafferty, LaMar, & Griffiths, 2015). A core goal of these approaches is to leverage insights about the dynamics of learning and memory in order to predict which materials are likely to be forgotten and which will be remembered at future points in time.…”