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Instrumenting a Perceptual Training Environment to Support Dynamic Tailoring
Abstract
Simulation-based practice environments would be more valuable for learning if they supported adaptive, targeted responses to students as they proceed thru the experiences afforded by the environment. However, many adaptation strategies require a richer interpretation of the student’s actions and attitudes than is available thru the typical simulation interface. Further, creating extended interfaces for a single application solely to support adaptation is often cost-prohibitive. In response, we are developing “learner instrumentation middleware” that seeks to provide a generalized representation of learner state via reusable algorithms, design patterns, and software.
The Combat Hunter program was first conceived in 2007 to meet a training gap in small unit close combat warfighting. This US Marine Corps (USMC) program of instruction (POI) trains the fundamentals of combat profiling, tracking, and optics-based observation, helping students become successful "combat hunters" in an irregular warfare battlespace. The two lead instructors, who also designed the original course, include a former undercover inner-city police detective and a man-tracker from Africa. Both possess extraordinary innate expertise, each with over 30-years experience in their respective fields. However, the Combat Hunter POI is not currently available outside of the USMC, and the USMC's 10-day course actually represents a shortened version of what each instructor originally desired. Ideally, they would prefer a 20-day POI that includes 10 days of tracking instruction and 10 days of observation/profiling instruction. Thus, a hybrid Combat Hunter-like POI is currently being developed as a capability for the Joint community. The first step in its development was a 20-day course delivered this April by the two subject matter experts (SME) to 43 military and law enforcement trainees. A team of researchers was also embedded with the course. The researchers' goals were to (1) capture the content of the course and package it for greater deployability, (2) assess the instructional outcomes of the course, and (3) explicitly articulate the linkages between the course content and underlying scientific principles.
Acquiring intercultural competence is challenging. Although intelligent learning environments developed to enhance cultural learning can be effective, there is limited evidence regarding how best to dynamically manipulate these environments in support of learning. Further, the space of potential manipulations is extremely large and sometimes tangled with the implementation details of particular learning systems in particular domains. This paper offers a framework for organizing approaches to such dynamic tailoring of the learning experience. The framework is hypothesized to be useful as a general tool for the community to organize and present alternative approaches to tailoring. To highlight the use of the framework, we examine one potential tailoring option in detail in the context of an existing simulation for learning intercultural competence.
We propose a novel approach to intelligent tutoring in which feedback messages are associated with constraints on correct problem solution. The knowledge state of the student is represented by the constraints that he or she does and does not violate during problem solving. Constraint-based tutoring has been implemented in SQL-Tutor, an intelligent tutoring system for teaching the database query language SQL. Empirical evaluation shows that (a) students find the system easy to use, and (b) they do better on a subsequent classroom examination than peers without experience with the system. Furthermore, learning curves are smooth when plotted in terms of individual constraints, supporting the psychological appropriateness of the constraint construct.
Pedagogical agents usually play the role of tutor (Johnson, Rickel, & Lester, 2000) or peer (Y. Kim & Baylor, 2006) in virtual learning environments. In these roles, the agent works alongside the learner to solve problems, ask questions, hold conversations, and provide guidance. Over the last decade or so, a new breed of pedagogical agents has emerged that do not play the role of expert or peer, however, but rather act as the object of practice. That is, instead of helping on the side, it is the interaction itself with the virtual character that is intended to have educational value. Here, the agent is usually a virtual human playing a defined social role in an interaction that requires the learner to use specific communicative skills to achieve some goal. For example, to prepare for an international business trip, one might meet with a virtual foreign business partner from the country of interest to negotiate a fictional contract agreement. The technology challenge is to simulate social encounters in realistic ways and in authentic contexts. The pedagogical challenge is to design scenarios in ways that achieve the learning goals, maintain a high level of accuracy, and stay within an ideal window of challenge (whatever that may be). The basic problems of doing this with virtual humans are eloquently stated by Gratch and Marsella (2005): These “virtual humans” must (more or less faithfully) exhibit the behaviors and characteristics of their role, they must (more or less directly) facilitate the desired learning, and current technology (more or less successfully) must support these demands. The design of these systems is essentially a compromise, with little theoretical or empirical guidance on the impact of these compromises on pedagogy. (p. 256)
INTRODUCTION The Soar architecture was created to explore the requirements for general intelligence and to demonstrate general intelligent behavior (Laird, Newell, & Rosenbloom, 1987; Laird & Rosenbloom, 1995; Newell, 1990). As a platform for developing intelligent systems, Soar has been used across a wide spectrum of domains and applications, including expert systems (Rosenbloom, Laird, McDermott, Newell, & Orciuch, 1985;Washington & Rosenbloom, 1993), intelligent control (Laird, Yager, Hucka, & Tuck, 1991; Pearson, Huffman, Willis, Laird,& Jones, 1993), natural language (Lehman, Dyke, & Rubinoff, 1995; Lehman, Lewis, & Newell, 1998), and executable models of human behavior for simulation systems (Jones et al., 1999;Wray, Laird, Nuxoll, Stokes, & Kerfoot, 2004). Soar is also used to explore the integration of learning and performance, including concept learning in conjunction with performance (Chong & Wray, to appear; Miller & Laird, 1996), learning by instruction (Huffman & Laird, 1995), learning to correct errors in performance knowledge (Pearson & Laird, 1998), and episodic learning (Altmann & John, 1999; Nuxoll & Laird, 2004). This chapter will introduce Soar as a platform for the development of intelligent systems (see also Chapters 2 and 4). Soar can be viewed as a theory of general intelligence, as a theory of human cognition, as an agent architecture, and as a programming language. This chapter reviews the theory underlying Soar but considers Soar primarily as an agent architecture. The architecture point-of-view is useful because Soar integrates a number of different algorithms common in artificial intelligence, demonstrating how they can be used together to achieve general intelligent behaviour. © Cambridge University Press 2006 and Cambridge University Press, 2009.
The United States military’s strategic position is evolving, and as a result, the Services are emphasizing the importance of sociocultural pattern recognition, sensemaking in ambiguous urban contexts, and understanding of patterns of life. In fact, military personnel at increasingly lower echelons are expected to possess these nuanced psychosocial perception and decision-making skills. To facilitate training of these complex competencies, the authors are developing a Virtual Observation Platform, an immersive virtual environment designed to adaptively train US Marine Corps personnel in sustained observation, sociocultural pattern recognition, anomaly detection, and other perceptual–cognitive skills. This paper briefly describes the purpose of the system and then covers its adaptive instructional tailoring in detail. The Platform’s adaptive features include information quality/quantity manipulation and instructional scaffolding in the form of communications from a virtual squad (i.e., peers) that are intrinsic to the narrative of the scenario.
"Situated tutors" combine intelligent, adaptive instructional technology with a simulated environment that allows trainees to explore the context, knowledge, applications, and social interactions inherent in the real-world equivalent. However, the situated tutor construct is, as yet, only superficially described. Thus, this paper seeks to add to the academic conceptualization of situated tutors by clearly defining these systems and their features. We go on to define "advanced situated tutors" as the most robust class of situated tutors, and then give examples of such systems.
ITS + SBT: A Review of Operational Situated Tutors. Military Psychology, special issue on current trends in adaptive training for military application
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