the social and behavioral sciences, particularly those who have a background in conducting projects in interdisciplinary research teams—what is needed to design and develop an advanced learning environment. Far too many learning environments are launched without the required empirical testing on usability, engagement, and learning gains. The pace of new technologies hitting the market is so fast that there typically is not enough time to adequately test the systems. Therefore, there is a need for basic research, theoretical models, and tools to forecast the quality of learning environment designs before or during their potential development.
The role of technology in training has had its critics. Cuban (1986, 2001) documented that technology has historically had a negligible impact on improvements in education. Clark (1983) argued that it is the pedagogy underlying a learning environment, not the technology per se, that typically explains learning gains. That conclusion of course suggests that we investigate how particular technologies are aligned with particular pedagogical principles, theories, models, hypotheses, or intuitions. For example, a film clip on how to dismantle an improvised explosive device is not a technology or subject matter naturally aligned with a pedagogical theory that emphasizes active discovery learning. Reading texts on the web about negotiation strategies is not well aligned with a social learning theory that embraces modeling-scaffolding-fading.
It is important to start with a broad perspective on the landscape of learning technologies and learning theories (National Research Council, 2000; O’Neil and Perez, 2003). Any given technology, T, affords a number of cognitive, social, and pedagogical mechanisms, M (Gee, 2003; Kozma, 1994; Norman, 1988). In addition to these TM mappings, it is essential to consider the goals, G, of the learning environment: Is the learning environment designed for quick training on shallow knowledge about an easy topic or for deep learning about explanations of a complex system? It is essential to consider the characteristics of the learner, L, such as high or low knowledge of the subject matter and high or low verbal ability. The resulting TMGL landscape of cells needs to be explored. Some cells are promising conditions for learning, others are impossible, and groups of cells give rise to interesting interactions.
We advocate a long-term research roadmap that identifies an appropriate TMGL landscape for military training and that selects research projects that strategically cover cells that need attention. For example, there has not been enough research on learning gains from serious games that afford active discovery learning in adults with low reading ability. In contrast, there is a wealth of research on learning gains from intelligent tutoring systems on algebra and physics that spans the gamut of learner characteristics, pedagogical mechanisms, and learning goals (Anderson, Corbett, Koedinger, and Pelletier, 1995; Corbett, 2001; VanLehn et al., 2002). There are