What would a game or simulation need to have in order to teach a teacher how people learn? This chapter uses a four-part framework of knowledge, learner, assessment, and community (Bransford et al., 2000) to discuss design considerations for building a computational model of learning. A teaching simulation—simSchool—helps illustrate selected psychological, physical, and cognitive models and how intelligence can be represented in software agents. The design discussion includes evolutionary perspectives on artificial intelligence and the role of the conceptual assessment framework (Mislevy et al., 2003) for automating feedback to the simulation user. The purpose of the chapter is to integrate a number of theories into a design framework for a computational model of learning.
The key question of this chapter is, “What would a game or simulation need to have in order to teach a teacher how people learn?” The chapter assumes that it is possible and desirable to create such a computational model for several reasons. First, a groundswell of research indicates a wide range of interesting benefits of educative games and simulations (Prensky, 2002; Beck & Wade, 2004; Gee, 2004; Squire, 2005): why we should build educative games (Galarneau & Zibit, 2006; Jones & Bronack, 2006), and what options and frameworks are available for building them with a technical and artistic balance of pedagogy, simulation, and game elements (Aldrich, 2005; Becker, 2006; Gibson, 2006; Stevens, 2006; Van Eck, 2006). Second, training needs in business, government, industry, and the military are already being addressed by a variety of games and simulations, but few if any efforts are addressing the need for effective training of the instructors. Third, teacher shortages and the lack of adequately prepared teachers who persist in the profession are perennial challenges of K-12 education—a situation that may be improvable through games and simulations. Fourth, teachers who learn by playing games may be more open to the motivational potential of games and more likely to use playful engagement strategies in their teaching.
It is important to combine and to an extent equate digital games and simulations. While there is a difference in emphasis in the two approaches, they are united in that both utilize some kind of application engine that displays an interactive microworld to a user and invites “playing around” within the boundaries of that system (Gibson, 2006). As the user interacts with the application, expertise develops. The subtleties of whether there are clear goals, rewards, and an emotionally charged atmosphere embedded in the interaction (as found in many games) or whether the user sees and acts within a realistic microworld (as found in many simulations) are important considerations, but not essential to the exploration of the characteristics needed to build a game or simulation capable of teaching a teacher about how people learn.
Recent efforts to research, design, and implement games to improve teaching have begun to surface. Classroom Sims, marketed by Aha! Process, Inc. is based on work by Dr. Ruby Payne. Cook School District, by Drs. Gerry and Mark Girod of Western Oregon University, is based in the “Teacher Work Sample Methodology.” simClass, in two versions developed by graduate students of Dr. Youngkyun Baek of the Korea National University of Education, is based on the ARC model of motivation, multiple intelligences, and other theories. simSchool, developed by me, Bill Halverson, and Melanie Zibit, is based on psychological models integrated with ideas from learning theory, cognitive science, computational neuroscience, complex systems, and artificial intelligence. This chapter will use simSchool as an illustration to help make the ideas more concrete.
The characteristics of a game or simulation designed to improve teaching need to take into account four broad arenas of learning theory supported by cognitive science and the research on teaching and learning, outlined in a National Research Council report on the “How People Learn” (HPL) framework (Bransford et al., 2000). The HPL framework elements are:
The characteristics of the learner,
The nature of knowledge,
The role of a community in shaping expertise, and
The role of feedback in shaping performance.
Key Terms in this Chapter
Darwinian Creatures: A concept of evolutionary agency by Daniel Dennett in which creatures evolve by simple mutation, recombination, and selection made by fitness on a landscape that serves as the evaluation function.
Popperian Creatures: A concept of evolutionary agency by Daniel Dennett in which creatures have internal models and can simulate or run the models disengaged from the world.
Game, Simulation: A computer code or application that embodies the rules, boundaries, and relationships of some system.
Activity Theory: A sociocultural historical analytic framework founded on the ideas of Leontyev, Engeström, and others. The framework has six elements: subject, object, artifact, praxis, community, and roles.
Computational Models: Abstract representations for investigating computing machines. Standard computational models assume a discrete time paradigm. A mathematical object representing a question that computers might be able to solve.
Pavlovian Creatures: A concept of evolutionary agency by Daniel Dennett in which creatures have a nervous system, and stimulus-response learning is possible.
Braitenberg Vehicles: A conceptual system of evolutionary agents developed by Victor Braitenberg characterized by neural and motor connections that give rise to locomotion and higher forms of activity in the world.
How People Learn (HPL) Framework: A review of research on “how people learn” produced for the Commission on Behavioral and Social Sciences and Education of the National Research Council, edited by John Bransford, Ann Brown, and Rodney Cocking. The framework has four broad themes, which organize the cognitive science literature: knowledge, learner, community, and assessment.
Gregorian Creatures: A concept of evolutionary agency by Daniel Dennett in which creatures use tools to create a shared base of knowledge or culture.
Agent, Intelligent Agent, Software Agent: A computational representation of embodied thought and action utilizing artificial intelligence. A piece of software that acts for a user or other program with the authority to decide when (and if) action is appropriate.