A groundswell of research and interest indicates a wide range of benefits of educative games and simulations: why we should build educative games, and what options and frameworks are available for building them with a technical and artistic balance of pedagogy, simulation and game elements. Among the benefits are practice-based development of procedural knowledge, motivational self-directed experiences, inquiry-based trial-and-error learning with immediate feedback on progress, and tangible results (Prensky 2002; Beck and Wade 2004; Gee 2004; Squire 2005). Social theorists add that learning in simulated environments engages participants in new forms of identity, social negotiation, and virtual economies while promoting and practicing skills needed for a knowledge-based, globally-networked society (Galarneau and Zibit 2006; Jones and Bronack 2006). Technical and theoretical entrepreneurs furthermore envision a radical transformation of e-learning from text-based to epistemic experience-based learning with vastly increased value to participants due to automated analysis, personalized feedback, and adaptive artificial intelligence (Aldrich 2005; Becker 2006; Gibson 2006; Stevens 2006; Van Eck 2006; Shaffer 2007). These developments suggest ways that teachers can benefit and education can be improved through games and simulations, including artificial teaching environments.
Efforts to research, design and implement computer-based games and simulations to improve teaching have begun to surface. Classroom Sims, marketed by Aha! Process, Inc. are 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 in the ARC model of motivation, Multiple Intelligences and other theories. SimSchool, developed by me, Bill Halverson and Melanie Zibit is based in the COVE model, which integrates ideas from learning theory, cognitive science, computational neuroscience, complex systems and artificial intelligence. This chapter will compare these and other examples of modeling approaches.
The plan of the chapter begins by outlining the cognitive science framework of learning and presenting a triad of broad instructional philosophies. Following that, the COVE model is outlined, and several alternative agent modeling approaches are briefly described and compared with implications for contributing to the field of simulations for improving teaching. In the second half of the chapter, design considerations are presented for simulating alternative instructional philosophies and methodologies. The chapter concludes by bringing the models of learning and teaching into a broader framework of learning provided by recent cognitive science literature.
For David's complete paper go to:
http://www.blueberry-brain.org/winterchaos/Modeling%20classroom%20cognition%20and%20teaching%20behaviors.htm
