ABSTRACT
Many of us have had the vision of learners acquiring STEM subject matters by being immersed in motivating learning environments (such as games) that advance learners to new levels of mastery. Concepts in STEM (science, technology, engineering, and mathematics) are complex and difficult, and require learning at deeper levels than merely memorizing facts, rules, and procedures. Learners would ideally be challenged and motivated to improve on mastering complex topics that might not be acquired with traditional training methods. They would spend hundreds of hours in a hunt for a solution to a problem that few have solved, for the sweet spot in a trade-off between two or more factors, or for a resolution to a set of incompatible constraints. This is precisely the vision of progress for training in the 21st century. How can deep learning be achieved in a motivating learning environment? Games provide a good first place to look for answers because well-designed games are motivating and some meta-analyses have reported positive impacts of games on learning (Mayer, 2011; O'Neil & Perez, 2008; Ritterfeld, Cody, & Vorderer, 2009; Shute & Ventura, 2013; Tobias & Fletcher, 2011; Wouters, van Nimwegen, van Oostendorp, & van der Spek, 2013). This chapter explores the prospects of integrating games with intelligent tutoring systems (ITSs). The hope is that there can be learning environments that optimize both motivation through games and deep learning through ITS technologies. Deep learning refers to the acquisition of knowledge, skills, strategies, and reasoning processes at the higher levels of Bloom’s (1956) taxonomy or the Knowledge-Learning-Instruction (KLI) framework (Koedinger, Corbett, & Perfetti, 2012), such as the application of knowledge to new cases, knowledge analysis and synthesis, problem solving, critical thinking, and other difficult cognitive processes. In contrast, shallow learning involves perceptual learning, memorization of explicit material, and mastery of simple rigid procedures. Shallow knowledge may be adequate for near transfer tests of knowledge/skills but not for transfer tests to new situations that have some modicum of complexity.
