Brown (2006) discussed game based learning in “New Learning Environments for the 21st Century: Exploring the Edge”. Learning-as-play has a great deal of exciting potential and research on the subject is equally compelling. The concept conjures up visions of irresistibly addictive (and fun) computer games which lead players effortlessly along on a voyage of discovery. At the end of the game, players would have attained learning outcomes which otherwise would have involved arduous difficulty. In this vision, games are brilliantly designed by experts in learning theory, and not only is the learning effective, but the experience is enjoyable at the same time. Brown supports this concept when he says:
It turns out that using excellent pedagogical principles in constructing a game—for instance, requiring that players tackle challenges that get increasingly more demanding, but at just the right pace—makes for great play. So game designers must know how to design good learning environments. (p. 21)
But how many games are really designed with pedagogical principles in mind? How many educational games are designed with fundamental learning theories in mind? Further, how much of the study of the effectiveness of these games and game learning has been rooted in these same fundamental learning theories? This last question is asked in “Investigating the Learning-Theory Foundations of Game-Based Learning: a Meta-Analysis” (Wu, Hsiao, Wu, Lin & Huang, 2011).
Contrary to what one might think from reading Brown (2006), educational computer games are not a terribly new idea. My first exposure to educational games came with Oregon Trail on the Apple II+ in the early 1980’s. Since then, there has been a steady and growing stream of educational software titles. Research on the effectiveness of these games has also been growing. Most of the learning software titles have not been built on learning theory foundations, and what is quite interesting is that much of the research on game based learning has also ignored learning theory (Wu et al., 2011).
Wu et al., (2011) provided a thorough review of foundational learning theories and their representative principles to frame their inquiry into the learning theory foundations of game based learning. Review of this material was helpful and enlightening. Data and results are presented clearly and shortcomings of design, though minor, are described explicitly. These shortcomings are similar to any meta-analysis and include problems associated with aggregating varied research studies. Sample size is admirably large and results are appropriately framed. Surprisingly, of the 658 studies on game based learning reviewed, only 91 were based on learning theories (Wu et al., 2011). This is a remarkably low number, especially given a history of previously published suggestions that future research on game-based learning build on learning theory foundations.
Much of the substance of Wu et al. (2011) is focused on which learning theory a particular study represents and which representative principle within a given learning theory can a given study be attributed to. For example, in the meta-analysis, 91 studies were based on learning theory; 15 were attributed to behaviorism, 17 to cognitivism, 25 to humanism, and 48 to constructivism, and 567 articles failed to use a learning theory foundation. Of the 15 attributed to behaviorism, 9 featured direct instruction, 3 featured programmed instruction, and 3 featured social learning theory. Each category is a representative principle of behaviorism. This aspect of the research was quite interesting. As I would have expected, constructivist theory accounted for the most studies overall at 48. However, I was surprised that 25 studies were attributed to the humanistic theory representative principle of experiential learning. I suspect that some may lump the category of experiential learning from humanistic theory with constructivist theory in many learning theory models as the notion of experiential learning leads one to infer construction of one’s own knowledge from experiences.
The review and breakdown of learning theories and associated representative principles was beneficial in many ways. It helped to clarify to me that the Khan Academy math system is more accurately based on behavioristic learning theory than on cognitive learning theory. Behavioristic learning theory would include “programmed instruction” featuring self-teaching with the aid of teaching machines or software, presenting material sequentially, and allowing students to advance only as they achieve success (Wu et al., 2011). Clearly, this model includes the math mastery system as developed by Khan.
Wu et al. (2011) also discussed trends in game learning research. One of their major findings was that learning theory as a foundation for game based learning inquiry is growing rapidly. Of the 215 studies that occurred between 1971 and 1990, only 6 were based on learning theory. Of the 193 studies that were done between 2007 and 2009, 42 were based on learning theory.
I was originally looking for a paper broadly covering innovation in game learning development. The selected paper did not contain exactly what I was hoping for. Still, Wu et al. (2011) provided a unique perspective on game based learning research. If one had read all 658 papers in their study, one would likely not have come to the same valuable big-picture perspective they achieved. It is not surprising that learning theory has rarely been employed when looking at game based learning, what is encouraging is the trend for the increasing use of learning theory as a foundation for game based learning inquiry. As researchers build a better knowledge base for learning with games, based on a common learning theory foundation, we hope more and more potential for learning-while-playing will be released.
Brown, J. (2006). New learning environments for the 21st century: Exploring the edge. Change, 38(5), 18-24.
Wu, W. H., Hsiao, H. C., Wu, P. L., Lin, C. H., & Huang, S. H. (2011). Investigating the learning-theory foundations of game-based learning: a meta-analysis. Journal of Computer Assisted Learning, 28(3), 265-279. doi: DOI: 10.1111/j.1365-2729.2011.00437.x.