How Computer Games Help Children Learn – Chapter 2

Shaffer, D. (2006). How Computer Games Help Children Learn. New York: Palgrave Macmillan.

The Digital Zoo game starts to tie together game qualities with authentic professions (in this case, mechanical engineering). The game is a design simulation with no right answer; goals are realistic tasks (such as build a bridge), and levels are introduced through increasingly difficult requests from clients. Accomplishing each task requires designing, building, and testing various solutions–the basic steps in engineering design–with each solution producing an incremental improvement. Concepts such as center of mass and bracing are learned (and integrated into the students’ deep understanding) during the process.

Students took pre- and post-tests and kept a design notebook which allowed the researchers to investigate numerical and attitude changes; the results were impressive:

  • “players used scientific justifications, on average, five times as often”
  • “design plans became, on average, 55 percent more complex”
  • “Players considered 47 percent more features in making a decision”

Players learned specialized language as part of acting like (and thus learning to think like) an engineer. The language provides labels which improves communication and also highlights what’s important and what’s not.

After describing the research with the game, Shaffer argues that the knowledge gained in the game persists because it’s tied to a particular epistemology. He describes knowledge as more than memory but as the use of symbols which represent knowledge. While words are efficient symbols because they can be written down and studied systematically, computers don’t just store symbols, they process them. While words led to a scientific culture based on symbol storage, computers will lead to as virtual culture based on symbol processing.

Shaffer argues that all games are simulations (microworlds), and that as humans and computers interact in these simulations, players display autoexpressivity. They come to a microworld with a set of beliefs, make decisions based on those beliefs, and and receive responses from the simulation which bring to the surface, challenge, and refine those beliefs. This concept is identical to Wiggins’ idea of eliciting misconceptions as the first step toward deep learning.

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