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Digital Game-Based Learning: Promise and Pitfalls Presenters Marleigh Norton Project Manager, Massachusetts Institute of Technology Zoo Scene Investigators, Columbus Zoo & Aquarium and MIT David Schaller Principal, eduweb WolfQuest, Minnesota Zoo and NSF Walt Scacchi
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Presenters • Marleigh Norton Project Manager, Massachusetts Institute of Technology Zoo Scene Investigators, Columbus Zoo & Aquarium and MIT • David Schaller Principal, eduweb WolfQuest, Minnesota Zoo and NSF • Walt Scacchi Research Director, Game Culture and Technology Laboratory, University of California Irvine DinoQuest, Discovery Science Center and UCI
Mapping Content to the Game The Mechanics of Learning in a Game Evaluation
Mapping Content to the Game Tactical Iraqi, Tactical Language and Training LLC
Mapping Content to the Game • Promise • Games are good for • Modeling systems • Situational learning • Abstract concepts and facts • Safe space
Mapping Content to the Game • Promise • Games are good for • Modeling systems • Situational learning • Abstract concepts and facts • Safe space • Pitfall • Misconceptions about the system • Not efficient for transmitting information? • Surrender of control
The Mechanics of Learning World of Warcraft, Blizzard Entertainment
The Mechanics of Learning Promise • Learning by doing • Education *is* the gameplay
The Mechanics of Learning Promise • Learning by doing • Education *is* the gameplay Pitfall • Going through the motions • Did they master the content or did they game the system? • Cheating
Evaluation Dance Dance Revolution, Konami
Evaluation Promise • We learn how students learn, what they learn, and why • Establish whether you met your goals
Evaluation Promise • We learn how students learn, what they learn, and why • Establish whether you met your goals Pitfall • How do you evaluate? • What do you do with the evaluation results? • If at first you do not succeed, do you give up?