This chapter looks at the effectiveness of commercially available educational computer games. It defines what a game is from game theory and what an intelligent tutoring system is, suggests some concepts from these areas to use for game development, and reflects on some surveys of commercial off-theshelf (COTS) educational software, including computer games. Two effectiveness studies conducted at John Jay High School, and the results of the studies are presented on the educational computer game Math Blaster Algebra. One of the studies showed a positive learning increase from using Math Blaster Algebra. Both studies showed no negative impacts on scores and grades with more time playing the game. With lessons learned from game theory, the intelligent computer-based training field, and these effectiveness studies, educational computer gaming can continue to grow, be effective, and be accepted into educational systems.
The study of games began to be formalized with the mathematical field of game theory (Osborne & Rubinstein, 1994). Players in a game are contenders that can be human, machine, nature, or other entities. The players control some piece of a situation in a game. Games with many players are called n-person or multiplayer. A strategy is a set of rules that a player uses to play the game. A move by a player is given by the player’s strategy. A move determines the next state of the game. Players are contending for various payoffs that are the results or consequences for the players at the end of the game. A player may get a reward or have to give up something.
One way to define a game is by the rules of the game, including the relationships between players, who moves when, what information is available, alternatives available to a player, and the outcomes of each sequence of choices. The extended form of a game from game theory specifies these four things:
Initial state of the game
Admissible moves from one state to another
Terminal or end of the game states
Payoffs to the players at the end of the game
All the possible strategies for a player i are represented by a set, Si. The normal form of a game is a set of all the strategy sets for each player and the payoff functions for each of the players Pi that map a strategy for each of the players to the payoff for the player i:
Key Terms in this Chapter
Computer-Based Training (CBT): A process of teaching and learning that is executed with software applications on a computer; the student is, in effect, trained by the computer.
Artificial Intelligence (AI): A branch of computer science that attempts to have computers and computer programs do things that appear smart or intelligent; knowledge representation and gaming are two areas within AI.
Perfect Information Game: A game where each player is aware of all the actions and consequences of moves of all the other players.
Student Model: A representation of the current state of what a student knows in relation to material that is being presented in an intelligent tutoring system.
Equilibrium Point: A set of strategies and payoffs for a multiplayer game that is the best that each player can do, given the other players keep the same strategy; in a two-person game, it is called a saddle point.
Intelligent Tutoring System (ITS): A computer system that tutors a student on some subject matter by presenting course content based on a model of the student. An ITS typically has four components: interface module, instructional module, expert model, and student model.
Computer Off-the-Shelf (COTS): Computer products or software that are ready-made and available for sale, lease, or license to the general public.
Strategy: The method of play for a player to play a game; the rules by which a player selects the move to make at any given point in a game.
Serious Games: Games used for training, advertising, simulation, or education that are designed to run on personal computers or video game consoles. These games are not only entertaining, but have some kind of learning value associated with them.
Game Tree: A representation of the play of a game where the root or first node is the initial state of a game, the first level or ply is all the possible moves of the first player, and the content of the nodes are the resulting states of the game; the leaf nodes represent when the game is over.
Complete Chapter List
Richard E. Ferdig
Richard E. Ferdig
Aroutis N. Foster, Punya Mishra
Sara de Freitas, Mark Griffiths
Michael A. Evans
James Oliverio, Dennis Beck
Andreas Breiter, Castulus Kolo
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Shree Durga, Kurt Squire
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Carol Luckhardt Redfield, Diane L. Gaither, Neil M. Redfield
Christopher L. James, Vivan H. Wright
Brian Ferry, Lisa Kervin
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Richard T. Cole, Elizabeth Taylor Quilliam
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Martha Garcia-Murillo, Ian MacInnes
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Paul A. Fishwick, Yuna A. Park
Linda van Ryneveld
David William Shaffer
Melissa L. Lewis, René Weber
Joseph C. DiPietro, Erik W. Black
Matthew Thomas Payne
Katrin Becker, James R. Parker
Clint Bowers, Peter A. Smith, Jan Cannon-Bowers
Slava Kalyuga, Jan L. Plass
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P. G. Schrader, Kimberly A. Lawless, Michael McCreery
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Vasa Buraphadeja, Kara Dawson
Edward L. Swing, Douglas A. Gentile, Craig A. Anderson
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Diane Carr, Caroline Pelletier
Yi Mou, Wei Peng
David J. Leonard
Sasha A. Barab, Adam Ingram-Goble, Scott Warren
Wei Qiu, Yong Zhao
Laurie N. Taylor
James Belanich, Karin B. Orvis, Daniel B. Horn, Jennifer L. Solberg
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Scott J. Warren, Mary Jo Dondlinger
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Douglas Williams, Yuxin Ma, Charles Richard, Louise Prejean
Lloyd P. Rieber, Joan M. Davis, Michael J. Matzko, Michael M. Grant
Leanna Madill, Kathy Sanford
Clark Aldrich, Joseph C. DiPietro
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Chee Siang Ang, Panayiotis Zaphiris
Lisa Galarneau, Melanie Zibit
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Renee Hobbs, Jonelle Rowe
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