Juan Carlos Sanchez-Lozano


Juan Carlos Sanchez-Lozano is a Ph.D. student in the Educational Technology program at Concordia University in Montreal, Canada. He is also a Flash developer and instructor. After working for a leading international consulting firm, he decided to apply his experience in the software and aerospace fields to training. His research explores the role of digital games in the acquisition of advanced competencies normally required in professional settings. Combining research on educational technology, cognitive science and artificial intelligence, he is currently in the process of incorporating these elements into an intelligent game/learning environment for software skills training.



Informing the design of instructional games: A conversation between the fields of education, gaming and artificial intelligence.

Instructional designers have produced computer games and simulations in order to create more engaging content, among other reasons, and use a more constructivist approach to instruction. However, on the instructional side, in many instances this has been done without guidelines derived from any research-based framework. In spite of more than four decades of research on educational games and simulations, there is yet little empirically based agreement regarding their constituting elements, their purpose, effects or methods of evaluation (Bredemeier & Greenblat, 1981; Feinstein & Cannon, 2002; Garris & Ahlers, 2002). It is also common to disregard the gaming aspect of the projects, leading hard-core gamers and many individuals to consider many instructional games boring as compared to their commercial counterparts. Combined, these issues result in environments that fail to achieve their instructional purpose, or provide a good game experience, or both.

In addition to the lack of communication between the educational and gaming fields, the underlying computer structures used to implement the game are seldom considered in the educational literature (Laurillard, 2002). An informal review of the Simulations and Gaming journal reveals that in the past ten years, there have only been a few articles that superficially address this issue, or exclusively focus on system dynamic models (Barbeau & Dececchi, 1997; Dasgupta, 2003; Lin & Sun, 2003; Mohapatra & Saha, 1996; Pillutla, 2003). Assuming that system dynamic models readily translate into games overlooks the fact that ‘the bare mechanics of the game do not determine its semantic freight’ (Koster, 2004). Meadows mentions that most instructional game designers do not keep a record of their models and assumptions, so it is impossible to built previously created games or modify the code, even with the help of the original designer, reflecting the lack of a systematic approach to the process (Meadows, 2001). It is suggested that a third domain has to be considered in the conversation: artificial intelligence (AI).

We propose that the game has to reflect in a unique manner the declarative and procedural knowledge of the particular domain that is being considered. The point of interface will be the computer system: knowledge has to be represented in such a way that can be translated into a computer programming language (Crawford, 2005) without losing the qualities and relationships that characterize it (Scandura, 2001). It also implies that, instead of simply plugging in an already existing game, the game emerges from the designer’s understanding of game genres, as well as from the way knowledge has been represented and translated into a computer system. This agreement between the three components, gaming, educational, and computational, might be relatively easier in cases where existing game genres nicely match the requirements of the domain, as in the case of military simulations. It is less straightforward when the domain is more abstract or when existing genres may not be a suitable counterpart. This workshop seeks to introduce current research on how to better align the three components mentioned above, as well as the application and limitations of this strategy in the acquisition of software programming skills.

Barbeau, L., & Dececchi, T. (1997). A framework for the object-oriented design and simulation of information system dynamics. Simulation & Gaming, 28(1), 44-64.
Bredemeier, M., & Greenblat, C. (1981). The Educational Effectiveness of Simulation Games: A Synthesis of Findings. Simulation & Games, 12(3), 307-332.
Crawford, C. (2005). Chris Crawford on Interactive Storytelling. Berkeley, CA: New Riders.
Dasgupta, S. (2003). The role of controlled and dynamic process environments in group decision making: An exploratory study. Simulation & Gaming, 34(1), 54-68.
Feinstein, A., & Cannon, H. (2002). Constructs of Simulation Evaluation. Simulation & Gaming, 33(4), 425-440.
Garris, R., & Ahlers, R. (2002). Games, Motivation, and Learning: A Research and Practice Model. Simulation &Gaming, 33(4), 441-467.
Koster, R. (2004). Theory of Fun for Game Design: Paraglyph.
Laurillard, D. (2002). Rethinking University Teaching: A Conversational Framework for the Effective Use of Learning Technologies (2nd. ed.). London: RoutledgeFalmer.
Lin, H., & Sun, C. (2003). Problems in simulating social reality: Observations on a MUD construction. Simulation & Gaming, 34(1), 69-88.
Meadows, D. (2001). Tools for Understanding the Limits to Growth: Comparing a Simulation and a Game. Simulation & Gaming, 32(4).
Mohapatra, P., & Saha, B. (1996). A system-dynamics-based game for new product growth. Simulation & Gaming, 27(2), 238-260.
Pillutla, S. (2003). Creating a Web-based simulation gaming exercise using PERL and JavaScript. Simulation & Gaming, 34(1), 112-130.
Scandura, J. (2001). Structural Learning Theory: Current Status and Perspectives. Instructional Science, 29, 311-336.

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