Complex Play and Computational Thinking in a Collaborative Board Game.

Matthew Berland · Victor Lee

Wed., June 09, 3:30–4:30, Old Madison

A great deal of interest has been expressed as of late in the complex reasoning that takes place during game play. This interest has developed for a multitude of reasons, including the design features of many modern-day games that foster learning (Gee, 2007; Nasir, 2005; Steinkuehler, 2006). It is largely thought that the ability to foster a sense of immersion is a genuine strength of games that distinguishes them from many other learning contexts (Shelton & Wiley, 2007).

Through observations of students playing ‘designer board games’, Berland & Lee (2010) found that certain games elicit a substantial amount of collaborative computational thinking. Computational thinking has been discussed in detail beneath the larger umbrella of computational literacy (NRC, 2009), but, in this instance, we focus on computational thinking as a name for the ‘cognitive pillar’ of computational literacy (as described by diSessa, 2000); computational thinking encompasses the manners of knowing, thinking, and reasoning using computation as a tool. Computation as tool should not be confused with a computer as a tool: computation here refers to the ability to use tools such as mathematical logic, modeling, algorithms, and engineering design to solve a variety of complex problems. Recent work has tied computational thinking as described to benefits in related learning goals (NRC, 2009).

In this presentation, we consider the complex collaborative play elicited by the board game Pandemic (Leacock, 2007), and evaluate the computational thinking with respect to the target goals described by Wing (2006). To do so, we will present the board game Pandemic and describe and analyze two qualitative examples of computational thinking that we have observed and documented. In our analysis, we will focus on three core aspects of computational thinking: conditional (branching) logic, distributed processing, and algorithmic strategy design. This subset does not approximate the range of cognitive capabilities or processes that are involved in thinking computationally, but it represents some clear connections between board game play and computational thinking.

Our data describe three complete runs (30-60 minutes) of a game of Pandemic played by three groups of three-to-four different undergraduates each. The students had never played Pandemic before, nor had they played any related game. The students were encouraged beforehand to talk freely during the game, which is explicitly encouraged in the accompanying instruction booklet (see Leacock, 2007).

Through this work, we posit that the computational thinking showing in this board game suggests that (a) board games can be engineered and designed as productive learning environments and (b) the reasoning that is rapidly and intuitively developed through board game play can be leveraged for instruction.

References

Berland, M. & Lee, V. (2010). Using Designer Board Games to Understand Distributed Computational Thinking. The annual meeting of the American Educational Research Association (AERA-10).

diSessa, A. (2000). Changing minds: Computers, language and literacy. Cambridge, MA: MIT Press.

Gee, J. P. (2007). What video games have to teach us about learning and literacy. New York: Palgrave Macmillan.

Leacock, M. (2007). Pandemic. Mahopac, NY: Z-Man Games.

Nasir, N. i. S. (2005). Individual cognitive structuring and the spciocultural context: Strategy shifts in the game of Dominoes. Journal of the Learning Sciences, 14(1), 5-34.

NRC (2009). Report on a Workshop on the Scope and Nature of Computational Thinking. Retrieved from http://www.nap.edu/catalog.php?record_id=12840.

Shelton, B. E., & Wiley, D. (2007). The design and use of simluation computer games in education. Rotterdam, The Netherlands: Sense Publishers.

Steinkuehler, C. A. (2006). Why game (culture) studies now? Games and Culture, 1(1), 97-102.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.