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Collaborative Strategic Board Games as a Site for Distributed Computational Thinking

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This paper examines the idea that contemporary strategic board games represent an informal, interactional context in which complex computational thinking takes place. When games are collaborative - That is, a game requires that players work in joint pursuit of a shared goal - The computational thinking is easily observed as distributed across several participants. This raises the possibility that a focus on such board games are profitable for those who wish to understand computational thinking and learning in situ. This paper introduces a coding scheme, applies it to the recorded discourse of three groups of game players, and provides qualitative examples of computational thinking that are observed and documented in Pandemic. The primary contributions of this work are the description of and evidence that complex computational thinking can develop spontaneously during board game play.
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Utah State University
DigitalCommons@USU
ITLS Faculty Publications Instructional Technology & Learning Sciences,
Department of
4-1-2011
Collaborative strategic board games as a site for
distributed computational thinking
Mahew Berland
University of Texas at San Antonio
Victor R. Lee
Utah State University
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Recommended Citation
Berland, M., & Lee, V. R. (2011). Collaborative strategic board games as a site for distributed computational thinking. International
Journal of Game-Based Learning, 1(2), 65-81. doi: 10.4018/ijgbl.2011040105
This paper appeared in a special issue of the International Journal of Game-Based
Learning edited by Patrick Felicia and Guest Editor Sean C. Duncan, Copyright
2011, IGI Global, www.igi-global.com. Posted by permission of the publisher.
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April-June 2011, Vol. 1, No. 2
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
 guesteditorialPreface
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
 SeanC.Duncan,MiamiUniversity,USA
 researcharticles

 
 ChristopherL.Holden,UniversityofNewMexico,USA
 JulieM.Sykes,UniversityofNewMexico,USA

 ColleenMacklin,ParsonstheNewSchoolforDesign,USA
TheSims2

 ElizabethKing,UniversityofWisconsin-Madison,USA
 
 
 RebeccaW.Black,UniversityofCalifornia,Irvine,USA
 StephanieM.Reich,UniversityofCalifornia,Irvine,USA
 
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 MahewBerland,UniversityofTexasatSanAntonio,USA
 VictorR.Lee,UtahStateUniversity,USA
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 
 ElizabethEllcessor,UniversityofWisconsin-Madison,USA
 SeanC.Duncan,MiamiUniversity,USA
Table of Contents
InternatIonal Journal of
Game-Based learnInG
International Journal of Game-Based Learning, 1(2), 65-81, April-June 2011 65
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global
is prohibited.
Keywords: Board Games, Collaboration, Computational Thinking, Computer Science Education,
Designer Games, Group Processes, Multiplayer Games, Tabletop Games, Teams

A great deal of interest has been expressed as of
late in the complex reasoning that takes place
during gameplay. This interest has developed
for a multitude of reasons, including the inherent
motivational aspects of gameplay, the aware-
ness that there are millions of people across
the country who are actively participating in
games and gaming communities, and the extant
design features of many modern-day games
that foster learning (Gee, 2007; Nasir, 2005;



Matthew Berland, University of Texas at San Antonio, USA
Victor R. Lee, Utah State University, USA

This paper examines the idea that contemporary strategic board games represent an informal, interactional
context in which complex computational thinking takes place. When games are collaborative – that is, a game
requires that players work in joint pursuit of a shared goal – the computational thinking is easily observed
as distributed across several participants. This raises the possibility that a focus on such board games are
protable for those who wish to understand computational thinking and learning in situ. This paper intro-
duces a coding scheme, applies it to the recorded discourse of three groups of game players, and provides
qualitative examples of computational thinking that are observed and documented in Pandemic. The primary
contributions of this work are the description of and evidence that complex computational thinking can develop
spontaneously during board game play.
Steinkuehler, 2006). Often, these benefits are
associated with video games and other highly
interactive computational media. It is largely
thought that the ability to foster a sense of im-
mersion is a genuine strength of video games that
distinguishes them from many other learning
contexts (Shelton & Wiley, 2007).
Still, there are reasons to suspect that some
of the generative potential of games is not re-
stricted to those that take place on a computer
platform. At their most base level, games are
systems of rules in which players operate on
representations. In a computer game, those rules
are generally executed and strictly enforced by
the game itself. Board games and other table-
DOI: 10.4018/ijgbl.2011040105
66 International Journal of Game-Based Learning, 1(2), 65-81, April-June 2011
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global
is prohibited.
top games, on the other hand, have no such
inherent game rule management; it becomes
incumbent upon the players themselves to
know and execute the rules of the game. The
players are doing the computation that would
normally be the purview of the computer or
console in a video game.
We consider a new genre of board games,
of which Pandemic (Leacock, 2007) is an in-
stance, to be an especially interesting context.
These board games, which we often refer to
as ‘strategic’ board games, involve complex
coordinated play and highly motivating con-
texts. This class of games has also been referred
to as German-style games, Eurogames, and
designer games.1
In this paper, we show how this family
of strategic board games can prompt novice
game players to engage in relatively complex
computational thinking. Through the gameplay
we observed, players came to understand,
‘debugged’, and created global rules to guide
their play of the game and their development of
strategies. They did so in a socially distributed
way; the players created rules together, they
helped each other understand those rules, and
they collaboratively built complex logics. To
investigate this computational thinking, we
created and deployed a coding framework for
distributed computational thinking, which we
present with examples.
In this study we recruited 3 groups of 3-4
college-age novice players. Each group played
the selected board game Pandemic, (Leacock,
2007), at least once, and we video-recorded
their gaming sessions. For this paper, we focus
strictly on the first gaming session for these
groups. We present three sources of evidence
for the students’ computational thinking: 1)
quantitative analysis of the makeup of the
students’ computational thinking; 2) quantita-
tive analysis of code counts for instances of
‘global’ and ‘local’ computational thinking;
and 3) some descriptive examples of compu-
tational thinking.
Our work complements earlier findings
with pen-and-paper role-playing games (Fine,
1983), in which players were found to do sig-
nificant mathematics in order to play strategy
games. Our data suggest that this claim can
be made stronger – players are doing more
than simple math, they are doing computation.


This particular paper is guided by a mutually
shared interest by the authors to understand the
nature and development of computational think-
ing. Given the increasing role that computation
plays in teaching and learning (Borgman et al.,
2008), understanding how people both interact
with computation and learn to think through the
language of computation has become an area
of interest for education and media researchers
(National Research Council, 2010). Compu-
tational thinking has been discussed in detail
beneath the larger umbrella of computational
literacy (diSessa, 2000), the broad suite of
practices associated with using computational
media in our everyday and professional lives.
Of specific interest to us here and most relevant
to the study of computational thinking is what
diSessa describes as the ‘cognitive pillar’ of
literacy – how to use computation to think
through hard problems. Papert (1980) calls
this type of thinking ‘procedural thinking’ and
his work focuses on students’ problem solving
with programmatic representations and symbol
systems. According to the National Research
Council (NRC) (2009), computational think-
ing is roughly defined as using the methods,
language, and systems of computer science to
understand a wide variety of topics. This can
range from creating computational models of
scientific phenomena to creating algorithms to
plan one’s day more efficiently. Board games
are a relatively closed set of representational
resources that are organized in a coherent, rule-
like manner; as such, they are amenable to this
kind of inspection.
We do wish to note that we are not the first
to attempt to understand computational think-
ing in material and rule-based contexts. This
endeavor has been undertaken in a number of
International Journal of Game-Based Learning, 1(2), 65-81, April-June 2011 67
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global
is prohibited.
contexts, particularly with computer science
education (Koedinger, 2001), and as far back at
Newell and Simon’s (1972) study of cognition
in chess. Our contribution through this work is
description of and evidence that complex com-
putational thinking can happen spontaneously
using non-traditional, non-computational media
like strategic board games. In the discussion
section, we tie the computational thinking more
closely with the design of the game; we believe
that further work will show that the reasoning
that is developed through board game play can
be leveraged for instruction through further
design work and future research.
In our analysis, we will focus on five core
aspects of procedural computational thinking
that have been identified by Wing (2006). These
include: conditional logic, distributed process-
ing, debugging, simulation, and algorithm
building. This subset does not encompass the
full range of cognitive capabilities or processes
that are involved in thinking computationally.
However, this selection represents a modest start
on our endeavor and also shows some of the
clearest overlap between board game thinking
and computational thinking.
Pandemic

Board game play is a recreational activity
common among groups of friends and family
members and can involve very different sets of
rules and playing styles. For example, one can
classify the playing style of a board game as
being competitive, cooperative, or collaborative
(Zagal, Rick, & Hsi, 2006). Interest in board
games appears to have experienced a recent
resurgence (Kleinfeld, 2009) for example,
the web site ‘boardgamegeek.com’ has seen
membership consistently increase over a period
of 7 years from 929 accounts in 2002 to 240,623
accounts in 2009 (from S. Alden, co-creator of
boardgamegeek.com, Personal Communica-
tion, March 25, 2009). This renewed interest
in board games has even led some to advocate
them as resources to include in public library
collections for educational purposes because
they can tie into content learning or information
literacy standards (Nicholson, 2008).
The increased interest in board games
comes in part from a growth of German-
influenced strategic board games in which re-
source management, short play times, elaborate
themes, decreased reliance on chance, sustained
participation of all players (e.g., no one is
eliminated), and incentives to interact directly
with peers are all designed into the game. The
game we have studied in this work, Pandemic
(Leacock, 2007) is one such board game. It is a
collaborative game, similar to those studied by
Zagal et al. (2006), in which 2-4 players share
a common goal and either collectively win or
lose the game. The cover story for the game is
that there are four highly infectious diseases
(designated by the colors red, yellow, black,
and blue) that simultaneously appear and are
spreading across the world. The players must
combat the spread of the disease by moving
player tokens to various cities and treating the
infected populations while also gathering and
exchanging ‘information’ (i.e., cards) that will
lead to cures and/or vaccines for all the diseases.
During each turn of play, a player makes
decisions about where in the world to travel (e.g.,
which lines to follow on Figure 1), whether or
not to treat diseases (signified by wooden blocks
placed on the cities) or if she should focus on
other strategic decisions (e.g., knowledge shar-
ing between players, passing her turn to enable
a future action). Each player is limited in the
number of actions they can take on their turn.
Obstacles exist throughout the game in which
disease spread begins to accelerate and penalties
exist for delaying disease treatment, which may
ultimately cause the players to lose the game.
For example, one additional game obstacle is
the presence of “Epidemic” cards, in which a
drawn card will dictate that a disease should
appear at full strength in a previously uninfected
city. The precise rules are communicated in an
8-page guidebook that specifies rules, justifica-
tions for some of those rules, and even a sample
turn for the game. Many of the same rules and
procedures are also written on the game board
68 International Journal of Game-Based Learning, 1(2), 65-81, April-June 2011
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global
is prohibited.
itself, on rule cards that each player has for
reference, and on individual playable cards that
enable a specific sequence of events.
Players must coordinate moves and care-
fully utilize their resources to both prevent
disease outbreaks (undesired spreading of
disease to neighboring cities) and to discover
cures. Cures are obtained by matching five
cards of the same color at one of several loca-
tions on the board. The game relies on two
decks of cards, one “player deck” which pro-
vides resources for a cure and another “disease
deck” that results in new infections to different
cities. Players are encouraged to speak to one
another. Each turn allows a player to take up
to four actions, which may involve activities
such as movement, disease removal, card ex-
changes. The planning and decision-making
with respect to which cards to use and where
to move player tokens requires players to follow
several logic chains, and the coordination among
players to achieve a unified goal encourages
parallel processing between players.


Any observation of Pandemic gameplay would
reveal that there is a great deal of complex
reasoning and inference that is taking place
among the players. However, any effort to tie
player behaviors to computational thinking is
complicated by the lack of a concrete defini-
tion of computational thinking. Computational
thinking has been the focus of several recent
papers, studies, and reports (Haberman & Yehe-
zkel, 2008; Sieg, 2007). Many of papers in this
field have attempted to independently define it,
and, as such, the construct is defined variously.
Little of this recent work has satisfactorily
operationalized computational thinking nor pro-
vided clear guidance on what we may identify
in real interactions as computational thinking.
As such, we created a working definition of
computational thinking, drawing mostly from
Wing (2006), Papert’s (1980) concept of proce-
dural thinking and the recent National Research
Council’s (2010)Report from a workshop on the
nature and scope of computational thinking.
Our approach is empirically based, in that
the data that we had motivated the categories
that we created. We distilled computational
thinking into a few categories and two stages
that we thought might be relevant to our research
topic. We did not expect to see the full breadth
and width of what others may consider compu-
tational thinking; trying to operationalize all of
computational thinking is outside the scope of
this paper. The five categories we considered
for this work included: conditional logic, al-
gorithm building, debugging, simulation, and
Figure 1. Approximation of the game board from Pandemic (Leacock, 2007). Each circle rep-
resents a major world city
International Journal of Game-Based Learning, 1(2), 65-81, April-June 2011 69
Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global
is prohibited.
distributed computation. Our two stages are
local logic and global logic.
These categories are imperfect (at best),
and perhaps their most serious problem is that
they are overlapping and mutually dependent.
Nonetheless, the categories present a scheme
with which our discourse analysis coders were
able to systematically analyze talk and behav-
iors. In the following sections and in Table
1 below, the categories are presented, with a
description, a rationale from the literature, and
a concrete example from the transcript.

As mentioned above, the data that we analyzed
for this paper describe three complete runs of
a game of Pandemic (Leacock, 2007) played
by three groups of three-to-four different first
year undergraduate students (ages 17-19) at
a major university located in the Mountain
West. We refer to these groups as “Alpha”
“Delta” and “Lambda”. Alpha was made up of
3-4 males (1 player could not make the game
sessions consistently), Delta was made up
of 4 females, and Lambda was made up of 4
males. 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; this is explicitly
encouraged in the accompanying instruction
booklet (Leacock, 2007). Each session lasted
between 60 and 90 minutes.
We collected video recordings of all inter-
actions, and we generated transcripts from the
recordings. We segmented video excerpts into
gameplay turns and then sub-divided them by
utterance. Each of these excerpts was coded
with respect to the rules that were being in-
terpreted or the strategies that were ultimately
developed. Iterative reviews of the video for
the excerpts yielded narrative accounts for
how the actions of different participants, their
state of knowledge at the time, and the state
of the game materials resulted in the ultimate
strategies or understanding of game rules. These
accounts were constructed independently by the
two authors, then refined through competitive
argumentation (Schoenfeld, Smith, & Arcavi,
1993) and extended discussion.
Using the interpretive analysis of specific
excerpts, a set of codes related to computational
thinking was developed (Table 1). These codes
were then refined from multiple coding passes
with data subsets. Our final set of codes included
five categories of computational thinking:
conditional logic, algorithm building, debug-
ging, simulation, and distributed computation.
Conditional logic involves using an “if-then-
else” logic structure, and often involves players
describing the chain of events that might happen,
based on the games rules, should a particular
action be taken. Algorithm building involves
the construction of a plan of action, with the
long-term goal being that the algorithm be robust
enough that it can be reused in the future for
unknown or unpredictable events. Debugging
involves diagnosing errors in logic or behavior.
It often involved clarifying rules or strategies
during game play. Simulation involves the en-
actment of algorithms or plans in order to test
the likely outcome. For example, a player who
moves their token to various spots and declares
the actions they would take without releasing
the token (and thus committing herself to a
set of decisions) would be engaged in simula-
tion. Distributed computation is an inherently
social aspect of computational thinking, in
which different pieces of information or logic
are contributed by different players over just a
few seconds during the process of debugging,
simulation, or algorithm building.
As stated above, this list is not exhaustive
nor are the codes mutually exclusive. Because
of the inherent interdependencies of different
computational actions, we did not expect that
we would be able to define explicit boundar-
ies. Our approach is to identify each category
by its exemplars. We validated those decisions
by comparing our set of categories against
computational activities already described in
the literature related to computational thinking
(Abelson, Sussman, & Sussman, 1984; National
Research Council, 2010; Papert, 1980; Wilensky
& Reisman, 2006; Wing, 2006).
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Table 1. Summary of the code categories, accompanied by rationale from the literature and
examples from the data corpus
Category Description Rationale Example
Conditional
logic
Conditional logic is the use
of an “if-then-else” construct.
It requires a student to think
globally about the local conse-
quences of the
truth-value of a
given statement.
Wing (2006), the National Research
Council (2009), and a common intro-
ductory computer science textbook
(Abelson, Sussman, & Sussman, 1996)
all present the conditional logic construct
as the simplest construct underlying all
computation. Most machine language is
evaluated conditional logic and simple
variable use. We are categorizing vari-
able use as “global logic.” Effectively,
a student using conditional logic with
variables is doing computational thought.
“...if Milan gets one more,
that means Istanbul gets one,
and if Istanbul had 3, that
means Istanbul would start
infecting ones next to it, too,
and it would be like a chain
reaction.”
Algorithm
building
An algorithm is a data “recipe”
or set of instructions. Funda-
mentally, computer programs
consist of algorithms and data.
Algorithms often contain sets
of related conditional logic.
In its simple form, it is the
planning of actions for events
that are taking place; in its
complex form, it is planning
for unknown events.
Though there is significant debate in
our source literature on the relationship
between programming and computational
thinking, Papert’s (1980) “procedural
thinking” construct is about teaching
students to abstract their concepts into
algorithms. The National Research
Council (2009) makes several references
to procedural thinking as a core concept
of computational thinking.
“...I could move ... here, that’s
1. And then take out 1 there,
then go to Tokyo, so 3. Wait,
1, 2 ... I could move here; and
then just not do anything there;
and then move to Tokyo; and
then fly from Tokyo to where
A is; and then give him this
card so the beginning of his
next turn ... he can play.”
Debugging Debugging is the act of
determining problems in order
to fix rules that are malfunc-
tioning.
Papert (1980) describes debugging as a
core “powerful idea” of procedural think-
ing. Wing (2006), the National Research
Council (2009), and Abelson, Sussman,
and Sussman (1996) describe debugging
as central to both programming and
computational thinking.
Alex2: “...but I think that might
be only during epidemics.”
Brad: “Do you add them back
to the top during epidemics?
Cause I was reading here,
whenever a player draws...”
Alex: “Okay, so then I’ll just
leave it there.”
Simulation Simulation is modeling or
testing of algorithms or logic.
Simulation is used in debug-
ging in order to determine
problems, and it uses algo-
rithm building to test a model.
We are defining simulation as
the enactment of algorithms
or plans.
Simulation or model building underlies
computation in the mathematical sense.
Wilensky and Reisman (2006) define
computational thinking as various as-
pects of model building or simulation.
“...Essen, I have [the Essen
card], so I could fly, I could
take care of that during my
turn. [I could address] that
London outbreak after I take
care of that. ‘Cause that would
take one, then I can fly to Es-
sen, then move there. And then
I can take the rest of that.”
Distributed
computation
Distributed computation ap-
plies to rule based actions. For
instance, if 3 people act togeth-
er through a rule-based plan,
this is distributed computation
as considerations, contingen-
cies, and strategy formation
involve multiple parties with
different knowledge resources.
The National Research Council (2009)
describes distributed computational
thinking as one social aspect that dis-
tinguishes computational thinking from
computer science.
Patrick: “Okay, for my turn
first off I’m going to cure
Lima... And then I’m going to
move LJ. ... I’ll move you here
because that way you’re only
two away.”
L.J.: “You can move me to
one of your cards, and then I’ll
teleport there.”
Michael: “But you can only
trade the card of the one
you’re standing in.”
L.J.: “Oh, that’s right.”
Michael: “Just because you
have one, you can’t turn all of
them in...”
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With this set of categories in place, codes
were then assigned to the individual utterances
in each of the three transcripts. Data coding was
done while the video recordings were reviewed
simultaneously, so that the players’ meanings
could be more accurately inferred. Having these
codes in place served multiple purposes, as we
illustrate below. First, these codes allowed us
simply to determine the relative frequencies
of each category relative to the larger corpus.
Second, they allowed us to more precisely de-
scribe some of the game-turns in a systematic
and consistent way.

An initial analysis step in our work was to
identify how frequently the different aspects
of computational thinking appeared across the
three groups. To do that, the coded transcript
lines were automatically counted and plotted
in five-minute intervals in the graphs shown
in Figures 2 through 4. In total, there were
1711 utterances in the Lambda game (1 hour,
26 min), 1286 utterances in the Alpha game (1
hour, 23 min), and 869 utterances in the Delta
game (1 hr 3 min). As talk was fairly continuous
throughout the game, each five-minute interval
should be thought of as roughly between 70-
100 utterances. It is important to note that the
amount of talk that indicated computational
thinking is generally often less than half overall
because of a high frequency of utterances in
which players expressed agreement with one
another (“Yeah”, “Uh huh”, “Okay”), stated
directly the actions they were taking without
specifying their thinking processes (e.g. “I’m
going to move here, and now I draw a card”),
or engaged in miscellaneous banter (E.g.,
“Dude, you’re so lame”, “What time is that
party tomorrow?”) with each other. Also, recall
that the coding categories were not necessarily
mutually exclusive, so there is some overlap
between categories.
The first 10-20 minutes of each game in-
volved group members reading the guidebook
Figure 2. Alpha-game code counts and frequencies
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and deciphering the rules of the game, which
explains the low amounts of computational
thinking evidenced during the first five to ten
minutes of each game. However, as we will
discuss, dealing with or learning the rules were
not exclusive of computational thinking. It was
simply that much of those times involved one
or two individuals reading out loud to the oth-
ers. With those caveats in mind, it is apparent
that the groups all varied with respect to each
other and throughout the game in the kinds of
computational thinking they expressed. Dis-
tributed computation was consistently the most
frequently occurring computational discourse
for all groups. The players were indeed engag-
ing in a substantial amount of crosstalk and
were collaboratively making sense of actions,
algorithms, rules, and plans. In that respect, we
see that Pandemic is successful at fostering
collaboration.
The next most frequent type of observed
computational thinking, other than distributed
computation, depended on the group. For the
Alpha group, it was simulation of future steps.
For Delta and Lambda, it was debugging. In
the illustrative excerpts below, we will present
examples of both.

Both the Lambda and Delta groups were ac-
tively involved in reviewing the rules of the
game and resituating those rules into possible
actions during individual turns. This took place
during game play, rather than when the rules
were first announced. For example, one rule
introduced to all players early in the guidebook
involves the conditions under which one can
exchange cards with another player (Figure 5).
Basically, two players may exchange a card if
the card that is to be exchanged is of the city
that both pawns presently occupy. The one
exception to this is if a player draws a special
card at the beginning of the game that exempts
them from the requirement of having his and
another player’s pawn located in the city of the
card that is to be exchanged. It is an exemp-
tion that only applies to the giving (not the
Figure 3. Delta-game code counts and frequencies
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receiving) of a card by the exempt player. This
proved to be a complicated point for Lambda;
the group revisited it several times throughout
the game. It involves establishing a number of
preconditions being simultaneously met and
also knowing an alternative path for following
the card exchange action.
There was a substantial amount of time
devoted by both the Lambda and Delta groups
to determine what the preconditions were and
whether or not they were met. To illustrate, we
show some of the ways in which this played
out for the Lambda group.
Twenty-two minutes into the Lambda
group’s game, Michael declared that he had the
specific ability to exchange cards with other
players without needing to be in he same city
as the card that he is exchanging (i.e., the spe-
Figure 4. Lambda-game code counts and frequencies
Figure 5. Card exchanges (a) when both players’ tokens occupy the same space as the city card
they wish to exchange are permissible at all times. On the other hand, (b) card exchanges in
which a card different from the city occupied are not permitted unless a player has drawn a
special card at the beginning of the game that permits such exchanges.
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cial exemption). This then prompted Patrick to
clarify how and when cards could be exchanged
between players.3
1. Michael: I can give a player a card from
my hands, for one action per card…That’s
my ability.
2. Jackson: Oh you can give me a card for an
action point.
3. Michael: So it says both of our pawns must
be in the same city, though it doesn’t matter
which city you’re in.
4. Patrick: So since you’re operations expert,
your special ability.
5. Michael: Patrick, my special ability - I can
give you cards as my actions. From my
hand.
6. Patrick: They don’t have to be in the city?
7. Michael: [Interrupting] Aahh, we have to
be in the same city. But I can give it to you.
8. Patrick: Oh so, it doesn’t matter like what
city.
9. Michael: ...but we have to be in the same
one.
10. Patrick: So doesn’t matter like…
11. Michael: ‘Cause when you like share
knowledge…you have to be in the same
city that’s pictured on the card, or I can
just give it to you.
Michael began by stating that he operated
through the exception for card exchanges.
Jackson then affirmed that the outcome for that
operation would result in a card exchange and
a decrease in one action point. Michael then
clarified some of the preconditions. Patrick’s
subsequent interjection and discussion with
Michael was one instance in which there were
several utterances related to debugging (lines
6-9) because in those lines, the players sought to
clarify whether the current card to be exchanged
must be the same as the city that was currently
occupied by the two player tokens. That debug-
ging episode ends with Michael restating the
main distinction between the regular rule and
the exception that he can follow.
About fifteen minutes later, Patrick was
relying on this knowledge of how cards were
exchanged to suggest that LJ give Patrick two
black cards. Michael agreed that could be a
good plan, but Jackson interrupted to clarify
(and debug what he saw as a possible confu-
sion) around what was or was not possible
given the rules.
12. Patrick: LJ, if you can give me the two
blacks, we can build a research station in
black, we could cure black.
13. Michael: Yeah, you’re right in the, right
next to each other.
14. Jackson: How come you just, he can’t just
give them to you though.
15. Patrick: No, I have to be in Mumbai or
Chennai.
16. Jackson: So [Patrick] can get one of [either
the Mumbai or Chennai card]
In the second excerpt, Patrick was coordi-
nating with LJ on how to exchange two cards
in a way that would help the group to make
progress in the game. Their pawns were posi-
tioned near each other, but not in the requisite
locations. Jackson interjected by stating that
Patrick’s cards did not follow the exception that
applied to Michael, which Patrick agreed with
(line 14-15). Jackson further sought to clarify
the base rule that the exact city card that was
to be exchanged must be the one that both LJ
and Patrick player tokens occupied (line 16).
Twenty minutes later, on another one of
his turns, Patrick was trying to figure out if he
could receive a blue city card from Michael on
the immediate turn. In order to straighten out
what was or was not permitted, Michael had
to refer to a written rule and translate that into
permissible actions for card exchanges.
17. Patrick: My turn. Okay, so what we need
to do.
18. Michael: Remember, you want me to give
you my blues.
19. Patrick: So where are you?
20. Michael: Right there. In Sydney.
21. Patrick: So I need you to fly me, I’m going
to fly you to LJ, and then you can, on your
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turn, you’re sure you can only give it to
me on my turn, your turn?
22. Michael: Yes. [reading]You may give a
player a card from your hand. One action
per card.
23. Patrick: Okay.
24. Michael: So I have to spend my action to
give it to you.
25. Patrick: Okay. But at least the next time
around when it’s my turn, I can cure blue.
That exchange involved clarification
that the exception did not include any kind of
exchange. That is, only during Michael’s turn
could an arbitrary card be given, rather than
on Patrick’s turn. As the non-exception rule
permitted card-giving and receiving, this was
another debugging of the rule that took place
in the game, and ultimately prevented Patrick
from executing his larger plan (i.e., curing the
blue disease).
The above excerpts all represent instances
in which debugging was making up a larger por-
tion of the group’s game play. Note that while
these bugs could all be resolved by referencing
written rules, these excerpts illustrate some of
the same fundamental ideas shared in computa-
tion: special exceptions that must be handled
(as is the case with Michael’s special ability to
exchange cards more freely) or sequences of
actions must be reconfigured so that actions
are permissible given previously established
rules and conditions. These clarifications and
exceptions were encountered under a larger goal
structure that each individual player had. For
example, Patrick often had the goal of making
sure a specific player possessed a certain number
of cards so a cure to a disease could be found.

While the activities of debugging were distrib-
uted in the above excerpts between multiple
participants calling out exceptions or viola-
tions, distributed computation looked quite
different in situations where conditional logic
and simulation that takes place. To illustrate,
we present a brief excerpt from the game with
group Alpha (Figure 6). This example arose as
the players tried to decide what John should do
during his next turn as they observed that the
yellow disease and red disease were spreading
on the game board. Note that in this excerpt,
no pieces were being moved. They were only
discussing hypothetical actions.
26. Aaron: That’s the end of my turn. Right.
27. CJ: You should build the next one [research
station].
28. Aaron: And this one goes here. So we’re
down two epidemics. So.
29. CJ: You should build the next one [research
station] here so we can hurry and cure
the yellow one [disease] before it gets all
outbroken.
30. Aaron: Yeah, cause we got almost enough
[cards]. We just need one more.
31. CJ: Actually
32. John: We need to stop this [the red spread]
though. We need to prevent it.
33. Aaron: I can get over there. I’ll fly into
Bangkok and take care of that. That’s 1, 2
[actions].
34. John: I know but
35. Aaron: Move over here and take care of
that [yellow region].
36. CJ: Actually, you both-
37. John: Here’s the thing. The next card is
Ho Chi Minh, which means it’s the fourth
[disease marker] which means he’ll get
one-
38. CJ: Outbreak
39. John: -he’ll get one, he’ll, he’ll get another.
Which means these ones [cities] will all get
one.
40. Aaron: Okay, so I can take care of that.
41. John: No, I have to take care of it right
now.
42. Aaron: You going to take care of it? Okay.
Oh yeah, huh.
Table 2 summarizes the features, rules
and conditions, events, and outcomes of two
“simulations” in this transcript.
During this excerpt, two simulations were
being run (Table 2). One was being run primar-
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ily by Aaron, who suggested that the players
focus on finding a cure for the yellow disease
by using John’s turn as an opportunity to estab-
lish a research station. That would then allow
Aaron to cure the yellow disease, under the as-
sumption that he would obtain a yellow card
during his next turn. That was ultimately an of-
fensive strategy against the game, in which the
players would prevent the long-term spread of
the yellow disease for the rest of the game. John
ran the other simulation, which was more defen-
sive in nature. John noted that at the end of each
turn in the game, there were cards to be drawn
with cities specifying where a disease infection
will escalate next. If the Ho Chi Minh City card
were drawn, then four disease tokens would
infect Ho Chi Minh City, and if a city received
four disease tokens, it would trigger a cascading
outbreak. This was the point that John made
when he announced, “we need to stop this” (line
32). He illustrated the implications of this hypo-
thetical scenario by describing what would
happen next in the game (lines 37, 39).
Aaron, who had already run his own of-
fensive simulation, made a quick appeal that he
could address the problem with the red disease
during his turn (line 33). This was, effectively,
an attempt to slightly modify John’s defensive
simulation. However, moments after he had a
chance to fully understand the conditions under
which John’s simulation were being run (i.e.,
John would have to draw cards to increase the
level infection at the end of his turn, and the next
card could be Ho Chi Minh City), he accepted
John’s suggestion to treat Ho Chi Minh City
immediately. CJ, who unsuccessfully tried to
interject, these two simulations tacitly agreed
to the defensive plan offered by John.
The utterances in this excerpt suggest that
the players were using conditional logic to plan
against unknown future conditions. They had
internalized a set of rules and understand some
starting conditions under which those rules can
then be run over a period of time (characterized
as player actions and player turns). By moving
forward, one time-step after another, they were
able to make some predictions about future out-
comes and decide on a course of action, based
on their game-based computations.


The preceding examples show instances of com-
putational thinking, but we have shown it only
firmly situated within the game context. Action
is based on immediate demands, and it is not
always clear that any process of abstraction or
generalization is taking place. While we expect
that to some extent a player’s computational
thinking must be situated in the game context
(Greeno, 1998), we would hope that the nature
of that computational thinking would begin
to involve some degree of generalization, as
the ‘programs’ that students develop and run
through their discourse should become more
efficient over time. For example, we hope for
players to develop new strategies (e.g., ignore
Figure 6. Current game state for the red region of the board (East and Southeast Asia). Ho Chi
Minh City and Bangkok have 3 disease cubes each and Taipei and Jakarta have 1 disease cube
each. Referenced starting at line 32.
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disease levels until they reach 3 on a given city,
always designate a single player to handle each
disease) or principles or produce some gener-
alizations (e.g., disease levels greater than 3
always trigger outbreaks) that would help them
to control the game.
We describe the kinds of logic and pro-
cesses in the two examples from the Alpha
and Lambda games as ‘local’ logic. Local logic
relates directly to immediate actions being taken
and the structure of the logic or processes is
not identified as applicable to a future course
of action. A statement such as “let’s move to
Chennai and cure the diseases there” is an
obvious example of local logic. An alternative
is abstracted (or ‘global’) logic, in the sense
that it involves “higher order” relationships
(as per Newell & Simon, 1972). For example,
Table 2. The two simulations run during John’s turn in the Alpha group game
YellowCureSimulation(runbyAaron) RedInfectionSimulation(runbyJohn)
Central Features of
Current Game State
for Simulation
Aaron possesses 4 yellow city cards The Ho Chi Minh City space on the game
board has 3 red disease cubes, as does the
one for Bangkok.
Relevant rules
and conditions
for simulation
• A player must have 5 of the same color
city cards to develop a cure of the disease
of that color (e.g., James must have 5 blue
cards in order to develop a cure for the blue
disease)
• A disease cure can only be developed on a
city that has a research station built upon it
• A research station can be built in a city by
a player occupying that city as one of their
actions during their turn.
• New city cards for each player to use for
cures or transportation are drawn at the end
of each turn
• A player can remove a single disease cube
from a city they occupy as one of their four
actions in a turn
• A disease cube of a pre-designated color
must be added to each of the cities drawn
from the infection card pile at the end of each
player’s turn.
• Should the number of disease cubes of a
given color at a city location be greater than
three, then no new disease cube is placed on
the current city. Instead, an additional disease
cube is placed on each neighboring city.
• A player can remove a single disease cube
from a city they occupy as one of their four
actions in a turn
Simulated events (based on transcript lines 27, 29, 30, 33 35)
Step1: John builds a research station
within reasonable proximity to Aaron’s
player token
Step2: John begins to eliminate yellow
disease cubes from yellow cities.
Step3: Aaron begins to eliminate some red
disease cubes from red cities.
Step4: Aaron obtains an additional yellow
city card on his next turn,
Step5: After CJ and John complete their
next turns, Aaron moves to the research
station to cure the yellow disease with 5
yellow city cards.
(based on transcript lines 32, 37, 39, 41, 42)
Step1. Regardless of what John does during
his turn, he draws two cards indicating cities
that must be infected.
Step2. One of those cards is Ho Chi Minh
City. That requires him to place an additional
disease cube on Ho Chi Minh City.
Step3. Should no new action be taken prior,
Ho Chi Minh City will already have three
disease cubes, and therefore will not increase
in total number of disease cubes. Jakarta,
Bangkok, Manila, and Hong Kong will each
receive an additional red disease cube.
Step4. Bangkok will also already have 3
disease cubes, and thus will not receive the
new disease cube from Ho Chi Minh City.
Instead, it will cause it’s own neighboring
cities to increase their number of red disease
cubes by 1.
Simulation
outcome
Yellow disease is cured in 5 player turns Red disease level increases by a net of 6
cubes after 1 player turn.
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global logic may involve reasoning discourse
such as: “we need to make sure to cure when
cities have three disease cubes or else we have
an outbreak risk.” Global logic and processes
require players to make a set of predictions for
potential actions or decision in the game given
past evidence, and to act on those predictions.
This is roughly equivalent to (multi-agent)
programming, in that players design plans for
multiple actors given novel data.
Global logic and processes, as we refer to
them, can be identified by using second order
predicate logic operands (e.g., ‘there exists some
X’, ‘all X such that Y’, or ‘every X’) and their
synonyms in spoken English. For instance, at
the simplest level, clarifications of the game’s
rules often take the form of global logic (e.g.,
Alex: “So you can use an action point every time
to move me.”). This form of logic recurs with
increasing frequency as the games progress, as
the players learn how to play the game. This
occurs most clearly in the Alpha transcript, as
shown in Table 3. As another example, consider
the Lambda game4, in which global logic state-
ments appeared once in the first 500 utterances
of the game, but then 9 times during the second
500 utterances. While we have not yet examined
the interactions between local and global logic
in great depth, we expect that the increased
number of times when more global logic is
used would increase over future games with
the same group. We hope to devote more time
to analyzing the transition toward abstraction
in future papers.


In light of our analyses and from our own
observations of these groups of student play-
ers, we suspect that the quantity and quality
of computational thinking in our data occurs
because the players were required to: 1) inter-
nalize a set of rules and 2) devise strategies for
optimizing behavior given the set of rules. In
terms of computation, using conditional logic
and debugging more often occurred as students
internalized the rules of the game. None of
the groups understood the rules by reading
through the guidebooks without attempting
to play through the rules. Behavior optimiza-
tion required running those rules as part of a
simulation or developing algorithms that will
lead to desired outcomes. We have attempted
to illustrate through the examples and analyses
in this paper that the processing and reasoning
that takes place in a collaborative strategic board
game is complex and computational in nature.
Based on this work and our own observa-
tions as amateur tabletop gamers, we expect
similar computational processes also take place
in non-collaborative strategic board game play.
However, they are less visible as players attempt
to obscure their actions and motivations (in order
to compete more effectively). We believe that
the emphasis in many of these board games on
resource management still engenders the same
forms of conditional logic and simulation activi-
ties. For example, in the farm-themed strategic
Table 3. ANOVA for changes in Alpha’s use of global logic during game play. Alpha increasingly
used more global logic statements later in the game
ANOVAb
Model Sum of Squares Df Mean Square F Sig.
1 Regression .427 1 .427 5.355 .022a
Residual 11.407 143 .080
Total 11.834 144
a. Predictors: (Constant), TIME
b. Dependent Variable: GLOBAL_LOGIC
International Journal of Game-Based Learning, 1(2), 65-81, April-June 2011 79
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is prohibited.
board game, Agricola (Rosenberg, 2007), a
player must consider how many turns will be
required before he acquires enough material to
build fencing for their livestock and the pos-
sible implications on a future score of making a
pastures of a particular size or shape; the game
requires some simulation activity in order to win.
The research advantage to focusing on a collab-
orative strategic board game like Pandemic lies
in the requirement that players must externalize
their thinking processes and collaborate on their
actions. Because coordination is necessary to
win the game, it makes the development of rule
understanding and group strategy formation
an important part of the thinking and reason-
ing that takes place; distributed computation
is explicitly verbalized. Those features make
Pandemic uncommon among contemporary
strategic board games, although there are oth-
ers that are similarly collaborative such as Lord
of the Rings (Knizia, 2000) or Shadows Over
Camelot (Cathala & Laget, 2005), but it also
makes Pandemic a promising site for future
research (Zagal et al., 2006).
Thinking more broadly across board
games, even beyond the contemporary, German-
influenced ones that we have considered here,
we expect there to be a degree of computa-
tional reasoning in gameplay. As we have said
before, tabletop games are rule-based systems
and require players to do the work that is often
done by a computer in a video or console-based
game. For example, we expect debugging to
be the component of computational thinking
most obvious across numerous game settings
because it can be associated with the process
of learning and internalizing rules. This was
illustrated in Lambda group’s work in debug-
ging the card exchange rules. In that example,
a player: 1) found a bug in his thinking through
the enactment of his plan (‘program’); 2) was
flagged on his error by another player; and 3)
revised his program so that it could conform
to the actual game rules.
Algorithm building can occur across many
different types of tabletop games, and we expect
that it grows as an individual learns to better
play a game. However, the reduced reliance on
chance is one distinguishing feature of contem-
porary, designer board games. In this genre of
games, the player can build established routines
fairly early on in their gameplay experience that
can be reused several times in the future. The
specific form of the randomness has a large
effect on the types and amount of algorithm
building that will occur. That is not to say that
complex algorithms do not take place in games
with high-randomness (e.g., Scrabble, in which
the tiles one draws can absolutely influence the
likelihood that an excellent player wins or loses
(Fatsis, 2002)), but that designers can encourage
complex computation through design features.
Having presented the argument that com-
plex computational thinking takes place in one
collaborative board game, we close with a bolder
hypothesis that must ultimately be empirically
verified. We suspect that many of these contem-
porary strategic board games could represent an
important, and as-yet, under-considered founda-
tion from which designers can intentionally de-
velop computational thinking. This hypothesis
invites new avenues for research. For example,
we are exploring the instructional design is-
sues associated with turning board game based
computational thinking into a digital media
computational literacy. The authors are also
studying game-based computational thinking
in its own right, focusing on understanding the
complexities and design issues associated with
the tabletop environment. One important next
step is to more precisely connect the relationship
between aspects of a game’s design (e.g. turn
structure, constraints, etc.) to the computational
thinking that is ultimately elicited.
Thinking more broadly, there are also sev-
eral social aspects related to strategic games that
could also be explored. A number of contem-
porary board games have an inter-generational
appeal to them; in our experience, we have
observed families will often gather to play
casually and local hobby stores frequently have
a mix of teenagers and adults. An interesting
issue to explore further is how this particular
medium of play can increase participation in
computational activities. Based on the work
we have done in this paper and the possibili-
80 International Journal of Game-Based Learning, 1(2), 65-81, April-June 2011
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is prohibited.
ties we see ahead, we remain optimistic that
a promising arena for research has naturally
emerged and is awaiting closer examination.
Just as Gee (2007) shows how video games can
be productive spaces for learning print literacy,
we believe the same holds true and should be
seriously considered for contemporary board
games and computational literacy.
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is prohibited.

1 Many of the most popular games originated
from German designers, such as Klaus Teu-
ber’s Settlers of Catan (1995), and they have
gained a large following in Europe. The term
“designer game” has been used to acknowl-
edge these games often prominently feature
the name of the game designer on packaging
materials.
2 All names used here are pseudonyms.
3 Transcript excerpts are numbered for the ease
of reference in the text. These numbers do not
necessarily indicate any order or sequence
to the excerpts (e.g., utterance 12 happened
several minutes after utterance 11).
4 The absolute numbers of such logic statements
in the Lambda and Delta games were too small
to run meaningful statistical analyses.
Matthew Berland is an assistant professor in the Department of Interdisciplinary Learning &
Teaching at the University of Texas at San Antonio. He received his Ph.D. in Learning Sciences
from Northwestern University in 2008, studying computational literacy, systems literacy, and
the design of constructionist learning environments. In 2009, he completed a postdoctoral fel-
lowship in the Institute for Computational Engineering and Sciences at the University of Texas
at Austin working on AI systems and human-robot interface design. His current projects include
a mobile robotics game/learning environment; a computational thinking project using tabletop
board games; a project to investigate the learning processes of novice programmers; and novel
assessments for constructionist classrooms.
Victor R. Lee is an assistant professor of Instructional Technology and Learning Sciences at
Utah State University. His research involves the study of visual representations, cognition as
it takes place in face-to-face interactions, science education and instructional materials, and
new technologies to support teaching and learning in K-12 settings. Current projects include a
design investigation into the use of physical activity data devices for math and science learning,
a qualitative evaluation of schools moving to an online-only science curriculum, and a study of
how students represent informal learning experiences with digital photography. On the side, he
is an amateur gamer who maintains a slight preference to tabletop games over console-based
ones. Lee obtained his Ph.D. in Learning Sciences from Northwestern University in 2008.
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Thesis
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