Game-based structural debrieﬁng
How can teachers design game-based
curricula for systems thinking?
Yoon Jeon Kim
Playful Journey Lab, Massachusetts Institute of Technology,
Cambridge, Massachusetts, USA, and
Department of Social Science and Policy Studies, Worcester Polytechnic Institute,
Worcester, Massachusetts, USA
Purpose –The authors developed a pedagogical framework called the game-based structural debrieﬁng
(GBSD) to leverage the affordances of video games for teaching systems thinking. By integrating system
dynamics visualization tools within a set of debrieﬁng activities, GBSD helps teachers make systems thinking
an explicit goal of the gameplay andlearning when they use available educational games in the classroom.
Design/methodology/approach –This study uses a design-based research methodology with the goals
of validating GBSD and investigating the utility of GBSD across different contexts as a design source to
develop game-based curriculum. Over the course of 12 months, the authors conducted one focus group
interview and three design workshops with participating teachers and master teachers. Between the
workshops, the team rapidly iterated the framework, as well as curricular materials, in collaboration with the
Findings –The authors developed a curriculum unit that integrates systems dynamics visualization tools
and a video game for middle school life scienceecosystem curriculum. The unit was implemented by the three
teachers who participated in the co-design. The implementations conﬁrmed the ﬂexibility of the unit because
teachers created additional instructional materials that supplemented the GBSD protocol and addressed the
unique limitations and needs of their classrooms.
Originality/value –GBSD builds on system dynamics, which is a distinct academic discipline and
methodology, and it uses its visualization tools, which are not widely used in the systems thinking
educational literature. GBSD is also unique, in that it applies these tools within the debrieﬁng activities
developed for an off-the-shelf educationalgame. This paper illustrates how a design framework can be used to
support teachers’thoughtful integration of games in curriculum development.
Keywords Systems thinking, STEM, System dynamics, Game-based learning,
Design-based research, Video games, Causal diagrams, Structural debrieﬁng
Paper type Research paper
The beneﬁts of digital games as a vehicle to support student learning have been well
documented. In a meta-analysis study, Clark et al. (2016) reported that compared to nongame
conditions, digital games had a moderate to strong effect in terms of overall learning
The authors gratefully acknowledge the ﬁnancial support of the Spencer Foundation, Grant#
201600124. They also thank BrainPOP for participating in this project. The authors are grateful to
Christine Whitlock for her excellent research assistance, and they thank two anonymous reviewers
for their insightful comments and extremely helpful suggestions.
Received 7 May 2019
Revised 28 July2019
Accepted 10 September2019
Information and Learning
© Emerald Publishing Limited
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outcomes including cognitive and interpersonal skills. Similarly, a literature review by
Boyle et al. (2016) found that games are beneﬁcial for learning of various outcomes such as
knowledge acquisition, affect, behavior change, perception, and cognition. Numerous
studies also reported academic domain-speciﬁcbeneﬁts of games for learning including
science (Li, 2013; National Research Council, 2011; Danish et al., 2017;Puttick and Tucker-
Raymond, 2018) and math (Kebritchi et al., 2010;Divjak and Tomi
c, 2011; Starkey, 2013).
Games are well suited as instructional tools not only for children but also for adults in cases
where adults need to be educated about complex policy issues, such as the climate change
(Wu and Lee, 2015).
While the ample evidence shows that games have great potential to support learning,
Clark et al. (2016), caution that not all games are appropriate for learning because how
certain game design elements (e.g. game mechanics and narrative) are associated with the
learning goals matters. Even when the game was intentionally designed for learning, only if
combined with a thoughtful curriculum that aligns the game elements with curricular
activities can it be successful in supporting learning in classrooms (Squire, 2011). Given that
many classroom teachers have adopted games –the Joan Ganz Cooney Center’s Level Up
Learning survey indicates that 74 per cent of teachers are currently using digital games for
instructional purposes with their students (Takeuchi and Vaala, 2014)–further
investigation is needed to support thoughtful integration of games with existing curricular
Successful and meaningful integration of game-based learning in classrooms largely
depends on teachers’practices and classroom-contexts (Klopfer et al.,2009;Hmelo-Silver
et al., 2015). Several studies investigated the challenges of adopting game-based learning in
classrooms (Baek, 2008;Kirriemuir and McFarlane, 2003;Hmelo-Silver et al., 2015), which
include the rigidity of the curriculum, the perceived negative effects of gaming, unprepared
students, the lack of supportive materials, ﬁxed class schedules and limited budgets.
Moreover, ensuring that the gameplay is relevant for both the classroom’s context and
curricular needs is a signiﬁcant factor –without a balance between the needs of the
curriculum and the structure of the game, achieving the intended learning outcomes of the
game-based curriculum cannot be possible (Van Eck, 2006). Therefore, it is crucial to
support teachers as they select and integrate appropriate games and align them with the
goals of their curriculum (Baek, 2008). However, little is known about teachers’pedagogical
roles and practices in game-based curriculum (Foster and Shah, 2015), and only few
curriculum design frameworks exist that can guide teachers’efforts.
Games for systems thinking
Systems thinking (ST) skills have been recognized as part of the core science literacy, and
more recently, as critical twenty-ﬁrst century skills. The Next Generation Science Standards
(NGSS) reﬂect this perspective by explicitly infusing systems thinking concepts across the
standards. While this is a signiﬁcant step forward, it poses imminent practical challenges
for teachers in terms of how to teach ST skills.
Educational research suggests that video games support systems thinking skills because
learners’understanding of complex systems improves when they are engaged with complex
phenomena in hands-on or simulated experiences (Gee, 2003; Torres, 2009; Hmelo-Silver
et al., 2015;Danish et al.,2017;Peppler et al.,2018;Puttick and Tucker-Raymond, 2018).
However, without a systematic pedagogical approach, the game remains a “black box”to
students, even if they implicitly understand how the system operates (Alessi, 2000;Größler
et al., 2000;Pavlov et al.,2015;Hmelo-Silver et al.,2015;Hmelo-Silver et al.,2017). The
curriculum aimed at developing skills that are needed for investigating complex systems
phenomena must encourage critical questioning, collecting data, generating hypotheses and
testing these hypotheses (Hmelo-Silver and Azevedo, 2006;Jacobson and Wilensky, 2006;
Hmelo-Silver et al.,2015;Yoon et al.,2016).
So what aspects of games or curriculum that is based on a game could lead to learners’
understanding of complex systems? We know that system modeling encourages
constructive discussion and makes ideas visible, and therefore leads to a better
understanding of a system (Hmelo-Silver et al., 2017). Additionally, visualization of complex
causal mechanisms between explicit and latent components has been shown to be useful in
developing students’systems thinking and helping them understand complex systems
(Hmelo-Silver et al.,2017). For example, in a multi-year study by Hmelo-Silver et al. (2017)
students drew aquatic environments and constructed computer-aided conceptual maps,
which helped students improve their understanding of causation between components and
Game-based structural debrieﬁng
The majority of educational research that deals with systems thinking and complex systems
uses agent-based modeling (Hmelo-Silver et al.,2015;Yoon et al.,2016;Hmelo-Silver et al.,
2017). However, there is a distinct systems modeling approach called system dynamics,
which was invented by Jay Forrester at Massachusetts Institute of Technology in the 1950s
to address problems in management (Forrester, 1958). Unlike agent-based simulations that
create artiﬁcial agents and model their interactions, system dynamics models are computer
representations of systems at the population level (Scholl, 2001;Schieritz, 2002).
Mathematically, system dynamics models are systems of continuous partial differential
equations (Morecroft, 2007;Maani and Cavana, 2007). One of the fundamental tenets of
system dynamics is that system structure determines system behavior (Richardson and
Pugh, 1981, pp. 15-16; Größler et al.,2008). Now, system dynamics is an academic discipline
with an academic society, conferences, a peer-reviewed publication (System Dynamics
Review), and graduate academic programs (Davidsen et al.,2014;Pavlov et al., 2014;
Schaffernicht and Groesser, 2016).
Over several decades, the system dynamics ﬁeld has developed its own graphical
conventions and visual tools for making variables, system behavior, causal structure,
feedback loops, delays and stock-and-ﬂow structure of systems explicit (Camara et al., 1994;
Lane, 2000). While these visual tools and system dynamics modeling have been successfully
used in numerous real world applications (Vennix, 1996;Moxnes, 2000;Stephens et al., 2005;
Morecroft, 2007;Maani and Cavana, 2007;Pavlov et al., 2008;Hovmand, 2013;Lane et al.,
2015) and in the classroom (Sweeney and Meadows, 1995;Alessi, 2000;Nasakkala, 2000;
Lyneis and Fox-Melanson, 2001;Sweeney and Sterman, 2007;Richardson, 2014;Quaden
et al.,2015), these tools are not widely used by the mainstream educational research on
systems thinking. For example, while conceptual maps have been recently used to show
system variables and mechanisms between them (Hmelo-Silver et al.,2017;Danish et al.,
2017), conceptual maps are distinct from connection circles and causal loop diagrams, which
are used in system dynamics (Appendix). In fact, system dynamics refers to the causal loop
diagrams (CLDs) as dynamic hypotheses because a CLD is a system model that may or may
not replicate the observed system behavior (Morecroft, 2007;Maani and Cavana, 2007).
We developed a pedagogical framework called the game-based structural debrieﬁng
(GBSD) that leverages the affordances of video games for teaching ST by integrating system
dynamics tools within a set of debrieﬁng activities. Because game participants learn in
different ways, not all participants are able to reﬂect deeply on the experience gained during
the game; therefore, debrieﬁng has been recognized as an important addition to game- and
simulation-based learning that helps teachers to achieve curriculum objectives (Crookall,
1995; Peters and Vissers, 2004; de Freitas and Oliver, 2006;Hmelo-Silver et al., 2015;Danish
et al., 2017). GBSD builds on the structural debrieﬁng that was developed for a popular
management simulation (Pavlov et al.,2015).
Our initial protocol (initial GBSD protocol) was established based on a literature review
of system dynamics modeling (Pavlov et al.,2015;Richardson, 2014;Morecroft, 2007;
Richardson and Pugh, 1981) as follows:
Step 1: Play and analyze game. Students play the game and discuss its rules and
Step 2: List variables. Students identify and list variables that make up the
underlying game structure.
Step 3: Draw and discuss behavior-over-time graphs. Students gain insight into the
system’s behavior by drawing and discussing behavior-over-time graphs (BOTGs),
also known as reference modes. BOTGs, commonly used in systems thinking, are
graphs that show variable behavior over time. They capture historic patterns as
well as desired and undesired future scenarios. See Appendix for an example of a
Step 4: Identify momentum strategies. To uncover their potential misconceptions
about the system, students document their momentum strategies. A momentum
strategy is a particular strategy (or action) that students select as their “default”to
achieve a desired outcome. In the subsequent steps, students improve their
understanding of the underlying game structure and reexamine momentum
Step 5: Construct causal loop diagram. Students represent the web of interactions
between variables by drawing causal loop diagrams, including key variables and
feedback loops. See Appendix for an example of a causal loop diagram.
Step 6: Construct system dynamics model. Using special modeling software (e.g.
Insightmaker), students build a computational system dynamics model. This model
represents the system in greater detail than the causal loop diagram because it
distinguishes between stock and ﬂow variables, and also uses mathematical
equations or graphical functions to deﬁne relationships between variables.
Step 7: Validate model. Students run a series of simulations to assess the model
Step 8: Test strategies. Students experiment with the model to discover new
strategies that improve their in-game performance.
Step 9: Prepare report. Students prepare a report in which they communicate the
underlying system structure of the game, describe their model, and reﬂect on how
their strategies evolved from their initial momentum strategies.
This initial protocol involves activities that allow students to relate game components (e.g.
goals, rules, and core mechanics) to the system’s structure (e.g. causal relationships,
feedback loops, accumulations and delays) and behavior (population dynamics). These
activities are consistent with the view that the best way to learn about a system is to build a
model of the system and then explore its behavior by running simulations with the model
(Alessi, 2000;Größler et al., 2000;Hashem and Mioduser, 2013;Yoon et al., 2016;Hmelo-
Silver et al.,2017).
While the structural debrieﬁng protocol was initially developed and piloted for use with
college-level instructional simulations (Pavlov et al., 2015), its utility and usability for K-12
contexts was unknown. Also, while the initial protocol assumed that the skill level and
completion time vary for each step depending on the instructor’s and students’familiarity
with the game and systems thinking tools, further investigation was needed to understand
how teachers would adapt this protocol to best serve their instructional needs. Therefore, we
investigated how this protocol can be used as a curriculum design tool for teachers to make
a digital game more relevant for their curricular needs while also teaching systems thinking
skills. Here we report our iterative design processes and how three middle school teachers
used the framework to implement a game-based curriculum.
Food ﬁght game
Food Fight is a two-player online game developed by BrainPOP to teach about ecosystems
in a middle school biology curriculum (Pavlov et al., 2019). The game represents a complex
system in which two species compete for limited food and habitat resources while the
players strategically add one new species to the ecosystem per turn, increase the population
of any species already in theecosystem or play a wild card which introduces a random event
into the ecosystem, such as poachers or a drought (Figure 1). The objective for players is to
sustain and grow their respective animal populations. For example, a pair of players may
pick the Eagle and Rhino as the competing species. Status bars next to avatars provide
information on the size of the species’population. Players can obtain information about each
species’predators and prey by rolling over cards at the bottom of the game screen. It takes
about 15 min to play one round of the game.
This study uses a design-based research methodology (Collins et al.,2004;McKenney and
Reeves, 2012;Thompson et al.,2017;Peppler et al., 2018) with the goals of validating GBSD
and investigating the utility of GBSD across different contexts (e.g. school contexts and
teacher proﬁles) as a design source for developing game-based curriculum. In this article, we
address the overarching research question of:
RQ1. How is the GBSD framework used as a design resource during the development of
game-based curricula for systems thinking?
Over the course of 12 months, we conducted one focus group interview with two master
teachers and three design workshops with three participating teachers and two master
teachers. Between the workshops, the team rapidly iterated the framework as well as
curricular materials in collaboration with the teachers. There are three sources of data that
we are pulling together to describe iterations of GBSD. First, we review notes by the team
captured during the focus group interview and design workshops. Second, we took ﬁeld
notes as well as recorded video and audio during teachers’implementation of the
curriculum. Finally, we analyze teacher exit interviews conducted after the curriculum
We recruited two master teachers and three science teachers. Science teachers were recruited
through an advertisement via the Massachusetts Science Education network. They were
selected based on two criteria. First, their interest in the project and willingness to use games
in their science curriculum, and second, their interest in learning more about systems
thinking and helping students to practice systems thinking skills in the life sciences
curriculum. Table I summarizes teachers’backgrounds and experiences. All teachers were
Design process and products
First iteration of the game-based structural debrieﬁng framework
To validate the initial version of the GBSD protocol, we ﬁrst conducted a focus group
interview with two systems thinking Master Teachers. We asked them about their overall
impression of the initial protocol and how they thought the protocol could be applicable or
not applicable for science teaching at the middle school level. The Master Teachers and the
authors of this article played the Food Fight game multiple times and analyzed it together to
understand the affordances and limitations of the game that might be coupled with the
protocol. In particular, we thought about ways that Systems Dynamics tools such as
behavior-over-time graphs, causal loop diagrams and stock-and-ﬂow models can be created
A lead educator in a non-proﬁt organization dedicated to teaching systems thinking in K-12
A retired public high school biology teacher with an MA in Biology/Environmental Policy.
He has been developing and teaching science curricula that incorporate systems thinking
and computer modeling for over 25 years
Teacher 1 A teacher at an urban, all boys, public charter school. He is a recent graduate from an urban
science teacher preparation program and this was his second year teaching science at a
middle school. While he occasionally used educational games and simulations in his classes,
he never used formal game-based curricula. He had no training in systems thinking
Teacher 2 A life sciences teacher at an afﬂuent suburban public school. He majored in biology in
college, has a Master’s degree in science, and pursued a PhD in neuroscience. He has more
than 10 years of teaching experience in the same district, although he did not go through a
formal teacher preparation program. He had no training in systems thinking
Teacher 3 A teacher at a suburban public school, who taught engineering. He worked as an industrial
engineer for more than 30 years and began his teaching career a few years ago. Because of
his engineering background, he likes to experiment and use various technologies in his
classes. He had not used games in his classes before this project. He did not have formal
teacher training and he had no prior knowledge about systems thinking. He had the same
students in his engineering class once a week
for this game and then used within the limited available time of a typical middle school class
by teachers with little or no prior systems thinking training. The group discussed the
following changes to the protocol.
Reduce the complexity by gradually introducing GBSD elements: Master Teacher 1 pointed
Combining the game with the GBSD protocol with the full complexity might be overwhelming for
middle school students. Therefore, you should consider having students play the game in the
simplest form ﬁrst, for example, the teacher preselects the two creatures that students play.
Master Teachers also pointed out that teachers need to provide sufﬁcient opportunities, even
before introducing visualization tools, for students to understand the game itself, to allow
students to start thinking about the rules of the game and identifying variables in the game.
Master Teacher 2 also suggested that teachers should ask prompting questions, so students
can analyze the game. The questions that teachers can ask are: What is the problem? How do
you win the game?
Align the gameplay goal with the curriculum goal: The two Master Teachers noticed that
the game affords multiple learning outcomes. Master Teacher 1 suggested that Food Fight
could provide the context to teach the notions of complexity and equilibrium. Similarly,
Master Teacher 2 noted that the game displays the respective populations at the given
moment, and calculates player scores which are distinct from the population size (Figure 1).
The score increases when the population increases and it also increases for each period that
the population persists, even if the population does not increase. Every period the population
declines, points are taken off. As a result of this setup, while the population could remain
constant, the player could still be increasing her score. Therefore, the teacher could choose
whether to concentrate on the population or the score as the goal of gameplay to closely
align the overall goal of the curriculum and how the structural debrieﬁng is introduced.
Introduce a variety of visualization tools: The Master Teachers had speciﬁc suggestions
regarding what system visualization tools –e.g. stock-and-ﬂow diagrams, connection
circles, and behavior-over-time graphs –could be used over the course of gameplay. Chosen
systems tool should help students better understand the complex ecosystem represented by
the game and possibly help the students come up with better gameplay strategies based on
their deeper understanding of how the game works. Master Teachers suggested that some
systems tools might be an “overkill”for this context as middle school teachers might have
very limited class time to fully introduce these tools to students. For instance, Master
Teacher 2 suggested that students need to have a list of variables in the game –the list could
be either created by students or given to them by the teacher. As they play the game,
students would track values for each variable by recording them in a worksheet. Master
Teachers also suggested that using BOTGs could be a concrete way for students to track the
changes of the variables and how the variables interact with each other. Master Teachers
thought that studentsshould draw connection circles.
The research team and Master Teachers had an extensive discussion about introducing
the stock-and-ﬂow diagrams and computational models because the system dynamics
community believes it is such an important step towards deeper understanding of systems
(Größler et al.,2000;Alessi, 2000). However, the Master Teachers had conﬂicting opinions
about asking students to build stock-and-ﬂow models (Appendix) in the context of GBSD.
First, Master Teachers recognized that given students’level of modeling skills, building a
functional stock-and-ﬂow model would require more time than the limited time available for
the unit. Master Teacher 1 believed that drawing a simple causal loop diagram would be
sufﬁcient, while Master Teacher 2 thought that the curriculum should at least introduce the
stock-and-ﬂow diagram. Besides class time, another overarching concern was ensuring that
teachers themselves would be comfortable with system dynamics modeling. The research
team and Master Teachers were keenly aware of the research and anecdotal evidence that
suggested that embedding system dynamics in science curriculum might be challenging for
teachers who lack experience with system dynamics concepts, terminology, and tools
(Lyneis and Fox-Melanson, 2001;Skaza et al., 2013). During this meeting, several software
options for building stock-and-ﬂow models were discussed including Forio, Netlogo,
Insightmaker, iThink and Vensim. Eventually, it was decided with some regret that
building a computational model would not be part of the new iterated version of the
protocol. However, in recognition that building computational models fosters learning and
systems thinking (Clement, 2000;Hashem and Mioduser, 2013;Puttick and Tucker-
Raymond, 2018), stock-and-ﬂow models were left optional for situations when teachers
would have more time and are familiar with system dynamics modeling.
Based on the input from the focus group interview, we iterated the GBSD protocol, as
shown in iterated GBSD protocol (explanations of the visualization tools can be found in the
Appendix) as follows:
Step 1: Play a short and simple version of the game.
Step 2: Discuss with class –What is the goal of this game? Repeat this as the goal
will change over time.
Step 3: Ask students to list variables of the game (e.g. hare, eagle, bush, grass).
Step 4: Create a table with these variables.
Step 5: Play a longer version of the game.
Step 6: Students track the behavior changes of the variables.
Step 7: Students draw BOTGs for several variables.
Step 8: Students create a connection circle (see Appendix for an example) and then
discuss connections between variables.
Step 9: Pull out a causal loop diagram from the connection circle.
Step 10: Discuss what is happening in the causal loop diagram, discuss feedback
embedded in the causal loop diagram.
Step 11 [optional]: Create a stock-and-ﬂow model. Including this step depends on the
teacher’s learning objective. If the learning objective is understanding complexity,
then the causal loop diagram is sufﬁcient. Learning about stocks and ﬂows can be
an additional learning objective.
Step 12: Replay the game.
Step 13: Prepare a report.
Co-design of game-based curriculum using game-based structural debrieﬁng
Over three in-person design workshops, the team and participating teachers spent a great
deal of time learning about the game and creating curricular materials for the GBSD
protocol, such as BOTGs and connection circles. While not intended to be shared with
students, one of the teachers prepared a detailed document that described the game
mechanics, rules and parameter values that he deduced from playing the game multiple
times; the document was available to the rest of the team. Over several months, we co-
designed a game-based curriculum unit around the iterated GBSD protocol (iterated GBSD
protocol). During this process, the protocol underwent further modiﬁcations (ﬁnal GBSD
protocol) as follows:
Step 1: Gain basic understanding of the game context, including relevant science
vocabulary and concepts.
Step 2: Play a simple version of the game (pre-selected animals).
Step 3: Discuss the goal of the game and game strategies.
Step 4: Play the game again (students pick their own animals).
Step 5: Ask students to identify variables of the game (e.g. hare, eagle, bush, grass).
Step 6: Students track the behavior changes of the variables using the causal
worksheet (Figure 2 for an example).
Step 7: Students learn about the connection circle diagram.
Step 8: Students create a connection circle and then discuss connections between
Step 9: Students learn about feedback loops.
Step 10: Students play the Living Loops Activity.
Step 11: Students learn about the causal loop diagram.
Step 12: Students identify feedback loops in the connection circle that was created
Step 13: Students combine feedback loops into a causal loop diagram.
Step 14: Using the causal loop diagram, students discuss complex indirect causality
and the effects of reinforcing and balancing feedback loops in the game.
Step 15: Replay the game.
Step 16: Students discuss what they learned in this unit.
We dropped BOTGs, decided against having the students build a computational model, and
increased gameplay time.
The initial and iterated GBSD protocols (initial GBSD protocol and iterated GBSD
protocol) implied that students would draw BOTGs for several variables. However, the team
realized that drawing BOTGs for Food Fight would make little sense. The purpose of
BOTGs is to document system behavior by plotting variable trajectories that can be later
explained by examining system structure (Quaden et al.,2015). For example, without
external shocks, a reinforcing loop would generate exponential growth. However, because in
Food Fight, the players continuously shock the system by adding species and changing
species population, it is impossible to attribute variable trajectories to the structure of the
game system rather than the player-introduced shocks.
We describe the ﬁnal version of the unit below. Each of the ﬁve lessons incorporates 1-3
elements from the ﬁnal GBSD protocol (ﬁnal GBSD protocol). The curriculum unit integrates
The causal worksheet
that shows “a zebra-
students in one of
Teacher 2’s classes
system dynamics visualization tools and the video game with middle school life science
Lesson 1 –Introduction of the African Savanna Ecosystem: Very early in the project, the
teachers pointed out that middle school students have limited knowledge of ecosystems in
general and the African Savanna ecosystem in particular; therefore, it became clear that
even before the game is played, the class would need to spend some time learning about the
ecosystem. Lesson 1 is intended to help students gain a basic understanding of the savannah
ecosystem and learn concepts and vocabularies of the ecosystem. Students conduct online
research of the African Savanna and examine examples of organisms and their relationships
that constitute complex food chains.
Lesson 2 –Explore the Ecosystem in Food Fight: This lesson provides an opportunity for
the students to become familiar with the Food Fight game. They play one round of the game
for two selected organisms, followed by a discussion. During the discussion students
identify goals, rules, the mechanics of the game, and list the variables in the game. They
play the game again.
Lesson 3 –Connection Circles: Students learn about the connection circle diagram, which
is a tool for capturing multi-variable causal relationships (see Appendix). In pairs, students
play the game to collect data on interactions that are recorded in a causal worksheet
(Figure 2) prepared by the team. These data are then used to create connection circles that
document relationships between variables in the game. Using connection circles was
recommended by Master Teachers —connection circles were not part of the initial GBSD
protocol (Initial GBSD Protocol). Educators found connection circles useful for K-12 teaching
(Quaden et al.,2015).
Lesson 4 –Feedback Loops: Students are introduced to more systems thinking tools and
concepts such as the causal loop diagram and positive (or reinforcing) and negative (or
balancing) feedback loops. To understand reinforcing and balancing feedback loops, Master
Teachers suggested that students play the Living Loops Activity (Sweeney and Meadows,
1995), which is an experiential way of exploring the dynamics of feedback loops.
The teacher guides students in extracting feedback loops from the connection circles that
they created in Lesson 3. Then, these individual feedback loops are combined into causal
loop diagrams that visualize the complex causal relationships among the organisms within
the ecosystem of the video game. The teacher leads a discussion to help students learn how
to use the causal loop diagram to think about the ecological system represented by the game.
After discussing possible new strategies, students play the game again.
Lesson 5 –Putting It All Together: A comprehensive report was the ﬁnal part of the
structural debrieﬁng protocol piloted at the college level by Pavlov et al. (2015). However,
owing to the limited classroom time and the young age of the students in this study, we
decided that during the ﬁnal lesson, the class would discuss the African Savanna ecosystem
using the systems thinking concepts and tools, which they would have learned in previous
Implementations of the curriculum
The unit was implemented by the three teachers who participated in the co-design
workshops (Table I). The implementations conﬁrmed the ﬂexibility of the unit because
teachers created additional instructional materials that supplemented the protocol and
addressed the unique limitations and needs of their classrooms. For example, Teachers 1
and 2 covered the material in ﬁve consecutive days, but Teacher 3 extended the unit to 10
non-consecutive days that spanned two months, which was because of the winter school
break and the fact that he had the same students in his classroom only once a week. In this
section, we describe lesson implementations and how each teacher further modiﬁed the
lesson plans based on their students’needs.
All teachers spent Lesson 1 by having students individually research online the African
Savanna ecosystem including animals and plants. After the research, Teacher 1 asked the class
questions to explore relationships between the species (e.g. “What would happen to creature X,
if creature Y’s population increased?”); however, most of the students could not explain the
relationships. Teacher 1 also spent some time covering ecosystem vocabulary, as his students
were unfamiliar with many of the terms. Overall Teacher 1 felt that students were engaged and
thelessongavethemsomesenseoftheSavannaecosystem and predator-prey relationships.
Teacher 2 implemented the unit with four cohorts over ﬁve consecutive days, one 40-min
lesson per day. Teacher 2 began each cohort with a 5-10 min introduction to the lesson and
shared with the students that the goal of this lesson was to learn how to apply systems
thinking skills to understand organism interactions within an ecosystem. In contrast to
Teacher 1, he had the students research the animals of the African Savanna, including their
predator-prey relationships, in pairs, one laptop per pair. After students completed the
research with 3-8 creatures, Teacher 2 spent some time reviewing the food chain
terminology and gave an example of a food chain. He then had students create a food chain
from their research. Some students did not place the producer at the bottom of the chain or
did not have food relationships in the correct direction. When Teacher 2 asked students later
in the week if this lesson was useful, their average response was that it helped only slightly.
As a result, Teacher 2 suggested that he would skip this activity in the future or he would re-
design the research activity. For example, he thought of grouping the animals into
herbivores, carnivores and omnivores, which might help students to see the predator-prey
relationships. Also, in light of the time constraint (students had about 20 min for research),
the teacher proposed restricting research to those animals that are on the African Wildlife
Foundation website instead of spending a lot of time searching the Internet for images.
Teacher 3 did not follow the sequence of activities as outlined in the unit. Teacher 3 spent
two class periods covering Lesson 1. He introduced additional materials to explain predator-
prey relationships. In addition to the research, his students individually completed tree chart
diagrams from the BrainPOP website that showed the food chain. For example, grass and
oak trees were shown as producers, squirrels were shown as primary consumers, and a
hawk was recorded as a secondary consumer. Additionally, Teacher 3 distributed a link to
an online template that he prepared for students; the template was organized as a slide set.
Students referred to these slides during the entire unit to answer the following questions:
Q1. What animals were included in your ecosystem? Which ones were producers?
Which were primary consumers? Which were secondaryconsumers?
Q2. How did introducing or adding a new organism to the ecosystem affect other
Q3. How is your connection circle a representation of the ecosystem?
Q4. If you removed something from the circle, what would be the effect on the other
species in the ecosystem?
Q5. What reinforcing and balancing loops occurred in your ecosystem?
Q6. What have I learned about the ecosystem in the game?
Q7. How has playing the game with systems thinking tools helped me to understand
how the ecosystem works?
Q8. What are the three lessons that youlearned in this unit?
Teacher 1 ﬁrst had students play a round of the game by preselecting Rhino and
Hippopotamus. Because the teacher did not tell students the goal of the game and how to
play it, some students were confused. For example, some students did not realize that the
goal was to increase animal populations. After playing the ﬁrst round of gameplay, Teacher
1 debriefed with students about their experiences and strategies that they used. After the
debrieﬁng, students freely played the game again. Students were more engaged during the
second round. During the follow up debrieﬁng, the students began to connect ecosystem-
related strategies with the goal of the game. For example, students talked about adding
predators that ate their opponent’s animals. A student observed that increasing competition
for a resource meant that both competing populations were going to decrease.
Teacher 2 began by reviewing the tutorial provided by BrainPOP. Teacher 2 instructed
students to choose a Hare and an Elephant and then play for 5min. When students ﬁnished,
the teacher went over concepts important for the game, including the concept of a variable.
Students and Teacher 2 discussed variables in our lives, and then deﬁned variables in the
game. Teacher 2 asked students to discuss what causes population to increase or decrease.
As the last activity, the teacher asked students to play one more round of the game with any
animals. Some teams added many species. The teacher asked students to state the goal of
the game. Students talked about the following three distinct goals: the score, the population,
and trying to kill the competing animal so they can win. For example, one student said to his
partner: “I put animals that eat your food!”The class discussed game strategies. Students
asked questions such as “Wouldn’t any animal always win over an elephant because
elephant eats more than any other animal?”(Answer: No). “Can you take away population?”
Teacher 3 requested that students play the game and then immediately tested their
observations and thoughts about the gameplay. Each student had to provide written
responses to prepared questions about the goal of the game, strategies used by students and
the mechanics of the game. To ensure that students clearly understood the game, Teacher 3
asked questions such as “How can you identify predation relationships based on the
organism card?”The class began exploring the causal relationships between species, when
the teacher asked, “What happened to X when you added Y?”
Teacher 1 started his third class by having students hypothesize how baboons, rabbits,
fruits, available space, owls, tall grass and termites were related in the game. The teacher
then led the whole class in a discussion to create a connection circle for those variables
(Appendix). To help students think about the connection circle as a tool to understand the
complexity of the system, Teacher 1 emphasized that before tracing connections from
multiple organisms, they should look at the whole circle and think about how intertwined
the ecosystem was. The teacher then ran through examples using the circle of what
would happen if they removed or added an organism. Then students played the game again
and worked on their connection circles. Teacher 1 thought that students would be more
engaged if they picked their own species. Yet, he also acknowledged that it was more
difﬁcult to see how well the entire class understood the connection circle as everybody ended
up with different ecosystems.
Teacher 2 also implemented Lesson 3 on Day 3. He had his students create their own
connection circles based on data from the Food Fight game. He preselected Termite vs
Baboon for a game that the entire class played together. For turn 1, he added grass to the
ecosystem. For turn 2, he clicked on the grass to add more grass. This brought the grass
population up to 10 units. Then he told the students that he was going to add zebras. He
asked students to show with their thumbs what they thought was going to happen to the
grass population. They predicted a decrease. To their surprise, the population of grass
increased to 11 when the zebras were added. Some students asked why. The teacher told
them that they would revisit this after they collect their data. Student groups recorded the
result on their causal worksheet (Figure 2). Then students built their own ecosystems (all
students were playing with the same organisms because they picked Termite vs Baboon).
The teacher told students to pay attention to one or two organisms per turn, to see if they
changed when the players added an organism of their choice. Students played for about
10 min and most students were able to complete about15 lines of the causal worksheet.
Once ﬁnished with the game, the students created connection circles based on their
observations as recorded in the causal worksheet (Figure 2). The teacher told the students,
“Using your data, draw connections and label the arrows as 6depending on the effect you
saw.”This caused a bit of confusion, because in some cases students observed an increase in
predators apparently leading to an increase in prey. Some students stuck to the prompt and
created connections that reﬂected their data even when it went against what they knew
about the predator-prey relationships. Others chose to ignore the causal worksheet and
made connections that made sense to them.
Although the lesson accomplished the goal of introducing connection circles and the
connection circles led to some very interesting discussions, Teacher 2 thought it was hard
for students to see how the connection circles related to the game. Also, some students
wanted to know if their connection circles were right or wrong, particularly with respect to
the mysterious “when zebras increase, grass increases”kinds of cases. Teacher 2, however,
did not explicitly address this confusion because he was not sure why that was happening in
the game either.
Teacher 3 implemented Lesson 3 on Day 5. Students played the game and ﬁlled out the
causal worksheet. They played two scripted versions of the game: Zebra vs Monkey and
Leopard vs Lion. In the causal worksheet, students recorded the effect on the other species of
the actions taken during the game. Then students, working in pairs, completed the
connection circles using the information from the causal worksheet. For the connection
circle, students identiﬁed no more than 10 variables and then they showed the polarity of
Teachers 1 and 2 spent Day 4 helping students to understand what reinforcing and
balancing loops are and identifying those loops within the game. Teacher 2 asked students
to document direct interactions between species with arrows, including whether it is a
positive or negative connection. Then the teacher asked the class, “Now I want you to
identify a closed loop in your connection circle. Follow the pattern of arrows and see if it
comes back to where it started.”Teacher 2 asked if there are any resource limits that would
form balancing feedback loops. Students suggested other creatures, availability of land, and
environmental variables such as water. Most of the students were able to connect feedback
loops to the ecosystem but others were having a tough time with it. After the students
identiﬁed balancing loops from the connection circles, the teacher asked, “Did any of you
ﬁnd reinforcing loops?”Students had difﬁculty identifying reinforcing loops. While working
in pairs, a student remarked, “I don’tﬁnd any reinforcing loops.”This was not surprising,
as there were no reinforcing loops visible in the connection circles. The teacher talked about
possible reinforcingloops in ecosystems.
To experience feedback loops, students played a physical activity called Living Loops
(Plate 1). For this activity, students stand in a circle, holding hands, while wearing a
notecard with either a “þ”or a “–” on it, and move their hands up or down as they receive
“signals”from the person next to them. To experience a reinforcing loop, all student wear
the “þ”notecards, which means that they have to match the signal they receive through one
hand by moving the other hand in the same direction. For a balancing loop, one student in
the circle wears a “–” notecard, which means that he has to ﬂip the signal and move his other
hand in the opposite direction from the received signal.
After the Living Loops activity, Teacher 2 asked, “Can you keep going forever? What is
going to slow down?”The student correctly responded that the growth would stop “[...]
because of the limited resources.”Then teachers asked students to identify balancing
feedback loops in their lives. Students offered perfect examples of balancing loops. One
student said, “Play a video game. Get bored [...]”Another student suggested that when his
room gets dirty, “[...] mom tells you to clean.”
At the end of class, students and Teacher 2 discussed some of the “unexpected”results,
such as the zebra-grass puzzle, which was discovered during the previous lesson. The
students observed earlier during gameplay that adding zebras to the game led to more
grass. The students came up with several feedback explanations for the zebra-grass puzzle.
In one explanation, students hypothesized that zebras fertilize the grass. That would lead to
a reinforcing loop, as indicated by the letter R, which stands for “reinforcing,”in Figure 3.
Interestingly, this was not the case as fertilization of grass through droppings was not
modeled in the game.
Teacher 3 covered Lesson 4 on Day 7. He had completed the Living Loop activity during
one of the preceding days. Students developed responses to the following questions using
Google slides. The ﬁrst question required students to submit their connection circles and
feedback loop diagrams. Then they were asked to explain why they played the game
differently based on their understanding of how the system behaves. Students also wrote
about what they learned about the ecosystem. They concluded by providing personal
insights related to playing the game with systems thinking tools.
Students play the
activity Living Loops
during Lesson 4
Day 5 was intended to bring everything together, with students preparing ﬁnal PowerPoint
presentations. Teacher 1 also gave students homework where they reﬂected what was
helpful or not helpful for them to understand systems thinking concepts in the game.
Teacher 2 began the lesson by reviewing balancing and reinforcing loops. Then he used
a“beads activity”that he invented to discuss relationships between species with beads. He
used two examples. The ﬁrst example (Figure 4) showed a reinforcing loop, which
considered the effect prey has on its predator; the shown loop is closed because of the
assumption that doves fertilize the soil, which leads to more grass. The teacher then gave
each pair of students a sheet of graph paper with a dashed line indicating the starting
population. He then had them place four beads, representing each organism’s population on
the dashed line. The class then walked through step-by-step what happens when one
population increases. For example, if grass goes up, then termites go up, if termites go up,
praying mantises go up, etc. In debrieﬁng notes, the teacher wrote, “I found this [beads
activity] helpful because I felt like I did a poor job linking the Living Loop to an ecosystem
the day before.”The class then looked at a balancing loop (the zebra eats the bush in which
the praying mantis lays its eggs). Student groups worked on their own, moving the beads up
or down, to prove that it was a balancing loop. The teacher found that he had to correct some
identiﬁed by students
on a connection circle
that uses beads to
in a corresponding
The class discussed strategies for the game, which included anything that students learned
about systems. For instance, one student said:
Mack and I are competitive, and back in my mind, I used the connection circle, and I imagined
what would be negative for him. His species was the elephant and I knew that it was [an]
herbivore and I added another herbivore to decrease [the] amount of food available to the
Another student said, “I looked at the food and then placed the food only I can eat.”Students
noticed that bigger omnivores, such as the Ostrich, Rhino and Elephant, usually could get
more points because nobody can eat them. Students used the scientiﬁc language of the
ecosystem to describe the strategies they used. Examples of student comments were: “I
introduced predators that ate the other person’s species.”“When I ﬁrst started, I didn’t know
how to play, but this time I am paying attention to predators and prey.”Another student
said, “I tried to do a reinforcing loop with termite and grass, but praying mantises died too
much. It was working otherwise.”
Teacher 3 started Lesson 5 on Day 7 and continued it on Days 8, 9 and 10. Teams of two
students worked on their presentations throughout the unit and then presented to class on
Day 9. Teacher 3 asked students to ﬁll out a reﬂection form, which asked:
In what way did each of the following improve your understanding of food webs and the game
Food Fight? How did each of the following cause you to play the game diﬀerently? If it wasn’t
helpful, describe what was confusing or what you would change to make the lesson tie into your
understanding of food webs.
Students found vocabulary presentation helpful, as well as the African Savannah graphic
organizer, connection circles, and causal worksheet. Students thought that the connection
circle was helpful because it showed how species were connected even indirectly.
It appears that a common perception among students in Teacher 1’s and Teacher 2’s
classes was that reinforcing loops were more difﬁcult to understand than balancing loops.
One student wrote regarding reinforcing loops: “It took me a while [...] to fully understand
and I still have a lot of questions.”Another student showed some confusion about the
concept of reinforcing loops:
One example of a reinforcing loop is a lion and fruit because if there is more fruit, then they would
be more animals eating that food and the lion would then eat the animals and there would be more
This student explained complex positive causation rather than a reinforcing loop. Regarding
balancing loops, a student wrote: “I think this was the easiest to understand and give
examples of.”But another student showed the misunderstanding of the balancing loop
concept by not completing the loop but rather talking only about the complex causation:
“One example of a balancing loop is an eagle and a worm. This is because if there is [sic]
more worms, there will be more eagles.”Additionally, this student was confused, thinking
that eagles eat worms.
This project aimed to develop a new framework called GBSD for teaching systems thinking
with video games within the STEM curriculum. The teachers appreciated the utility of
GBSD as a concrete framework to incorporate systems thinking in their regular curriculum.
They felt that GBSD allowed them to elevate the level of discussion in an ecosystem unit.
One teacher commented that he felt that systems thinking, in particular the feedback loops,
was a powerful framework that he wanted to use more in the classroom. Teacher 2 said:
[...] I enjoyed teaching systems thinking and I’m excited that I now have connection circles and
feedback loops as tools I can use when we discuss other topics this year.
A teacher suggested “bundling”systems thinking with more STEM courses –“Can we
make this into a STEM approach so we can get systems thinking across more types of
Teachers agreed on the beneﬁts of using a video game as the context in which to engage
students with systems thinking. Teacher 1 commented about students, “[...] they were able
to make some pretty amazing connections.”Teachers noted that the competitive style of the
game worked well for the purpose of making theunit fun and exciting. One teacher noted:
[...] I think what was also cool is that some of the kids [...] chose to play it cooperatively. They
wanted to build this massive ecosystem to see what would happen. So there was this great
curiosity that went beyond the competitive part [...]
Teachers reported on challenges during the implementation. First, the goal and mechanics
of the game were not always clear, which led one teacher to say, “It was very hard to make
good decisions about playing the game.”Second, some students played the game without
carefully using the systems thinking strategies, even though if they introduced many
species, they’d start losing track of causal connections. A teacher reported that, “I was
wondering if the students are hacking their way through it or are they analyzing their way
through it?”In addition, the experience of this project suggests that to increase the
affordances of video games for teaching systems thinking, game designers may need to
consider how the game mechanics, goals, and interface of the game provides opportunities
for players to understand the systems’behavior and structural complexity. Especially for
the case of Food Fight, the overall complexity of the ecosystem created by players
inﬂuenced the score. Yet it was unclear to students and teachers how scoring worked, and
this was frustrating for the teachers.
Teachers also reported challenges related to developing their own deeper understanding
of the systems thinking concepts. For instance, Teacher 2 said during the exit interviews:
In my head I thought I understood everything, having to then explain it in front of a classroom
and access the vocabulary at the right time and the right place and in the right context was
A teacher suggested that there could be curriculum coordinators, who could be ST
champions, going from class to class, helping teachers with their respective units. Teacher 2
suggested that if the material is challenging for teachers, it must be also challenging for
students. Teachers also pointed out that because systems thinking is not part of
standardized tests, teachers might be less motivated to incorporate systems thinking into
the curriculum. As Teacher 3 put it, “[...] the teachers must see the direct link to the
standards of the NGSS.”However, teachers agreed that there should be more opportunities
for the kids to use systems thinking tools.
This article makes a unique contribution to the educational literature by introducing the
GBSD, which is a set of debrieﬁng activities that use visualization tools from the academic
discipline called system dynamics to support systems thinking as a distinct goal of
gameplay and learning. GBSD facilitates the classroom exploration of the structure of
complex systems by helping students to identify system variables and visualize causal webs
as well as feedback loops between the variables. To the best of our knowledge, no previous
study attempted to create curriculum that combines an off-the-shelf educational game with
tools of system dynamics (see Appendix for a brief review of system dynamics tools).
While this study tested GBSD with a single game, our hope is that a wide range of
educational games can be paired with the GBSD protocol. Moreover, GBSD can be
integrated with the design process when students build their own games about complex
systems such as climate change (Puttick and Tucker-Raymond, 2018) or biological systems
(Danish et al.,2017;Thompson et al., 2017). GBSD can assist the emergence of
understanding of thegame as a system problem.
Because, in general, teachers are not familiar with system dynamics, they may require
specialized professional development that would introduce them to GBSD. Such training
would take into account that teachers may appropriate the same curriculum differently
depending on varying teaching styles and distinct comfort and familiarity levels with the
material (Hmelo-Silver et al.,2015). Whether or not different teaching strategies and
implementations would lead to similar learning outcomes can also be tested.
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Appendix. Systems dynamics tools
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