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Game-based structural debriefing: How can teachers design game-based curricula for systems thinking?



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 and learning when they use available educational games in the classroom. 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 teachers. The authors developed a curriculum unit that integrates systems dynamics visualization tools and a video game for middle school life science ecosystem 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. 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 educational game. This paper illustrates how a design framework can be used to support teachers’ thoughtful integration of games in curriculum development.
Game-based structural debrieng
How can teachers design game-based
curricula for systems thinking?
Yoon Jeon Kim
Playful Journey Lab, Massachusetts Institute of Technology,
Cambridge, Massachusetts, USA, and
Oleg Pavlov
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 debrieng
(GBSD) to leverage the affordances of video games for teaching systems thinking. By integrating system
dynamics visualization tools within a set of debrieng 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 conrmed the exibility 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 debrieng activities
developed for an off-the-shelf educationalgame. This paper illustrates how a design framework can be used to
support teachersthoughtful integration of games in curriculum development.
Keywords Systems thinking, STEM, System dynamics, Game-based learning,
Design-based research, Video games, Causal diagrams, Structural debrieng
Paper type Research paper
The benets 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 nancial 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.
curricula for
Received 7 May 2019
Revised 28 July2019
Accepted 10 September2019
Information and Learning
© Emerald Publishing Limited
DOI 10.1108/ILS-05-2019-0039
The current issue and full text archive of this journal is available on Emerald Insight at:
outcomes including cognitive and interpersonal skills. Similarly, a literature review by
Boyle et al. (2016) found that games are benecial for learning of various outcomes such as
knowledge acquisition, affect, behavior change, perception, and cognition. Numerous
studies also reported academic domain-specicbenets 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 Centers 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 teacherspractices 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, xed class schedules and limited budgets.
Moreover, ensuring that the gameplay is relevant for both the classrooms context and
curricular needs is a signicant 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 teacherspedagogical
roles and practices in game-based curriculum (Foster and Shah, 2015), and only few
curriculum design frameworks exist that can guide teachersefforts.
Games for systems thinking
Systems thinking (ST) skills have been recognized as part of the core science literacy, and
more recently, as critical twenty-rst century skills. The Next Generation Science Standards
(NGSS) reect this perspective by explicitly infusing systems thinking concepts across the
standards. While this is a signicant 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
learnersunderstanding 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 boxto
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 studentssystems 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 debrieng
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 articial 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 eld has developed its own graphical
conventions and visual tools for making variables, system behavior, causal structure,
feedback loops, delays and stock-and-ow 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 debrieng
(GBSD) that leverages the affordances of video games for teaching ST by integrating system
dynamics tools within a set of debrieng activities. Because game participants learn in
different ways, not all participants are able to reect deeply on the experience gained during
the game; therefore, debrieng has been recognized as an important addition to game- and
curricula for
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 debrieng 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
systems 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 defaultto
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 ow variables, and also uses mathematical
equations or graphical functions to dene 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 reect 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 systems 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 debrieng 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 instructors and studentsfamiliarity
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.
Research method
Food ght 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 speciespopulation. Players can obtain information about each
speciespredators 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.
Study design
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 proles) 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?
Figure 1.
Food Fight
curricula for
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 eld
notes as well as recorded video and audio during teachersimplementation 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 teachersbackgrounds and experiences. All teachers were
from Massachusetts.
Design process and products
First iteration of the game-based structural debrieng framework
To validate the initial version of the GBSD protocol, we rst 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-ow models can be created
Table I.
Design workshop
Teacher 1
A lead educator in a non-prot organization dedicated to teaching systems thinking in K-12
Teacher 2
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 afuent suburban public school. He majored in biology in
college, has a Masters 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
out that:
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 rst, for example, the teacher preselects the two creatures that students play.
Master Teachers also pointed out that teachers need to provide sufcient 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 debrieng is introduced.
Introduce a variety of visualization tools: The Master Teachers had specic suggestions
regarding what system visualization tools e.g. stock-and-ow 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 overkillfor 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-ow 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 conicting opinions
about asking students to build stock-and-ow models (Appendix) in the context of GBSD.
First, Master Teachers recognized that given studentslevel of modeling skills, building a
functional stock-and-ow 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
sufcient, while Master Teacher 2 thought that the curriculum should at least introduce the
curricula for
stock-and-ow 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-ow 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-ow 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-ow model. Including this step depends on the
teachers learning objective. If the learning objective is understanding complexity,
then the causal loop diagram is sufcient. Learning about stocks and ows 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 debrieng
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 modications (nal 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 nal version of the unit below. Each of the ve lessons incorporates 1-3
elements from the nal GBSD protocol (nal GBSD protocol). The curriculum unit integrates
Figure 2.
The causal worksheet
that shows a zebra-
grass paradox
discovered by
students in one of
Teacher 2s classes
curricula for
system dynamics visualization tools and the video game with middle school life science
ecosystem curriculum.
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 nal part of the
structural debrieng 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 nal 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 conrmed the exibility 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 ve 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 modied the
lesson plans based on their studentsneeds.
Lesson 1
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 Ys 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 ve 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?
curricula for
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?
Lesson 2
Teacher 1 rst 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 rst round of gameplay, Teacher
1 debriefed with students about their experiences and strategies that they used. After the
debrieng, students freely played the game again. Students were more engaged during the
second round. During the follow up debrieng, the students began to connect ecosystem-
related strategies with the goal of the game. For example, students talked about adding
predators that ate their opponents 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 nished,
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 dened 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 Wouldnt any animal always win over an elephant because
elephant eats more than any other animal?(Answer: No). Can you take away population?
(Answer: No).
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?
Lesson 3
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
difcult 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 nished 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 reected 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 increaseskinds 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 lled 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 identied no more than 10 variables and then they showed the polarity of
Lesson 4
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
curricula for
identied balancing loops from the connection circles, the teacher asked, Did any of you
nd reinforcing loops?Students had difculty identifying reinforcing loops. While working
in pairs, a student remarked, I dontnd 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 þor a “–” on it, and move their hands up or down as they receive
signalsfrom the person next to them. To experience a reinforcing loop, all student wear
the þnotecards, 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 ip 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 unexpectedresults,
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 rst 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.
Plate 1.
Students play the
activity Living Loops
during Lesson 4
Lesson 5
Day 5 was intended to bring everything together, with students preparing nal PowerPoint
presentations. Teacher 1 also gave students homework where they reected 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
abeads activitythat he invented to discuss relationships between species with beads. He
used two examples. The rst 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 organisms 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 debrieng 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
Figure 3.
A reinforcing
feedback loop
identied by students
on a connection circle
Figure 4.
Abeads activity
that uses beads to
explain relationships
in a corresponding
feedback loop
curricula for
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 scientic language of the
ecosystem to describe the strategies they used. Examples of student comments were: I
introduced predators that ate the other persons species.”“When I rst started, I didnt 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 ll out a reection 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 dierently? If it wasnt
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 1s and Teacher 2s
classes was that reinforcing loops were more difcult 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 Im 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 bundlingsystems 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 benets 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, theyd 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 systemsbehavior and structural complexity. Especially for
the case of Food Fight, the overall complexity of the ecosystem created by players
inuenced 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 debrieng 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
curricula for
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.
Alessi, S. (2000), Designing educational support in system-dynamics-based interactive learning
environments,Simulation and gaming: An International Journal, Vol. 31 No. 2, pp. 178-196.
Baek, Y.K. (2008), What hinders teachers in using computer and video games in the classroom?
Exploring factors inhibiting the uptake of computer and video games,CyberPsychology and
Behavior, Vol. 11 No. 6.
Camara, A.S., Ferreira, F.C., Nobre, E. and Fialho, J.E. (1994), Pictorial modeling of dynamic systems,
System Dynamics Review, Vol. 10 No. 4, pp. 361-373.
Clark, D.B., Tanner-Smith, E.E. and Killingsworth, S.S. (2016), Digital games, design, and learning: a
systematic review and meta-analysis,Review of Educational Research, Vol. 86 No. 1, pp. 79-122.
Clement, J. (2000), Model based learning as a key research area for science education,International
Journal of Science Education, Vol. 22 No. 9, pp. 1041-1053.
Collins, A., Joseph, D. and Bielaczyc, K. (2004), Design research: theoretical and methodological
issues,Journal of the Learning Sciences, Vol. 13 No. 1, pp. 15-42.
Danish, J., Saleh, A., Andrade, A. and Bryan, B. (2017), Observing complex systems thinking in the
zone of proximal development,Instructional Science, Vol. 45 No. 1, pp. 5-24.
Davidsen, P., Kopainsky, B., Moxnes, E., Pedercini, M. and Wheat, D. (2014), Systems education at
Bergen,Systems, Vol. 2 No. 2, pp. 159-167.
de Freitas, S. and Oliver, M. (2006), How can exploratory learning with games and simulations within
the curriculum be most effectively evaluated?,Computers and Education, Vol. 46 No. 3,
pp. 249-264.
Divjak, B. and Tomi
c, D. (2011), The impact of game-based learning on the achievement of learning
goals and motivation for learning mathematics literature review,Journal of Information and
Organizational Sciences, Vol. 35 No. 1, pp. 15-30.
Forrester, J.W. (1958), Industrial dynamics: a major breakthrough for decision makers,Harvard
Business Review, Vol. 26 No. 4, pp. 37-66.
Foster, A. and Shah, M. (2015), The ICCE framework: framing learning experiences afforded by
games,Journal of Educational Computing Research, Vol. 51 No. 4, pp. 369-395.
Gee, J.P. (2003), What Video Games Have to Teach us about Literacy and Learning, Palgrave Macmillan,
New York, NY.
Größler, A., Maier, F.H. and Milling, P.M. (2000), Enhancing learning capabilities by providing
transparency in business simulators,Simulation and Gaming, Vol. 31 No. 2, pp. 257-278.
Größler, A., Thun, J.-H. and Milling, P.M. (2008), System dynamics as a structural theory in operations
management,Production and Operations Management, Vol. 17 No.3, pp. 373-384.
Hashem, K. and Mioduser, D. (2013), Learning by modeling (LbM): understanding complex systems by
articulating structures, behaviors, and functions,International Journal of Advanced Computer
Science and Applications, Vol. 4 No. 4, pp. 80-86.
Hmelo-Silver, C.E. and Azevedo, R. (2006), Understanding complex systems: some core challenges,
Journal of the Learning Sciences, Vol. 15 No. 1, pp. 53-61.
Hmelo-Silver, C.E., Jordan, R., Eberbach, C. and Sinha, S. (2017), Systems learning with a conceptual
representation: a quasi-experimental study,Instructional Science, Vol. 45 No. 1, pp. 53-72.
Hmelo-Silver, C.E., Liu, L., Gray, S. and Jordan, R. (2015), Using representational tools to learn about
complex systems: a tale of two classrooms,Journal of Research in Science Teaching, Vol. 52
No. 1, pp. 6-35.
Hovmand, P.S. (2013), Community Based System Dynamics, Springer, New York, NY.
Jacobson, M.J. and Wilensky, U. (2006), Complex systems in education: scientic and educational
importance and implications for the learning sciences,Journal of the Learning Sciences, Vol. 15
No. 1, pp. 11-34.
Kebritchi, M., Hirumi, A. and Bai, H. (2010), The effects of modern mathematics computer games on
mathematics achievement and class motivation,Computers and Education, Vol. 55, pp. 427-433.
Kirriemuir, J. and McFarlane, A. (2003), Use of computer and video games in the classroom,DiGRA
Conference. Nov 4-6, 2003, Utrecht.
Klopfer, E., Osterweil, S., Groff, J.S. and Haas, J. (2009), Using the technology of today, in the classroom
today: the instructional power of digital gaming and social networking and how teachers can
leverage it, An Education Arcade paper.
Lane, D.C. (2000), Diagramming conventions in system dynamics,Journal of the Operational Research
Society, Vol. 51 No. 2, p. 241.
Lane, D.C., Munro, E. and Husemann, E. (2015), Blending systems thinking approaches for
organisational analysis: reviewing child protection in England,European Journal of
Operational Research, Vol. 251 No. 2, pp. 613-623.
Lyneis, D.A. and Fox-Melanson, D. (2001), The challenges of infusing system dynamics into a K-8
curriculum,Proceedings of the International System Dynamics Society Conference, July 2001,
Atlanta, GA.
McKenney, S. and Reeves, T.C. (2012), Conducting Educational Design Research, Routledge, London.
Maani, K.E. and Cavana, R.Y. (2007), Systems Thinking, System Dynamics: Managing Change and
Complexity, Pegasus Communications.
Morecroft, J. (2007), Strategic Modelling and Business Dynamics: A Feedback Systems Approach, John
Wiley and Sons, Oxford.
Moxnes, E. (2000), Not only the tragedy of the commons: misperceptions of feedback and policies for
sustainable development,System Dynamics Review, Vol. 16 No. 4, pp. 325-348.
Nasakkala, E. (2000), Introducing simulation models into chemistry classrooms: a study in a nnish
senior secondary school with an international baccalaureate section, Research Report 201,
Department of Teacher Education. University of Helsinki, Helsinki.
Pavlov, O.V., Melville, N. and Plice, R. (2008), Toward a sustainable email marketing infrastructure,
Journal of Business Research, Vol. 61 No. 11, pp. 1191-1199.
Pavlov, O., Saeed, K. and Robinson, L.W. (2015), Improving instructional simulation with structural
debrieng,Simulation and Gaming, Vol. 46 Nos3/4, pp. 383-403.
Pavlov, O., Kim, Y.J. and Whitlock, C. (2019), Food ght: teaching systems thinking and ecosystems,
in Schrier, K. (Ed.) Learning, Education, and Games (Vol. 3): 100 Games to Use in the Classroom
and Beyond, ETC Press (Carnegie Mellon), Pittsburgh, PA.
curricula for
Pavlov, O., Doyle, J., Saeed, K., Lyneis, J. and Radzicki, M. (2014), The design of educational
programs in system dynamics at Worcester polytechnic institute (WPI),Systems,Vol.2
No. 1, pp. 54-76.
Peppler, K.A., Thompson, N., Danish, J. and Moczek, A. (2018), In the hive: designing for emergence
when teaching complex systems in early childhood,The Proceedings of the ICLS 2018,
International Society of the Learning Sciences.
Puttick, G. and Tucker-Raymond, E. (2018), Building systems from scratch: an exploratory study of
students learning about climate change,Journal of Science Education and Technology, Vol. 27
No. 4, pp. 306-321.
Quaden, R., Ticotsky, A. and Lyneis, D. (2015), The Shape of Change, Creative Learning Exchange.
Acton, MA.
Richardson, G.P. (2014), Model teaching,System Dynamics Review, Vol. 30 Nos 1/2, pp. 81-88.
Richardson, G.P. and Pugh, A.L. III (1981), Introduction to System Dynamics Modeling with DYNAMO,
Productivity Press. Cambridge MA.
Schaffernicht, M.F.G. and Groesser, S.N. (2016), A competence development framework for learning
and teaching system dynamics,System Dynamics Review, Vol. 32 No. 1, pp. 52-81.
Schieritz, N. (2002), Integrating system dynamics and agent-based modeling,Proceedings of the 20th
Conference of the International System Dynamics Society,Palermo.
Scholl, H.J. (2001), Agent-based and system dynamics modeling: a call for cross study and joint
research,34th HI International Conference on System Sciences,HI.
Squire, K. (2011), Video Games and Learning: Teaching and Participatory Culture in the Digital Age,
Teachers College Press. New York, NY.
Skaza, H., Crippen, K.J. and Carroll, K.R. (2013), Teachersbarriers to introducing system dynamics in
K-12 STEM curriculum,System Dynamics Review, Vol. 29 No. 3, pp. 157-169.
Stephens, C.A., Graham, A.K. and Lyneis, J.M. (2005), System dynamics modeling in the legal arena:
meeting the challenges of expert witness admissibility,System Dynamics Review, Vol. 21 No. 2,
pp. 95-122.
Sweeney, L.B. and Meadows, D. (1995), The Systems Thinking Playbook: Exercises to Stretch and Build
Learning and Systems Thinking Capabilities, Chelsea Green Publishing. White River Junction,
Sweeney, L.B. and Sterman, J.D. (2007), Thinking about systems: student and teacher conceptions of
natural and social systems,System Dynamics Review, Vol. 23 Nos 2/3, pp. 285-311.
Takeuchi, L.M. and Vaala, S. (2014), Level up learning: a national survey on teaching with digital
games, The Joan Ganz Cooney Center at Sesame Workshop, The Joan Ganz Cooney Center,
New York, NY.
Thompson, N., Peppler, K. and Danish, J. (2017), Designing BioSim: playfully encouraging systems
thinking in young children,Handbook of Research on Serious Games for Educational
Applications, IGI Global, pp. 149-167.
Van Eck, R. (2006), Digital game-based learning: its not just the digital natives who are restless,
EDUCAUSE Review, Vol. 41 No. 2, pp. 16-30.
Vennix, J.A.M. (1996), Group Model Building: Facilitating Team Learning Using System Dynamics,John
Wiley and Sons.
Wu, J.S. and Lee, J.J. (2015), Climate change games as tools for education and engagement,Nature
Climate Change, Vol. 5 No. 5, p. 413.
Yoon, S., Anderson, E., Klopfer, E., Koehler-Yom, J., Sheldon, J., Schoenfeld, I., Wendel, D., Scheintaub,
H., Oztok, M., Evans, C. and Goh, S.-E. (2016), Designing computer-supported complex systems
curricula for the next generation science standards in high school science classrooms,Systems,
Vol. 4 No. 4, p. 38.
Further reading
Clement, J. and Rea-Ramirez, M.A. (Eds) (2008), Model Based Learning and Instruction in Science,
Springer, New York, NY.
Forrester, J.W. (1994), System dynamics, systems thinking, and soft OR,System Dynamics Review,
Vol. 10 Nos 2/3, pp. 245-256.
Grotzer, T.A. (2012), Learning Causality in a Complex World: Understandings of Consequence, R&L
Education, Lanham, MD.
Grotzer, T.A. and Basca, B.B. (2003), How does grasping the underlying causal structures of
ecosystems impact studentsunderstanding? ,Journal of Biological Education, Vol. 38 No. 1,
pp. 16-28.
Hmelo-Silver, C.E., Marathe, S. and Liu, L. (2007), Fish swim, rocks sit, and lungs breathe: expert-
novice understanding of complex systems,Journal of the Learning Sciences, Vol. 16 No. 3,
pp. 307-331.
Perkins, D.N. and Grotzer, T.A. (2005), Dimensions of causal understanding: the role of complex causal
models in studentsunderstanding of science,Studies in Science Education, Vol. 41 No. 1,
pp. 117-166.
Peterson, D.W. and Eberlein, R.L. (1994), Reality checks: a bridge between systems thinking and
system dynamics,System Dynamics Review, Vol. 10 Nos 2/3, pp. 159-174.
Qudrat-Ullah, H. (2007), Debrieng can reduce misperceptions of feedback: the case of renewable
resource management,Simulation and Gaming, Vol. 38 No. 3, pp. 382-397.
Salen, K. (2007), Gaming literacies: a game design study in action,Journal of Educational Multimedia
and Hypermedia, Vol. 16 No. 3, pp. 301-322.
Shute, V.J., Masduki, I., Donmez, O., Dennen, V.P., Kim, Y.J., Jeong, A.C. and Wang, C.Y. (2010),
Modeling, assessing, and supporting key competencies within game environments,in
Ifenthaler, D., Pirnay-Dummer, P. and Seel, N.M. (Eds), Computer-Based Diagnostics and
Systematic Analysis of Knowledge, Springer, New York, NY, pp. 281-309.
Sterman, J.D. (2000), Business Dynamics, McGraw-Hill. Boston, MA.
Sterman, J.D. (2008), Risk communication of climate: mental models and mass balance,Science
(New York, NY), Vol. 322 No. 5901, pp. 532-533.
curricula for
Appendix. Systems dynamics tools
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... Game-Based Learning can stimulate three important parts of Learning: Emotional, Intellectual, and Psychomotor [21]. Game-Based Learning is one of the learning methods that is considered suitable for the conditions of the current digital generation or millennial generation for the following three reasons [22]:  Creating a fun learning environment and making students more motivated to learn.  Competition and teamwork in completing missions ingame applications can also add a motivational component to students. ...
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... The computational model can also provide value as part of an interactive learning environment that can help with dynamic decision making [70]. System-based learning and planning environments can improve performance and decision-making on several scales including decision heuristics, structural knowledge, decision time and decision strategy [71,72], especially when combined with prior exploration [73] and debriefing [74][75][76][77]. ...
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... The computational model can also provide value as part of an interactive learning environment that can help with dynamic decision making [70]. System-based learning and planning environments can improve performance and decision-making on several scales including decision heuristics, structural knowledge, decision time and decision strategy [71,72], especially when combined with prior exploration [73] and debriefing [74][75][76][77]. ...
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The COVID-19 pandemic has had a significant impact on higher education. Steering academic institutions through the pandemic is a complex and multifaceted task that can be supported with model-based scenario analysis. This article studies the short-term and long-term effects of the pandemic on the financial health of a college using scenario analyses and stress testing with a stock-and-flow consistent model of a representative tuition-dependent college. The article identifies six effects of COVID-19. By simulating six individual components of the COVID-19 shock, we learn that due to the causal complexity, nonlinear responses and delays in the system, the negative shocks can propagate widely through the college, sometimes with considerable delays and disproportionate effects. We find that different combinations of the pandemic mitigation protocols may have varying effects on the financial sustainability of an academic institution. Some pandemic mitigation choices may destabilize even financially healthy institutions. The article adds to the literature on the economics of higher education, management of the pandemic-related risk on campus, college stress-testing and model-informed decision making in higher education.
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Our paper builds on the construct of the zone of proximal development (ZPD) (Vygotsky in Mind in society: the development of higher psychological processes, Harvard University Press, Cambridge, 1978) to analyze the relationship between students’ answers and the help they receive as they construct them. We report on a secondary analysis of classroom and interview data that was collected with 1st and 2nd grade students completing a short scaffolded inquiry project designed to help them learn about how honeybees collect nectar. We explore how the progression of questions reveal students’ understanding of complex systems by examining how students’ progression through the questions tended to become more sophisticated as we increased support. We further compare two complex-systems perspectives, Component-Mechanism-Phenomena and agent-based approaches, to see how each would categorize students’ explanations. Findings demonstrate the value of the ZPD as an analytic framework in exploring students’ systems understanding in terms of the nature of questions (e.g., sequencing, type of question) and multiple conceptual models (e.g., component-mechanisms-phenomenon, single agent or aggregate behaviors), and how this might impact students’ groupings according to their ability and subsequent instructional support.
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Current teaching and learning of system dynamics are based on materials derived from the expertise of masters. However, there is little explicit reference to neither the stages which beginners go through to become proficient nor what is learned at each of these stages. We argue that this hinders cumulative research and development in teaching and learning strategies. We engaged 15 acknowledged masters in the field to take part in a three-round Delphi study to develop an operational representation of the competence development stages and what is learned at each stage. The resulting system dynamics competence framework consists of a qualified, expert-evaluated, empirically based set of seven skills and 265 learning outcomes. The skills provide a common orientation, in the language of current educational research, to facilitate research, course design and certification efforts to ensure quality standards. To conclude, this paper provides avenues for future work. Copyright © 2016 The Authors System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society. Copyright © 2016 The Authors System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society
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Information technologies are an integral part of a contemporary society which bases its progress on knowledge being one goal of education. Beside acquiring knowledge, skills and routines, the goal of education is to create a complete individual who can rationally and timely make decisions, purposefully react in new situations and be trained for life-long learning. In order to accomplish all this, it is necessary to make educational process more creative, contemporary and adjusted to new generations of computer literate pupils who demand quicker and more frequent interactions, a lot of information at the same time, generations who quickly acquire rules of computer games. Computer games meeting pedagogical criteria should become an integral part of learning. Teaching with mathematical computer games, which fulfil pedagogical criteria, influences pupils' motivation, learning, retention and forgetting. This paper provides a review of literature in this field and determines whether the use of mathematical computer games contributes to more efficient realisation of educational goals at all level of education. Furthermore, considering prior research we have attempted to establish whether the use of mathematical games for teaching has an impact on the formation of a positive attitude of pupils of different ages toward the subject of mathematics, their motivation and knowledge acquisition when compared to learning without computer games. Finally, we have analysed different research methods concerning this issue and assessed the impact of pedagogically designed mathematical computer games on the realisation of educational goals and quality improvement of teaching and learning.
This book describes new, model based teaching methods for science instruction. It presents research that describes these new methods in a very diverse group of settings: middle school biology, high school physics, and college chemistry classrooms. Mental models in these areas such as understanding the structure of the lungs or cells, molecular structures and reaction mechanisms in chemistry, or causes of current flow in electricity are notoriously difficult for many students to learn. Yet these lie at the core of conceptual understanding in these areas. The studies focus on a variety of teaching strategies such as discrepant questioning, analogies, animations, model competition, and hands on activities. Five different levels of organization for teaching strategies are described, from those operating over months (design of the sequence of units in a curriculum) to those operating over minutes ( teaching tactics for guiding discussion minute by minute).
The article summarizes key insights from four laboratory experiments to study renewable resource management. The commons problem, which is widely held to be the cause of mismanagement of common renewable resources, was ruled out by the design of the experiments. Still the participants overinvested and overutilized their resources. The explanation offered is systematic misperceptions of stocks and flows and of nonlinearities. The heuristics that people apply are intendedly rational for static, now resources, but not far dynamic, stock resources. Simplifying and reframing the management problem, by focusing on net growth rates, is suggested as a means to foster the use of more appropriate heuristics. Copyright (C) 2000 John Wiley & Sons, Ltd.