INTEGRATING GAME-DESIGN KNOWLEDGE AND EDUCATION THEORY TO
COMMUNICATE BIOLOGY CONTENT
A Dissertation
Presented to
The Graduate Faculty of The University of Akron
In Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy
Thomas R. Beatman
August, 2019
INTEGRATING GAME-DESIGN KNOWLEDGE AND EDUCATION THEORY TO
COMMUNICATE BIOLOGY CONTENT
Thomas Robert Beatman
Dissertation
Approved: Accepted: ______Advisor Program Director, Integrated Bioscience Dr. R. Joel Duff Dr. Hazel A. Barton ______Committee Member Interim Dean of the College Dr. Gavin Svenson Dr. Linda Subich ______Committee Member Dean of the Graduate School Dr. Hazel A. Barton Dr. Chand Midha ______Committee Member Date Dr. Randall J. Mitchell ______Committee Member Dr. Gary M. Holliday
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ABSTRACT
Civic scientific literacy requires communication between scientists and the public. To
bridge this gap, scientists must acquire skills and knowledge from those who study science
communication. Improving scientists’ communication of science requires better
implementation and availability of science communication skill sets and tools. One such tool
is the use of games, in whole or in part, as tools to improve engagement, motivation, and
understanding in science learning. The appeal of games in learning traditionally is based on
their familiar form and perceived value of fun to audiences. While modern research on
games in learning focuses predominantly in assessing and quantifying motivation &
engagement and learning outcomes, development of good game-design practices as a major
tool in educational endeavors has been slow to develop, which can impact their usefulness.
In this dissertation, I illustrate a number of theories of motivation and ideas that
support the idea of using games in learning, followed by quantifying the lack of consensus
on the differences between the numerous fields and terms, which describe the idea of using game to effect better learning outcomes. This is done using a novel variant of the item sort method, the Item Definition Semantic Sort. This method is also used to explore another set of fields and terms in a different format, and other methodological implementations are considered and described
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The second half of this dissertation presents a number of proof-of-concept projects which use gameful experiences in learning contexts. an advance organizer for field trips as part of a curriculum developed by the Cleveland Museum of Natural History that provides game-elements and competition to enhance student experiential learning in the classroom, a teaching-laboratory module utilizing an analog simulation to convey large-scale numbers and population growth concepts for non-majors biology students, and a game communicating how community watershed runoff and the development of harmful algal blooms interrelate.
These products utilize numerous ideas and theories from educational psychology and informal education combined with game-design knowledge acquired through relevant communities of practice, to provide experience for developing good games in learning, and important takeaways and recommendations for stakeholders.
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ACKNOWLEDGEMENTS
I would have never made it without the help, support, and love of a number of
people.
I’d first like to thank my advisor Joel Duff and Integrated Bioscience Chair Hazel
Barton. Hazel’s encouragement for me to pursue my passion for biodiversity awareness and
science communication, and support in changing my research and lab saved me from a dire
fate. She was the first to recognize and address my impostor syndrome, and will always be a
beacon of positivity. Joel also shares this honor, and helped develop and support my
dissertation work. Joel always made for fantastic conversation that would tangent frequently
out of and back into materials relevant to my research; his passion for science
communication helped to inspire me to pursue my career. Joel always played a wonderful
role as a supportive skeptic, and often he would start conversations feeling doubtful about
things so, I suspect, I would have to convince him otherwise. I will point out here as I have
frequently pointed out to others that I never give him his due share in the great Beatman
rescue of 2016.
I’d like to thank my committee members Hazel Barton, Gary Holliday, Gavin
Svenson, and Randy Mitchell for their contributions to my research and education, especially in my incredibly fruitful comprehensive candidacy qualifying exams. Without each of them, I would never have delved as deeply into the education and biology materials I needed to fully form my dissertation. I’m especially thankful to Gavin and Hazel for their eyes lighting
v up when I first brought up how I felt board games could be used for biology education. Joel was a little more hesitant, but I’d be nowhere without him.
My committee as a whole provided a diverse range of content backgrounds, and consistently provided unique perspectives as my dissertation developed. I am especially thankful that my candidacy exams provided a perfect nexus of intersection between biology content, science communication, informal education, and psychology, which lead to the synthesis of the integrations found in my dissertation. I am especially thankful that I continued to be able to use what I had written in my qualifying exams throughout my studies. A careful eye might recognize passages from them used here.
I’d also like to thank my friends and colleagues, Rafael Maia Villar de Quieroz,
Ashley Bair, Jonathan Gilmour, Travis Magrum, Carrie Buo, Lara Roketenetz, Michael Derr, and Michael Logsdon for their support during some of the hardest and darkest times during my time in the program. Rafael provided a constant sounding board for problems, dilemmas, challenges, and opportunities I faced during the full seven years of my time in the program, and was the one who got me into the hobby gaming community in the first place. Ashley
Bair kept an eye on me when I most needed a watchful eye. Jon Gilmour has taught me near-everything I know about game design, and his and Travis’ friendships and support has been boundless, whether it’s giving me feedback on game designs, just hanging out, or checking on me after I had to be driven to the ER because I had acute appendicitis. Carrie provided a useful sounding board and colleague in doing education as my integration. Lara’s positivity, can-do attitude, and wealth of experience in doing informal ed was profoundly useful. The Michaels have been constant friends and outlets to get my board game on both before and after that became a pivotal element of my dissertation.
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I’d also like to thank various members of the University of Akron Faculty and Staff, including Sarah Rieder-Bennet, Marilia Antunez, Debbie Ammerman, and Ashley Ramer, for their assistance in my research and success in completing my dissertation work.
I’d like to thank my tiered mentoring student Cass Johnson for her assistance in recruiting participants and entering data for Chapter III, Ryan Trimbath and Wendy
Wasman without whom Chapter V would never have happened, as well as Lamalani Siverts who originated what would become Chapter VII.
Finally, I’d like to thank my parents for their constant love and support.
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TABLE OF CONTENTS
Page
LIST OF BOXES & FIGURES ...... xi
CHAPTER
I. INTRODUCTION ...... 1
Chapter Summaries ...... 2
II. THE TRANSITION FROM MOTIVATION- TO TRANSER-FOCUS IN GAMES IN LEARNING RESEARCH ...... 4
Civic Scientific Literacy ...... 7
Games in Learning...... 8
Structure and Function: Psychology and Design ...... 15
Conclusions ...... 20
III. DEFINING GAMES IN LEARNING: AN UMBRELLA TERM TO ADDRESS THE FIELD’S DISORDER ...... 21
Abstract ...... 22
Introduction ...... 23
Methods ...... 26
Results...... 29
Discussion ...... 35
IIII. ITEM DEFINITION SEMANTIC SORT: METHODOLOGY ...... 42
Introduction ...... 42
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Methodology ...... 45
Conclusions ...... 52
V. GAMEFUL ADVANCE ORGANIZERS ...... 53
A.B. Williams Forest Challenge ...... 54
VI. MANIPULATIVES ILLUSTRATING LARGE-SCALE BIOLOGY CONCEPTS ..... 63
Abstract ...... 64
Background ...... 65
Pleistocene preserve: a simulation exercise demonstrating population growth .. 68
VII. PHILOSOPHY AND PRACTICE: DESIGNING FOR COMPLEX PROCESSES . 79
Design Philosophy ...... 82
Application in Practice ...... 85
VIII. CONCLUSION ...... 94
Implementation ...... 95
Final Thoughts ...... 98
REFERENCES ...... 99
Chapter II ...... 99
Chapter III ...... 102
Chapter IIII ...... 104
Chapter V ...... 105
Chapter VI ...... 106
Chapter VII ...... 107
APPENDICES ...... 108
APPENDIX A. CHAPTER III SUPPLEMENT ...... 109
APPENDIX B. CHAPTER IIII SUPPLEMENT ...... 116
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APPENDIX C. CHAPTER VI SUPPLEMENT ...... 118
APPENDIX D. IRB EXEMPTIONS FOR CHAPTERS III AND IIII ...... 151
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LIST OF BOXES & FIGURES
2.1 Sample of complex STEM concepts and processes which can be effectively modeled in
games and gameful experiences ...... 12
3.1 A quizzical diagram ...... 25
3.2 Dendrogram of item definition interrelationships ...... 28
3.3 Relationships of game usages ...... 37
4.1 Biomimicry IDSS results ...... 46
5.1 Game map showing the different regions of the A.B. Williams Memorial Woods in the
North Chagrin Reservation of the Cleveland Metroparks ...... 58
5.2 Example of card showing a single species of tree to be identified ...... 59
5.3 Dichotomous key to be used for identifying trees ...... 60
6.1 Overview of initial five steps of the simulation ...... 72
6.2 Example of data collected from the PP simulation to estimate population growth ...... 74
7.1 Early concept sketches of Erie ...... 87
7.2 Initial computer-drafted board for Erie...... 89
7.3 Final Erie board left side...... 91
7.4 Final Erie board right side...... 92
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CHAPTER I
INTRODUCTION
Improving communication of science requires continued improvement and
development skill sets and tools for doing so. One such tool is the use of games in learning,
whose appeal is primarily based on their familiar form and perceived value of fun to
audiences. Modern research on games in learning focuses predominantly in assessing and
quantification of user motivation, engagement and learning outcomes. My PhD research
investigates the problems in these research fields, in the context of identifying gaps in
current research.
Major problems encountered and identified were a lack of consensus on the names
and terms for the research fields relating to using games as tools in learning, a near absence
of academic literature relating to good game-design & development as a major tool in
producing effective educational resources, and a notable dearth of interest in the possibilities
of non-digital game products.
The goal of this dissertation is to identify and delineate gaps in current research on the use of games, in whole or in part, as tools to improve motivation, engagement, and understanding in learning. It combines knowledge acquired through relevant communities of practice, including psychology, education, game-design, and science content experts, and
provides lessons from this synthesis for developing good games in learning.
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Chapter Summaries
The following chapters of this dissertation contain:
• Chapter II outlines the current practices and theory found in using games in learning,
and details the underdeveloped psychology and education practices that are
employed in the design practices found in the rest of the dissertation.
• Chapter III surveys the various research disciplines that relate to the use of games, in
whole or in part, as tools to improve engagement, motivation, and understanding in
learning. It explores the meaning of the various terms in current use and identifies,
through the use of a novel variant of item sorting, a lack of consensus on differences
in meaning between these disciplines. It also coins the umbrella term Games in
Learning, which is used throughout the remaining dissertation
• Chapter IIII explores the methodology of the Item Definition Semantic Sort, a form
of item sort developed, described, and used in the previous chapter. It outlines
design, development, and implementation, and highlights numerous variations that
can be made to accommodate different participant audiences.
• Chapters V, VI, and VII detail the design process of numerous Games in Learning
products for varying audiences.
o Chapter V utilizes gameful experiences to deliver specific skills (dichotomous key use, tree identification, forest community surveying) as part of an
advance organizer for activities to be performed as part of a curricular field
trip.
o Chapter VI utilizes hands-on manipulatives and charismatic organisms (mammoths) to transform abstract population growth knowledge to concrete
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visualization and understanding, in conjunction with addressing human
difficulties in conceptualizing large numbers.
o Chapter VII details the design process of a game purpose-built to deliver watershed runoff dynamics in Lake Erie. It highlights specific design
principles in a proof-of-concept product that can be applied elsewhere in
developing Games in Learning products.
• Chapter VIII concludes the dissertation with a summary of its findings. It also
features specific implementation suggestions and takeaways for educators, content
experts, and game designers, and recognizes pitfalls and improvements to be made in
future research.
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CHAPTER II
THE TRANSITION FROM MOTIVATION- TO TRANSFER-FOCUS IN
GAMES IN LEARNING RESEARCH
As president of the American Association for the Advancement of Science, Jane
Lubchenco (1998) issued a call for a new social contract for science, one involving a new role for scientists in public discourse. Bazzaz et al. (1998) remarked,
. . . good science consisted of two basic activities: (i) doing first-rate research
and (ii) publishing it in the technical literature for the benefit of scientific colleagues.
We firmly believe that a third activity must now be added by all scientists: (iii)
informing the general public (and, especially, taxpayers) of the relevance and
importance of our work. We are convinced that this applies to even the most
esoteric of “basic” research, because understanding how the world works is
fundamental to both satisfying natural human curiosity and solving the human
predicament.
These remarks at the end of the millennium are indicative of the rather recent recognition of the need for scientific literacy in modern society, especially as it pertains to increasingly complex policy decisions. More telling perhaps is that this coincided with the recognition that the responsibility of science communication, to bridge the gap between scientists and laypeople, was a task which fell on the shoulders of scientists themselves.
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This gap reveals itself in the mindset of some scientists who view science policies and decisions as impenetrable to citizens, and so are best left to be performed within a technocratic framework, ignoring the general opposition of society to decisions being made outside of a democratic process (Miller, 1998). This highlights the discrepancy between the degree of technical expertise scientists must have with the general lack of such knowledge in the public domain. Scientists frequently cannot accurately gauge what it is like to not be technically trained in a subject, while nonscientists frequently cannot gauge what it is like
(Weber & Word, 2001). Thus, while lay audiences frequently perceive scientists as being distant and elitist, scientists frequently reflect this by their perception of audiences as being uneducated and uninformed.
The need for science communication from scientists is especially important when the other major sources of science information are examined. Numerous studies have shown that media, particularly television, are the primary source of science information regarding global climate change, among other contemporary science issues (Bell, 1994; Wilson, 1995).
When science journalism frequently has more errors than general news (Tankard & Ryan,
1974) and the majority of science journalists’ obtain their information not from scientists, but other journalists (Wilson, 2000), this generates a circular “news ‘food chain’” (Trumbo,
1996), where misunderstandings, inaccuracies, and misinformation can get traction.
It is apparent to scientists that communicating science to audiences is best accomplished by scientists who have taken efforts to recognize, understand, and adapt to this asymmetrical division (Weber & Word, 2001). Yet one of the greatest difficulties in establishing science literacy and effective science communication is often the nebulous way in which success in such efforts can be measured. This is due in part to the gap between the
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scientists communicating science, and the content experts in the fields that study how best to
do so, such as education, social psychology, science communication, and cognitive science.
Constructivism describes learning as being often heavily personalized and
contextualized based on prior knowledge and experience; most learning that occurs is built
upon reinforcing and expanding established knowledge rather than introducing entirely
novel concepts (Taber, 2006). When individuals’ constructed personal knowledge (mental
models) is directly challenged by new/corrective information, the clash caused by this
discrepancy creates dissonance which individuals will seek to avoid, leading to a failure to
integrate the new information. This concept is referred to in psychology literature as
cognitive dissonance (Festinger, 1962). Mental models shape how individuals view the world,
and shifting them requires extensive effort both in highlighting misconceptions, presenting
novel models, and supplying ready feedback and support for those moving through the
resulting cognitive dissonance. In addition to an understanding of these concepts, those who
seek to communicate science also have a distinct need for resources that will facilitate such
communication, whether it be in the form of instructional materials or science
communication platforms.
As stated by Fischhoff (2013), “The goal of science communication is not agreement, but fewer, better disagreements.” It has been shown that education, particularly education outside of formal class environs, is most effective at building upon and reinforcing preexisting knowledge and understanding of topics (Rennie & Williams, 2006), while the construction of new knowledge and mental models is far more difficult. This is particularly true when learners are aware, or made aware, of realms of knowledge that will produce cognitive dissonance with their mental models (Mckeachie & Lin, 2002; Wiles & Alters,
2011), and especially when they can decide to avoid such dissonance (Glaze & Goldston,
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2015). This can make scientific communication difficult, even as generating cognitive dissonance is one of the more effective ways to create awareness and address misunderstandings (Bybee et al., 2006). Collectively, this results in the compounding of psychological, educational, and sociocultural factors which all make redressing changes in thought and understanding far more complex than merely presenting facts or data to support the truth of an idea.
Civic Scientific Literacy
Civic scientific literacy is essential in modern society, but remains an ubiquitous challenge faced by scientists, educators, and science communicators. Civic scientific literacy describes the ability of a citizen to find, make sense of, and use information about science or technology to engage in a public discussion of policy choices involving science or technology
(Miller, 2016). Civic scientific literacy is similar to the concept of a public attentive to science
and technology. The attentive public comprises the population consider themselves interested and well-informed science and technology (Miller, 2016). While close to half of the
US population is interested, around 20% of the population is well-informed, with a
resultantly lower percentage in the teens being properly equipped to be attentive, and thusly, civically scientifically literate (Miller, 2016).
Achieving these facets required to generate an attentive public, being interested and well-informed, can be seen as disparate barriers to, namely ones of engagement and understanding. Addressing one without addressing the other will fail to produce improvements in civic scientific literacy.
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Games in Learning
A player gets caught up in playing a game because the instant feedback and
constant interaction are related to the challenge of the game, which is defined by
the rules, which all work within the system to provoke an emotional reaction and,
finally, result in a quantifiable outcome within an abstract version of a larger
system. (Kapp, 2012, p 9)
Historical games such as Hnefatafl, Shogi, Go, Chess, and their predecessors were used to illustrate the nature of strategy, serving as models for real world applications. The
Landlord Game, patented in 1904 and the precursor to Monopoly, was created as a practical
demonstration of the period’s system of land grabbing with all its usual outcomes and
consequences (monopolies). Games feature rapid feedback, and as such allow for
experimentation with a system to develop and test strategies. Simultaneously, the playful
nature of games allows them to branch socioeconomic and generational divides because of
its ubiquity in all cultures.
Serious games, those designed for purposes beyond solely entertainment, are a prime
example of using games and simulations as a tool of instruction. As used by the world’s
armed forces and elsewhere, serious games allow abstraction of complex concepts and
model scenarios that would otherwise be infeasible for reasons of cost, time, logistics, and
safety (Corti, 2006). Gamification and other products which utilize game-elements in
nongame contexts further illustrate that the features of games have broader applications in
society.
Games can support motivation by producing rapid feedback and fun experiences,
while supporting systems thinking, by combining the natural process of game-learning (Gee,
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2007) with models of STEM concepts and processes. Game-learning descries the process by
which players learn the nuances of how individual games work and the emergent strategies
required for effective/competitive play. An example would be recognizing the probabilities
of combinations of dice in a game of Yahtzee or cards in Poker to improve a player’s risk
assessment, or understanding the depths of strategy in Chess. Both of these elements are
critical for building understanding of STEM topics ranging from evolutionary processes to fundamental mathematics. The use of games and gameful experiences (those using game-
elements that are not games themselves) are an underdeveloped sector in the science
communication tool-set, found in isolated fields in both academic and business-marketing contexts. Simultaneously, these fields, such as gamification and game-based learning, are
rarely found in shared literature, making finding and identifying pertinent literature for game
and gameful applications for learning difficult.
Recent literature shows the potential of disciplines utilizing both structural (form)
and functional (psychology of effect) elements of games in conjunction with subject-content
to aid in learning. It has also revealed a dearth/scarcity of academic literature illuminating game-design processes. Much of the literature both on implementation and development focuses exclusively on digital games, a potent obstacle for aspiring designers of educational or content focused backgrounds without programming knowledge, and has only partial transferability for developing analog products.
State of the Work
Promising efforts are already being made to study the development and effectiveness of game-based learning (Eisenack, 2013; Ramirez & Squire, 2014; Kapp, 2012; Connolly,
Boyle, Macarthur, Hainey, & Boyle, 2012). The use of games and game elements (gameful design/experiences, Diewald, Möller, Roalter, Stockinger, Kranz, 2013) in the creation of
9 interactive and dynamic learning interventions in formal settings as well as Informal Science
Education environments have shown the power and potential of gameful experiences to stimulate learning (Gee, 2007; Wideman et al., 2007). While modern games continue to utilize their potential to convey concepts, their recognition as a potential educational tool has been muddied by the poor implementation of educational entertainment products of the past decades. Such ‘edutainment’ games were frequently reliant on flashy graphics (for the time), with trivia memorization and recall, fact-finding, and spatial reasoning puzzles rather than critical thinking, problem solving, and conceptual literacy (Murray, Mokros, & Rubin,
1999).
In the game-design sphere, numerous popular games utilize STEM content for their themes, including Pandemic, Evolution, Compounded, and Power Grid, to name a few.
Unfortunately, none of these games were designed with the intent to depict their contained accurately or for learning outcomes. Of those named above, Power Grid does convey content on sustainable energy, and Evolution incidentally models basic ecology (but not evolutionary processes). But the majority of commercially available games rarely contribute towards improving public scientific understanding. This may reflect that the designers of such games may have poor understanding of the concepts involved, and are unintentionally contributing towards misconceptions. There is a limited pool of explicitly educational (but unassessed) games made with STEM content, such as Xtronauts (Space exploration), Go Extinct!
(Phylogenetics), Science Ninjas: Valence (Chemistry), and Reach for the Sun (Plant anatomy and function), and these are frequently designed and developed outside of the professional landscape of typical commercial game design. As a result, they have minimal impact in the game industry at large and reach limited audiences. Attention has until recently not been
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brought to the novelty of content experts becoming involved in game design, and still
remains unpopular, if not discouraged, in academia (Kwok, 2017).
Games, natively, are able to break down social and racial boundaries within the space they create (Salen & Zimmerman, 2004, p. 95), the so-called “Magic Circle” (after Huizinga,
1955, p. 10), where the game-rules supersede the normal behavior and expectations of
everyday life. Hence, by increasing our understanding of how we can enhance STEM
learning through this medium the possible impacts on society at all points of interaction are
significant. The perception of gaming by those in education who are not digital natives has
often jaded their ability to appreciate game-based learning and its parallels as a potent
framework for education design (Bourgonjon et al., 2013; Proctor & Marks, 2013). This in
turn has created a barrier to the development of STEM education and educators to take a
more active role in the recognition, development, and implementation of Games in Learning
products alongside other informal STEM learning sectors (Ertmer, 2005).
Linking the Games in Learning field (GiL) and STEM
Learning how to play a game strategically and effectively is one of the natural
outcomes of gameplay, whether by intuitive understanding or trial and error. This process of
game-learning (Shaffer, Squire, Halverson, & Gee, 2005) translates directly to systems thinking development when the content of the game mirrors processes found in STEM
subjects. Games can use a combination of Random Number Generators (RNGs) in their
mechanics and topical themes to abstract and simulate STEM processes which can be
absorbed via game-learning (Becker, 2005). Within STEM there are countless subjects which
could potentially not only be effectively illustrated using game systems, but are often
challenges in formal education (Box 2.1).
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• Deep (Geological) Time and processes which necessitate large spans of time: o Evolution patterns and mechanisms o Continental Drift o Island Formation o Ecological dynamics o Climate Change o Sustainable Development o Radiometric decay and dating • Biochemistry/Cellular Metabolism • Probability and Statistics e.g. cell growth, antibiotic resistance and cancer growth • Rules of chemistry e.g. bonding, diffusion, atomic structure • Orbital Mechanics e.g. satellites, moons and planets in relationship to each other • Computers and Electronics e.g. logic circuits and programming Box 2.1 Sample of complex STEM concepts and processes which can be effectively modeled in games and gameful experiences
Games as education interventions
An example of a simple game that illustrates game-learning is Can’t Stop, designed by
Sid Sackson in the early 80’s. Can’t Stop is a simple game that involves rolling four six-sided
dice, which must be paired to advance on tracks that correspond to the sums produced.
Each sum’s track’s length corresponds to the probability distribution of rolling that sum.
The player then decides whether to risk further rolling dice to continue advancing on these
particular sums, or bust trying. The game’s core mechanics revolve around basic probability
distributions, but its functionality and fun come from it taking advantage of the reverse
gambler’s fallacy. In the game, one can perceive the reverse gambler’s fallacy in action, as
success in any moment of the game can overwhelm one’s willingness/ability to consider the
probability and risk involved in continuing. Often a player will find themselves on a hot
streak and continue their turn, eventually losing their progress made when the dice “turn on
them.” As one plays, they learn the strategies that can be, but are not always, most effective
(like in many “press your luck games,” the smartest strategy is to not press your luck very
12 much). Without even intending pedagogic content, games can convey probability concepts extraordinarily effectively, and the structures of games allow for the manipulation of the intrinsic probabilities of game outcomes in such a way that they can be used as models of almost any numerically driven concepts.
Purpose-built games with pedagogic framing and content can prove even more effective at conveying ideas to players passively as the players actively participate in the game. Beyond such simple mechanics as using dice or decks of cards as RNGs, interactions with player decisions and actions allow for comprehension, even in a somewhat complex system, of the direct cause and effect relationships of multifaceted interactions. The process by which game-learning occurs naturally can be attuned to enable learning of educational content.
Informal Education and Science Learning
In 2000 (Falk & Dierking), it was perceived that the popularity and functionality of museums was on the rise, in part due to the growing concept of science, and the enjoyment of science, as an identity distinct from the identity of being a scientist. Between science centers and museums, children’s museums, natural history museums, and botanical and zoological gardens, a broad range of topics targeting wide audiences are presented in an informal, free-choice context. For many, these sorts of institutions are the primary source of science education outside of the classroom, and the value of them continues to be developed and evaluated; outside of formal education, news and articles on scientific topics remain too sensational, technical, or abstract to facilitate connections to personal contexts and narratives, which renders many of them ineffective (Spranger, 1989). It is an ongoing challenge to design, build, and present means of science communication that not only effectively describe the concepts at hand, but do so in a way that enables citizens to engage
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with them and understand them at the personal level that is needed for productive, informed
decisions to be made.
There is compelling evidence that in visitors' minds, learning and
entertainment are not an either-or but a both-and phenomenon. The experience
construct developed by Pine and Gilmore [(1999)] emphasizes that experiences are
not about entertaining or teaching people, they are about engaging people. Taking
this thesis one step further, museums should strive neither to entertain nor to teach
but to engage people in educationally enjoyable experiences from which they can
take their own personal meaning. In fact, Pine and Gilmore argue that when
museums are successful, they go one step beyond experience and provide the
ultimate offering, transformation. Transformations are enduring memories and
benefits, lasting changes in individuals, which result from highly engaging and
personalized experiences. It is the expectation of an experience, or transformation,
revolving around a personal interest that primarily motivates people to visit
museums. These motivations and expectations turn out to directly affect learning.
(Falk & Dierking, 2000 p 76).
While Falk & Dierking (2000) are discussing museum experiences as part of the contextual learning model, their major takeaways are also applicable when considering using games in educational contexts. While it is intuitively understood by most that there is a precarious balance between implementing fun and learning in game design, this challenge is not a conflict: increasing one does not intrinsically decrease the other (Packer and
Ballantyne, 2014); balance is possible, albeit challenging.
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While educators readily recognize the potential for games as tools for learning, they
frequently overestimate the capacity of games, or more appropriately, of design, for didactic
content. This often results in trivia-driven “games” that are overloaded with content. In
these games the didactic nature is conspicuous, and the fun and engagement of the game
crumbles under this burden; people do not appreciate being tricked into learning, and people
are particularly skilled at sniffing out such deceit. As Lim (2008) put it, “Quite often,
educational games or games for education … smell too much like school.”, and engaged
learning is unlikely to follow. Examples exist of both successes and failures, but the reasons
for success and failure are often improperly appreciated for future application.
Structure and Function: Psychology and Design
In current research on using games in learning contexts, the primary appeal is the
functional psychology of motivation. As people natively enjoy games, most research utilizes this motivational component as a lever for traditionally formatted educational content, and assessing the degree of engagement and achievement of learning outcomes are the definitive focus as a result.
Throughout this dissertation, the design of games is the primary focus. This includes
the need for an improved understanding of how designing good games is essential for
producing products that achieve both the motivational and learning components. This issue
has been recognized in a small number of academic circles (Dickey, 2005; Ermi & Mayra,
2005; Abdul Jabbar & Felicia, 2015; Gaydos, 2015). Below is a summary of important concepts relating to the motivational appeal of games, as well as some other facets of their usefulness.
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Motivation
Self-Determination Theory
In any endeavor, humans are driven by some combination of extrinsic and intrinsic motivations. Extrinsic motivations typically consist of positive or negative rewards and punishments exterior to the activity directly that may or may not be reinforcements for desired behaviors and outcomes. Intrinsic motivations come from the performance of the activity itself, or from internally produced reinforcements. Studies have shown (classically,
Skinner, 1963), that extrinsic rewards can produce motivation for behaviors, but can frequently diminish engagement with the activity proper, as well as any intrinsic motivation.
Self-Determination Theory (Deci & Ryan, 1985; Ryan & Deci, 2000) delineates three major factors needed for meaningful intrinsic motivation: Autonomy, Competence, and
Relatedness. Autonomy describes the ability of a person to have a measure of meaningful control over the outcomes of their actions within an environment, an ability to perform an action and appreciate that the outcome corresponds to some degree in a rational measure.
Competence is the sense of being able to perform a task, neither with so little skill and/or so much challenge that failure or success are out of one’s control, nor so much skill and/or so little challenge that success is a guarantee; it is the self-perception that you understand the dynamics of inputs and outputs in a context, and are neither unchallenged nor overwhelmed.
Competence describes a state which shares much in common to that of the flow state
(Csikszentmihalyi, 1990), where one’s focus, attention, and awareness center onto the actions being performed. Relatedness then consists of your actions existing within a context relating to other actors, whether they are other individuals (social relatedness) or merely the responses produced by your actions. When all three of these factors are felt, a sense of self- confidence and worth is produced, intrinsic motivation is easily maintained, and engagement
16 in the pertinent activities can be sustained. Self-determination theory dovetails seamlessly into the game-playing experience (Ryan, Rigby, & Przybylski, 2006).
Educational Psychology theories, such as scaffolding and operant conditioning can in turn be utilized in conjunction with gamification to produce elements of the facets of Self-
Determination Theory. Scaffolding describes the tailored external support and feedback
(such as from a mentor or instructor) for learner skill levels that allows for comfortable risk taking and development until such time the scaffolding can be reduced and/or removed
(Vygotsky, 1978); this concept is intuitive to understand in practice, and further reinforces a learner’s sense of competence, as well as provides meaningful feedback which is useful for both self-correction and a buoyed state of autonomy (Gredler, 2009). In games, this role can be filled by the game itself, and by other more experienced participants. Operant conditioning can then be used as a model for optimal utilization and calibration of extrinsic rewards/reinforcements, again serving as a source of feedback contributing to perception of both autonomy and competence. Social Learning Theory and Cognitive Apprenticeship in turn represent the intrinsically social elements of games (which in turn are frequently the elements incorporated into gamification) in conjunction with the instruction through observation of cognitive modeling that richly reinforces relatedness in game contexts
(Lepper, 1998).
Understanding
Constructivism
The constructivist model of science communication is the natural result of the failure of the information deficit model of science communication. The central tenet of the constructivist model is that, contrary to the deficit model, people are not blank slates upon
17 which information can be drawn. The individual’s experience, background, preconceptions, and misunderstandings inform their perception of the world, and so simply providing information, particularly that which is contrary to their own mental models, will not change their viewpoint, but simply produce cognitive dissonance to an extent that will impede change in understanding. Providing an understanding of underlying concepts and processes allows for a piecemeal progression of understanding, allowing for acceptably small levels of cognitive dissonance to be encountered and incorporated into mental models. This in turn can far better allow for incorporation of new ideas into individuals’ mental models and achieve the learning outcomes desired. Thus Games in Learning products must be built in such a way that information is presented according to a constructivist form. A focus on the deficit model frequently results in games that share more features with trivia and basic puzzles, where the game teaches a fact or how to solve a single puzzle type rather than understanding of underlying concepts.
Use
Affordances
Games can serve as a type of affordance, an object whose intended use is readily perceivable and understood (Norman, 1988), namely that games are easily recognized as games. With games as a familiar activity, they can be used as tools, just as a hammer or screwdriver. Tools serve as affordances as their function is, by design, easy to grasp. One uses a hammer to drive nails; a game is played; users are not challenged in figuring out what they are for.
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Manipulatives
Manipulative materials are objects designed to represent explicitly and
concretely mathematical ideas that are abstract. They have both visual and tactile
appeal and can be manipulated by learners through hands-on experiences.
Manufacturers advertise manipulatives as materials that will make the teaching and
learning of mathematics ‘fun’ and promote their products as catalysts for engaging
students in mathematical learning. Because students’ abstract thinking is closely
anchored in their concrete perceptions of the world (Thompson, 1992), actively
manipulating these materials allows learners to develop a repertoire of images that
can be used in the mental manipulation of abstract concepts. (Moyer, 2001)
Analog games are made out of potential manipulatives. From tokens to figures to counters to spaces on boards, players interact with the both the literal and figurative moving parts in games, which contributes directly to the process of game-learning, understanding how to not only play a game, but how to play it better. Taking advantage of this aspect of games, and how they are designed, is a broadly underutilized application of current game work. As an example, a recent publication by Taspinar, Schmidt, and Schuhbauer (2016) describes the development and production of a trivia-style game, modeled after the quiz and trivia based review methods found commonly in classrooms. While the paper does provide the framework and infrastructure to facilitate endusers producing and utilizing this resource, it is in essence a game where one rolls a die, moves a figure, and then draws a question out of a deck. In essence, the cutting edge of games in learning is a barebones version of Trivial
Pursuit, which was first produced in 1979. Considering that GiL related work has existed since the mid-sixties, this is a rather troubling rate of progress.
19
Conclusions
The use of games in learning contexts continues to grow and expand application in
both formal and informal education environments. Early successes serve as proofs of
concept, but ample evidence supports a need for the incorporation of game design and
development knowledge in the literature so science communicators are not trailblazing on
well-trod ground. A recent article in Nature (Kwok, 2017) presents the concept of
educational games as a viable pursuit, but like other forms of science communication
frequently is underappreciated when compared to traditional academic and education work.
This dissertation expands on game design practices being employed for science communication, surveys the state of the relevant research fields, and provides a number of proofs of concept of using game design knowledge in conjunction with educational theory to communicate biology content.
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CHAPTER III
DEFINING GAMES IN LEARNING: AN UMBRELLA TERM TO ADDRESS THE
FIELD'S DISORDER
This chapter identifies and provides a partial solution to the confusing diversity of disciplines/fields/terms of research found being used to describe work relevant to this dissertation. In the initial literature review completed for this dissertation, seeking out the existing research on using games as tools for learning, it became rapidly apparent that not only was there a broad number of different terms relating to the ideas of using games, in whole or in part, as tools to improve motivation, engagement, and understanding in learning, but also an apparent lack of consensus between these different named fields. In order to better understand the relationships between these fields, and to describe them quantitatively, the Item Definition Semantic Sort (IDSS) method was developed (further methodology information can be found in Chapter IIII). As terms are intended to have robust definitions that often expand beyond what their composing words’ denotations would suggest, it was hoped that clarity could be pulled form their provided definitions in academic publications.
The paper contained in this chapter describes the first IDSS study, on a range of disciplines inclusively grouped together under the heading Games in Learning. Their disordered state illuminated through both qualitative assessment from the literature review, and quantitative sorting with the IDSS method. What was found was not only a lack of
21
consensus on definitions of each major GiL discipline, but also notable exclusivity in many
definitions that would not be implied by the words composing them.
Below is the abstract and body of the manuscript, Games in Learning: Shedding Light on a Problematic Taxonomy, published in EAI Endorsed Transactions on Serious Games (Beatman &
Duff, 2019). The supplementary materials for this chapter can be found in Appendix A.
Abstract
The use of games as tools for learning has been a paradigm of great interest for over
fifty years. Several terms have emerged to describe disciplines that study and apply this
paradigm. Many of these terms may be synonymous or have overlapping uses or have
multiple definitions within different disciplines. The literature was surveyed for the
definitions terms relating to this paradigm. Their working definitions and relationships are
discussed. Through both a survey of the literature and a novel variant of sorting activity, the
Item Definition Semantic Sort (IDSS), it is shown that individual item definitions do not
semantically cluster together by corresponding term. This indicates both a lack of consensus
on terms’ definitions, and of clarity between terms. The umbrella term Games in Learning
(GiL) is coined to integrate the disciplines and products within this design paradigm. Games
in Learning is defined, “Research and work involving the use of games, in whole or in
part, as tools to improve motivation, engagement, and/or understanding in
learning”. The rationale for the term is elaborated, and the major functional divisions found
within.
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Introduction
The topic of using games and more recently, gameful experiences, as learning tools has been a subject of great interest to educators for decades. The first pages of Games for
Growth (Gordon, 1970) summarize the state of the field, then and now, nicely:
Among recent innovations, educational games offer great promise of furthering this change [to experiential learning]. Not only are they fun, but they require that all players share in making decisions throughout the game. Unfortunately, educational games are not well understood. They are not yet widely available, and experience with them is necessarily limited; as a result, a mystique surrounds the technique. To complicate matter, the use of games implies a seeming irreverence toward education. Educational games are neither esoteric nor frivolous. But they differ enough from most other classroom activities to raise questions about the role of the teacher, the time and space required, how to evaluate what games teach students, and the benefits and drawbacks of using games. These and other operational problems will be discussed in ensuing chapters. But the primary question remains: Can educational games, which often resemble entertainment games, be employed for serious purposes in the classroom? A survey of the origin and history of serious games may help allay initial doubts about the viability of the technique.
Nearly fifty years have passed, yet the state of things is much the same, often focused on
building the rationale for using games in learning. For example, Wiktionary provides under
its definition of games the example sentence: Games in the classroom can make learning fun.
Multiple disciplines have emerged into common use, including Digital Game-based Learning
Educational Games, Edugames (Edu Games, Edu-games; short for either Educational
Games or Educational Computer Games), Edutainment Games, Elearning*, Game-assisted
Learning, Game-based Learning, Game-based Teaching and Learning, Gameful
Design/Experiences for/in learning, Games and Learning*, Games for Change*, Games for
Learning, Gamification of/for/in Learning, Instructional Games, Learning Games,
Persuasive Games, Serious Educational Games, Serious Games, and Simulation & Games*
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In these disciplines, there exists a dizzying array of terms. More confusingly, the definitions of these disciplinary terms are often nested within one another to the point that their differences are difficult to parse (see Figure 3.1, from Breuer & Bente, 2014). Despite this, multiple disciplines are often used in isolation with little effort to consolidate or even reconcile the relationships between them.
A major challenge in mapping the definitions and relationships is this use of terms.
Scientific/discipline-specific terms are meant to provide an economy of language, to allow concepts to be expressed succinctly. Terms transform broadly descriptive words and phrases into specific meanings in scientific contexts (Wilkinson, 1991). Unfortunately, such terms still suffer from shifts (semantic changes) in both meaning (denotation) and nuance
(connotation) (Blank, 1999). These semantic changes can lead to problematic differences in the use of a term, especially if similar changes are occurring with similar terms in parallel.
These sorts of shifts directly impede the concise and effective communication terms are designed to allow. These semantic changes are frequently found when discussing the disciplines relating to the use of games in whole or part in learning contexts.
Although there is overlap in their structural (form) and functional (psychology) paradigms, a concise, cohesive, or comprehensive umbrella term to describe the entirety of research involving games in whole or in part as tools for learning is lacking. This makes elaborating the many disciplines de rigueur when discussing games in learning. A search into these disciplines quickly reveals over a dozen headers, design disciplines, and pedagogical perspectives. Further problems arise in understanding the relationships between these terms, as their common usage in the literature is often in isolation from one another, and their definitions vary. Recent studies show misuse and misplacement of meaning even for terms that most frequently appear in the literature (Caponetto, Earp, & Ott, 2014). While such
24
confusion has persisted, the promise of these disciplines as a whole suggests it is worth
continuing to find ways forward. To that end we hope to foster better communication.
Figure 3.1 A quizzical diagram. The original caption was “The relations between serious games and similar educational concepts” (Taken from Breuer and Bente, 2014). While this diagram depicts the overlap between numerous Games in Learning fields, it has limited discrete information about what these fields entail.
This paper is a review synthesis of the numerous disciplines which examine and
develop products which use games, in whole or in part, as tools to produce motivation and
engagement in learning. The definitions of these disciplines are examined comparatively
using an item definition semantic sort (IDSS), a novel variant of card sorting, to expose the
significant overlap and synonymy between them, and lack of consistency within individual
terms’ definitions. To address this problem, the umbrella term, Games in Learning, is
coined. Games in Learning (GiL) describes any and all works that use games, in whole or
in part, as tools to improve motivation and engagement in learning. The intent of this
25
umbrella term will serve to aid in joining these disciplines into a transdisciplinary space.
These disciplines conspicuously share common structural and functional paradigms but have
had no means to quickly and easily refer to them as a common group nor has there been a
comprehensive attempt to map out their relationships in detail.
Methods
Definition Search
Sixteen terms used to describe research disciplines and applications are examined here: Digital Game-based Learning; Educational Games; Edugames; Edutainment Games;
Game-assisted Learning; Game-based learning; Game-based Teaching and Learning;
Gameful Design*; Games for Learning; Gamification*; Instructional Gaming; Learning
Games; Persuasive Games; Serious Educational Games; Serious Games*; Simulation [&]
Games (Terms denoted with an asterisk are also used in noneducation applications).
Approximately half of these emerged as terms since the start of the 21st century.
Definitions were found through examining original source literature, and recent well-
cited articles for occurrences of the relevant term in conjunction with a clearly provided
definition. Terms with multiple definitions were assessed for commonalities and combined
when possible. A total of 50 definitions were sourced for 15 terms, of which one term and
definition served as an outgroup to determine the height at which to cut clusters.
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Item Definition Semantic Sort
The relationships of the various disciplines found in the initial search were delineated using an Item Definition Semantic Sort. Unlike typical semantic card sorts, where the items sorted are individual words/terms, the items were instead definitions that correspond to the terms of interest. 50 individual definitions, covering 13 different GiL terms, were blinded to their corresponding terms, and printed onto slips or cards, and then sorted by volunteers
based on perceived semantic similarity. Volunteers were sourced from students, staff, and
faculty at the University of Akron.
Sorters were given limited guidance on ways in which items could be sorted, with
enough prompts to understand to sort them based on whether they described the same or
similar concept. The 25 sorts were performed by a number of volunteers from the University
of Akron. Each was provided with an instruction sheet as well as the blinded definitions
printed on slips or cards (see supplementary information). The card sort also allowed for
participants to describe the rationale for their groupings, and to select exemplar definitions
for each group. The explanatory rationales were used solely to identify and split groupings
whose purpose were to contain perceived outliers; the identified exemplars were not used in
any part of the analysis in this study.
Sorts were compiled into a co-occurrence matrix, transformed into Euclidean
distances and then analyzed by hierarchical cluster analysis using Hierarchical Clustering
v1.0.5 in Free Statistics Software v1.2.1 (Wessa, 2017). The output cladogram from the
program was then used as a basis for a cladogram containing more information regarding the
terms each individual item corresponds to, to facilitate visualization of the results (Figure
3.2).
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Figure 3.2 Dendrogram of item definition interrelationships. Branch length is Euclidean distance; color of items corresponds to associated term.
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Results
Definitions
Serious Games
Abt first described games that could be played seriously, and coined the term
“Serious Games”, describing them as “games [that] have an explicit and carefully thought- out educational purpose and are not intended to be played primarily for amusement” (Abt,
1970, p. 9). This does not mean that serious game are not, or should not be, entertaining”. In the past two decades, a number of other definitions have emerged. As Wilkinson (2016) highlights, these definitions frequently are reductive variations on “games that have a purpose beyond entertainment” (Michael and Chen, 2005; Egenfeldt-Nielsen, 2005; Zyda,
2006; Corti, 2006; Susi, Johannesson, & Backlund, 2007; Annetta, 2010; Bellotti et al., 2011;
Djaouti, Alvarez, & Jessel, 2011; Kiryakova, Angelova, & Yordana, 2014). As such, while
Abt’s definition is similar, it does emphasize the functional role of the use of an entertaining medium as leverage for education (or training).
Functionally, the purpose of the term Serious Games, classically, was to emphasize the use of games for serious purposes. The many definitions found frequently fail to incorporate the fact that since games are intended, by design, to be enjoyable activities, they can then be used as more engaging educational tools. Corti’s definition is exclusively referring to digital games (2006) but can be easily revised to a more meaningful and broader definition, “about leveraging the power of … games to captivate and engage end-users for a specific purpose, such as to develop new knowledge and skills”. Serious Games functions effectively as the broadest term for the use of games for non-entertainment purposes, including but not limited to education and learning.
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Serious Educational Games, Games for Learning, Educational Games,
Edugames, Edutainment Games
Other disciplines exist which either significantly overlap with Serious Games or are wholly nested within them. Serious Educational Games as a term has been used to specify
Serious games used for K-20 educational purposes (Annetta, 2008; 2010), a clarity which can find use when needing specificity while discussing the full array of Serious game applications, but unnecessary when a learning context is clear.
Educational Games, Edutainment Games, Games for Learning, and Edugames show similar context-dependent redundancy. Edutainment, as a descriptor for games, most
notably features this, as the entertainment/engagement value of games is, fundamentally, the
entire purpose of using them for educational purposes. Educational Games is most
frequently used as a descriptive term, rather than as the name of a discipline, like Games for
Learning as well as Learning Games (both discussed below). Edugames is frequently found
as an abbreviation for Educational Games, but also has usage in isolation in foreign language
publications of unknown quality.
Digital Game-Based Learning, Game-Based Learning, & Game-Based
Teaching & Learning
The term Digital Game-based Learning, much like Abt’s Serious Games, had its
origins as the eponymous title of a book by Prensky in 2001. von Wangenheim & Shull
(2009) defined it (drawing from both Digital Game-based Learning and Serious Games) as
game applications that have defined learning goals. Susi et al., in an overview of Serious
Games (2007) refers to a Wikipedia article (the provenance of which is unknown) that
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explicitly defines game-based learning as “a branch of serious games that deals with applications that have defined learning outcomes”, and then clarifies Digital Game-based
learning being a more specific term for such games that are digital. Corti (2006) also refers to
game-based learning and serious games as equivalents.
What is significant, however, is the connotation found when the phrase game-based
learning is used: it frequently is used to exclusively describe digital/video/computer games.
The term Digital Game-based learning notably predates the occurrence of Game-based
learning, and the way that Game-based learning is used, frequently preferred over Digital
game-based learning, suggests that rather than it occurring as a more inclusive descriptor of
game applications that have defined learning goals, there remains an implicit element of
those games being digital. As a result, while the term Game-based learning would appear to
be term with broader uses than its predecessor, practice shows that it is moreso an
abbreviation of its antecedent.
Parallel to these terms’ usages, “Game-based Teaching”, or “Game-based Teaching &
Learning” have been found in more recent literature. Use suggests a primary meaning
focused on actual use by instructors (D)GBL; it remains unclear what purpose this
distinction serves.
Instructional Games, Game-assisted Learning
Both of these terms have been used historically in the literature primarily as
descriptive language and are typically either used synonymously with “computer-assisted
learning” or game-based learning. Game-assisted Learning’s definition (Wu, Hsiao, Wu, Lin,
& Huang, 2012), “the outcome of integrating effective learning principles into game
environments for the purpose of utilizing engaging elements of games as a means for
31 improving the quality of education” is noteworthy as it explicitly details using the affordance of games in learning. Instructional Games, in contrast, in their definition (Hirumi,
Appelman, Bieber, & Van Eck, 2010), “any interactive, digital game that is designed specifically to facilitate the achievement of a specified set of learning outcomes that meet educational goals” features a digital qualifier and seems primarily prescriptive to the outcome rather than the means. Both terms are functionally the same as the common conception of game-based learning.
Games for Learning Abt used the phrase “games for learning” in an early paper, later published as a chapter in Simulation Games in Learning (1968), however it only occurs recently as a term, with the definition “games specifically designed for learning as opposed to the use of games in learning” (Slussareff, Braad, Wilkinson, & Strååt, 2016). It becomes clear that while this term intends to distinguish games built for education/learning from COTS games used in learning contexts, this distinction seems mostly a matter of specificity of product type within the broader scope of the established research.
Gamification & Persuasive Games and Gameful Design/Experiences
The term gamification and its applications have gained special interest over the past decade, the definition being “The use of game-elements in non-game contexts” (Deterding
Dixon, Khaled, & Nacke, 2011). While whole games have been in use for applied training and education, the application of game-elements or game-layer to improve non-games is a recent trend. The contrast, however, between its definition and actual applications, proves to be problem.
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Major success in gamification has been shown using the “game-elements” of points/high scores, badges, and achievements. These concepts have been used historically as sources of extrinsic motivation in games (arcade games, Xbox, etc.), despite a central premise/appeal of using games, in whole or in part, is that they can produce intrinsic motivation as a result of being fun (Hecker, 2010). These prominent examples of so-called game-elements are in fact hardly so; practitioners of this form of gamification often respond to this criticism with a general indifference to this mismatch. This can only be described as a use of language in bad faith. Numerous authors (summarized in Rughiniș, 2013) have had issues with the exploitative nature of gamification’s common uses and the problem being that while the definition precludes use in games, the layer being used in non-game contexts for extrinsic motivation can and is being used in games. These pseudogame-elements are more representative of operant conditioning practices (Gredler, 1997). This reductive use of basic operant conditioning as a key component of gamification minimizes the other game- elements more fundamental to gaming experiences, and their continuing use could undermine the concept fundamentally.
In part as a response, new terms and approaches to gamification have arisen.
Persuasive Games was a term coined by Bogost (2007), seeking to not only highlight the operant conditioning rampant in gamification by renaming it exploitationware, but to provide a less jaded alternate. Gameful Design, in contrast to gamification, focuses on gamefulness as a design goal, rather than a design strategy (Deterding, 2013). Diewald et al.
(2013) and Dichev (2014) provided the following definitions: The use of game design elements in non-game contexts with the goal of achieving long-term effects based on intrinsic motivation; designing systems that are intrinsically motivating and fun to use, by applying those techniques that game designers use to keep the players immersed and
33
engaged. Both definitions focus on using game design techniques or elements, with the
targeted goal of making systems that produce intrinsic motivation. Gameful Design, as a
term, seeks to address the contention surrounding Gamification. While the term Persuasive
Games has had limited traction outside of pointing to gamification’s flaws, Gameful Design
has succeeded in a more pointed effort in referring to using definitive game-elements for
nongame applications.
Broadly, gameful design as a practice is the more commonly accepted term for what
gamification purports to be, especially among those who dislike the current outputs of the
gamification movement, which is itself rife with definitional problems (explored in depth in
Seaborn & Fels, 2015).
Learning Games
Learning Games has definitions which are far more explicit in their functional
relationship between games and learning. For example, Ke (2016) describes, “A learning
game is supposed to provide structured and immersive problem-solving experience that
enables the development of both knowledge and ‘ways of knowing’ to be transferred to the
situations outside of the original context of gaming or learning.” This definition not only
recognizes the ability of learning games to fail (though the use of the phrasing is supposed
to), but also the goal of the takeaways of learning games expanding beyond the framing of
both original contexts.
Card Sort
The card sort produced 11 in-group clusters (Figure 3.2), with each cluster containing between two and nine cases/items. While a single cluster contained cases/items
34 all corresponding to a single term, no individual cluster was composed solely of all cases/items of a single term. Card item 44, the definition of Games with a Purpose, “a class of games in which people, as a side effect of playing, perform tasks computers are unable to perform” served as a functional outgroup in the card sort. Because of this, the clusters were cut at the height at which this term definition connected to other term definitions in the sort
(the red dashed line in Figure 3.2).
The lone single-term cluster contained definitions for serious games. As serious games as a discipline is broadly inclusive of games used for purposes beyond solely entertainment (more details in section below), it is unsurprising to find that its definitions both cluster together and can be found scattered throughout the other items in the taxonomy. While the taxonomy generated does show some hints of the relatedness of certain terms (most notably the common clustering of gamification and gameful design, as explored elsewhere in this article), there is an evident lack of clustering of definitions by the terms they correspond to. This pattern clearly illustrates a lack of consistency and coherency in the meaning of individual terms, but more importantly a lack of distinction between disparate terms that, functionally, would be expected to have clear and different meanings to justify their usage.
Discussion
Through both the survey of the literature and the card sort analysis, the disjointed state of the current disciplines of study becomes rapidly apparent. While each individual discipline is not intrinsically disrupted by this, it notably prevents productive cross- fertilization and organization between disciplines, and as such prevents the sorts of consilience and collaboration that could greatly benefit future research. To begin addressing
35 this challenge, the term Games in Learning is coined to frame the commonalities in form and function of the disciplines/disciplines which use games in whole or in part as tools to improve motivation and engagement in learning.
Relationships Fundamentally, when discussing using Games in Learning, the topic is the use of games in a learning application, as opposed to the use more broadly for other applications
(as Serious Games’ denotation typically allows) or use solely as an entertainment medium.
Whole-Partial Divide There are two major design perspectives found in Games in Learning. These perspectives, what can be called the whole-partial divide, are not limited to education applications. They consist of the use of either games in their whole entirety (full-fledged games) or in parts (non-game activities featuring game-elements), applied in nongame contexts (Figure 3.3A). Whole game applications are typically described as Educational or
Learning Games; Serious games is also a well-established term but is often considered to be more inclusive as it distinguishes training from education. The partial-game approach can be further subdivided dependent on whether gamefulness is a design strategy (Gamification proper) or a design goal (Gameful Design/Experiences) (Figure 3.3B). Functionally, this divide can be expressed as a difference in whether a game layer is added to an application or incorporated inextricably. These differences are elaborated upon above.
Media Forms
Crookall (2010) discussed the relationships between several the disciplines mentioned in this paper and sought to recognize Simulation/Gaming as a discipline that included “...simulation, gaming, serious game, computer simulation, computerized
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simulation, modeling, agent-based modeling virtual reality, virtual world, experiential
learning, game theory, role-play, case study, and debriefing.” His editorial also sought to fasten the concept of simulation/gaming explicitly to computerized forms.
Figure 3.3 Relationships of game usages A) Use of games in whole or in part, B) Perspective of gameful (use of
games in part) as design strategy or goal, C) Diagram combining information found in A and B
37
GiL disciplines also feature a notable divide between digital (video/computer games,
applications, websites, programs, etc.), and analog (tabletop, board, card, etc.) media. Since
the earliest commercial availability of computers, their potential for operating simulations
that can be used for education or training purposes has likely influenced the predominance
of using digital games in learning. As a result, the connotation of the word game has
changed, leading to it referring exclusively to digital/video/computer games in many cases.
In response to this, other disciplines and terms must then explicitly clarify whether
they are referring to digital games, analog games, or both, when using the word game either
in isolation or as part of a term or research discipline. There remains, however a growing
global market for analog (Hobby) games (Griepp, 2016), suggesting an underexploited
medium for Games in Learning. As such, while there is a divide in medium between analog
and digital games, there is limited rationale for a disciplinary division.
Games in Learning
Rationale
The term Games in Learning was chosen to describe the use of games, in whole or
in part, as a tool to produce engagement and motivation in learning, for two major reasons.
The first is that it is a novel umbrella term and functionally descriptive, and the second is its precedence in use as a descriptive phrase; both are elaborated below.
Functionally, Games in Learning serves as an inclusive umbrella term to discuss all
research which uses the paradigm of games as a tool in learning contexts. Games in learning,
even as a phrase rather than a term, has an intuitive meaning. Connotatively, it suggests that
the entirely of learning may not take place solely within a game framework, allowing for the
broadest nuance of understanding the idea. Effort was taken to not coopt extant terms, or to
38
create a term that forms a competing standard. It allows for an interpretation of the use of
games in whole and in part in all learning contexts.
The word learning is used in the term, rather than education, to focus attention on
the outcome, rather than the process. As GiL covers products used in structured,
unstructured, formal, and informal settings, the natures of which can vary, but the outcome
of learning remains consistent, using the word learning, rather than education/educating,
avoids a connotation that implies use solely in classrooms, curricula, and other structured
settings.
The historical precedent for Games in Learning is twofold. The first is found in
Simulation Games in Learning (Boocock & Schild, 1968). This book features a number of
relevant chapters on the theory and application of using games in learning. The word
Simulation was removed from our term to allow for the maximum breadth of game types,
and to include nongame applications (gameful designs, gamification, etc.). Secondly, in a
more recent writing (Slussareff et al., 2016) in their chapter Games for learning used the phrase
“games in learning” as part of their definition of the more specific “games for learning”
(bold added for emphasis):
In this chapter we will outline relevant aspects of serious games supporting a learning process. Under the term games for learning we refer to games specifically designed for learning as opposed to the use of games in learning
A broad takeaway should be that the use of descriptive phrases, and the formation of excessive and redundant terms, has generated the quandary that this latest addition seeks to resolve. One could say that we seek to achieve subtraction by addition. Game-based learning was used first as a descriptor before it became a term for a discipline or products from such disciplines, just as serious games, educational games, and others have. Working from this
39 precedent, the occasional descriptive use of “games in learning”, often in the format of
“using games in learning something”, has been adapted as a unifying term to describe the numerous disciplines which occur in the transdisciplinary space of using games in whole or in part in learning. At the very least, it will, as terms intrinsically do, serve as a shorthand to describe the broad paradigm of using games, game and gamelike elements, and gameful experiences, as tools in, for, with, and of learning/education.
Future and Implementation
Several different terms and disciplines currently in use in the research literature share commonalities, most importantly a shared paradigm of using games, gameful/like/inspired experiences, and game elements as tools for learning. Beyond this, several of the terms used either overlap significantly, or are for all intents and purposes synonymous. While making changes to usage to reduce such redundancy would conceptually be an effective takeaway from this paper, it is recognized that these terms are well established in the literature. It is suggested that, while individual terms may be preferred, that better efforts be made to not only acknowledge their definitions regularly to reduce semantic change, but also their relationships to other disciplines within Games in Learning.
The use of the term Games in Learning extends beyond simply being an inclusive descriptor but serves as a framework to recognize the utility and consilience to be gained in its various subdisciplines’ collaboration. Especially as many headings exist in isolation despite sharing similar definitions with others, there seems to be a need for better communication between each community of practice for the betterment of all Games in Learning research, development, design, and practice. Within discussions of Games in Learning products and outcomes, the following labels are recommended to identify whole- or partial-game
40 products. For whole Games in Learning products, the existing Educational/Learning games is both functional, descriptive, and well established in the literature. For partial-game applications, Gameful applications provides clarity that the product is not a game proper, but rather an application with gameful components. Gameful applications can be used more broadly for non-educational applications, but within the context of Games in Learning such clarifications should prove unnecessary.
Future work is needed to further examine the commonalities among disciplines with respect to Games in Learning, and the functional nature in which game-learning can be applied to content-specific learning. While much work has been done in conjoining motivational theories to the rationale of using Games in Learning, there remains many opportunities for improving the design of such products to improve their effectiveness. It remains to be seen whether the various disciplines grouped together here under Games in
Learning can effectively recognize their similarities and benefit from one another's’ differences.
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CHAPTER IIII
ITEM DEFINITION SEMANTIC SORT: METHODOLOGY
Introduction
This chapter focuses on the description of the origins, implementation, and future use for the Item Definition Semantic Sort (IDSS) developed and used as research described in the previous chapter. IDSS is a novel variant of item sorting for assessing similarity of terms. The primary role of IDSS is to quantitatively confirm a randomness/nonconsensus in contexts where the opposite is expected.
Item sorting, also known as object sorting or card sorting, is a quantitative method in the social sciences used to generate subjective categorizations of objects, ideas, or word. It sees most frequent modern use in User Interface/Experience design studies (Hayhoe, 1990;
Nielsen & Sano, 1995; Roth, 2013), but has historically been used for numerous fields involving typology and taxonomy, including library resource organization (Faiks & Hyland,
2000; Hider, 2009), comparing/identifying expert vs. novice perceptions of numerous types of categories (Hider, 2009; Bedwell et al., 2012; Bissonnette et al. 2017), and semantic relationships (Steinberg, 1967; Chaffin & Hermann, 1984).
Card sorts in particular typically consist of a number of cards, each containing an individual word, which are then sorted into piles by participants based on some criterion,
42
that may or may not be determined by the researchers. Typically similarity/dissimilarity is a
common criterion, and has been used commonly in the past to evaluate conceptual
relationships, either at a functional or semantic level (Steinberg, 1967). In many User
Experience studies individual words are used as sort items, to understand the perceived
associations of the endusers. The IDSS utilizes sorting to examine the relationship, not of
individual words or those word’s connotations (broader meaning), but rather the numerous denotations (definitions) that exist for terms that have context-specific definitions beyond the simple denotation of their composing elements.
IDSS was originally performed by the author as a thought exercise to explore the perceived incoherence of the numerous term definitions that had been pooled in the process of the review of the literature relating to what has now been defined as Games in Learning fields. Following this initial card sort using blinded definitions, the utility of this method to further quantify the perceived semantic relationships of terms with overlapping definitions was recognized. Further exploratory reading quickly revealed that the notion of using item sorts to identify relationships was a well-established research method, described above.
The IDSS screening developed in the previous chapter was comprehensive of the discovered term definitions for GiL fields. It comprised 50 definitions covering 13 different terms. This required 50 individual cards to be sorted, each containing the full text of their item definitions. While this could produce the most robust results due to the sort set’s comprehensiveness, it also contributed to lengthy time to complete (from between ~30-120
minutes), which makes the method’s utility somewhat limited if larger sample sizes are desired.
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Biomimicry Sort
In collaboration with Sarah McInerney of the University of Akron’s Integrated
Bioscience program and with the support and permission of the Cleveland Museum of
Natural History, a second IDSS experiment was performed, this time focused on terms that relate to the topic of biomimicry and its related fields. Over 70 definitions for 20 terms including biomimicry and related disciplines were collected. Ms. McInerney wished to specifically examine the terms biomimicry, biomimetics, and biology-inspired design, and so the pool was filtered to 34 definitions covering these three terms
For this IDSS, the participants would be visitors to the Cleveland Museum of
Natural History during one of their monthly events, the Think and Drink with the Extinct series, wherein various tables are staffed by scientists who engage with patrons on their research fields. This participant set thus required a number of adjustments to the existing
IDSS methodology to accommodate the nature of the event. This included multiple simultaneous participants, time constraints, accessibility/ease of completion of the sort activity, and the effects of alcohol-consumption during the event.
To address these issues, the set of definitions to be sorted was further reduced so sorts might be completed quickly. For the final sort set, 15 total definitions were selected, four from biomimetics (one instance from a German definition for “biomimetik”) and biology-inspired design each, and seven from biomimicry. Through a set of presorting by me and Ms. McInerney, many definitions were identified that were notably similar to one another, and placed into groups. Each of these groups were then considered as individual items when selecting items to compose the final sort set; if a group was selected (done pseudorandomly) one of its composing definitions would be chosen to be included in the item set.
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To address the possibility of large numbers of participants, the sort was modified
from decks of cards sorted by individuals to large placards placed on the provided event
table, each containing a single definition. There, participants would be provided a sort sheet
on which they could create their sort clusters. In this way, a number of participants could
encircle the table and complete their sort activities simultaneously, with the researchers
explaining the activity to new participants as they arrived.
These changes allowed for efficient collection of sort data from this audience, producing 22 usable sorts out of 44 collected (Figure 4.1). Fundamentally, this exposes a number of variations in implementing IDSS that should be taken into consideration when developing new studies.
Methodology
Through the development of these two IDSS variations, it became evident that the method could accommodate modifications, including but not limited to time to complete sorts, sample size, and sort throughput. Details regarding card sort analysis and common variants are detailed thoroughly elsewhere (Coxon, 1999; Rugg & McGeorge, 2005); here
IDSS-specific considerations are elaborated upon. Generally, IDSS is performed as an open sort, where clusters have not been preassigned labels for participants to sort into (as opposed to a closed sort where groupings are provided labels), using semantic similarity, and the perceived degree of difference in meaning between different definitions as the criterion, with complete linkage analysis
45
.
Figure 4.1 Biomimicry IDSS results. Used as the second implementation of the IDSS method. It shows an absence of clear divisions between 15 definitions for the terms Biology Inspired Design, Biomimicry,
Biomimetics, and Biomimetik. Branch length (height) is measured in Euclidean distance.
IDSS Appropriate Contexts
IDSS is intended to quantify the semantic relationships (or lack thereof) of terms
which feature multiple and overlapping definitions. Identifying whether terms meet this
criteria is done qualitatively, typically through literature review as one encounters
discrepancies in how individual terms are used, or overlap in different terms. As such, IDSS
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is, by the nature of its major use case, typically done to confirm a null hypothesis that the
multiple terms of interest do not have distinct differences in their denotation or connotation,
or that a single term has differences in its definition (possibly, but not necessarily, due to
different contextual schema) . Fundamentally, IDSS provides a means to quantitatively
measure what researchers may observe in reviewing a particular body of literature.
Postanalysis, additional metadata associated with sourced definitions can also be assessed;
examples include evaluating if individual term definitions (potentially irrespective of
individual term) cluster by year published, providing author, or the publication they are
found within.
Identifying Sort Items
In the card sorts for both Games in Learning and Biomimicry, individual definitions
(sort items) were identified through literature searches with search strings including the
Boolean “defin*” added to the terms of interest. Larger sets of item definitions provide
more flexibility in the development of the sort activity, allowing for larger numbers of sort
items (sort sets) and variation of item definitions. The argument has been made consistently,
that sort sets should be comprehensive enough to be representative of the terms of interest,
without being too taxing for participants (Faiks & Hyland, 2000). When possible, for comprehensiveness, it can be useful to compile as complete a set of item definitions as possible, which can then be later reduced for the actual sort set (see next section). Typically, identifying sort items begins naturally over the course of normal review of the literature in research contexts.
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Pseudooutgroups
IDSS produces trees diagrams representing the relationship of individual definitions for one or more terms. These diagrams visualize the Euclidean distance, a statistical measure of coocurrence frequency, between individual items. This allows for the formation of nested hierarchies depicting how individual pairings or clusters of items are then related to other items or sets of items. To identify a meaningful cutoff Euclidean distance for these trees, having a functional pseudooutgroup can be valuable. The function of an outgroup sort item is to provide a threshold for cutting the branches during analysis. In chapter III, the IDSS analysis provides a Euclidean distance threshold at which sort items that definitively belong to the ingroup terms are as close or closer to the outgroup item which sits outside of the terms which the other sort items belong. In chapter III, the definition for the term Games with a Purpose, served as a pseudooutgroup, as while it shared commonalities with the other sort items, its definition featured a notable different in purpose. While outgroups are not required for IDSS, they do allow for a measure for where to cut trees to form meaningful clusters.
Creating Sort Sets
In the GiL sort from Chapter II, the sort items used in the final sort activity, the sort set, was composed of all available sort items found in the Identifying Sort Items step. While comprehensive, this did contribute to a longer amount of time needed to complete the sort activity. Completion time can potentially affect completion rates, error rates, and resultantly final sample size, however this is shown to be limited in the context of traditional item sorts
(Blanchard & Banerji, 2016). The time range for completing the GiL IDSS of 50 items took between 30 to 120 minutes for participants. Providing participants with an expected
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completion time of ~30 minutes seemed to improve completion speeds, but was not
consistently done. While more sort items will obviously necessitate more time for the sort
activity, providing unbounded time could potentially lead to unnecessary overthinking,
restarting sorts to complete them “better”, and frustration leading to failure to complete.
Recommendations on the number of items in a sort set are highly variable, and desired time
to sort activity completion is likely the largest constraint.
Taking this into account, the Biomimicry sort the sort set was reduced to 15 items, conjoined with providing an expected completion time of 5-10 minutes, which proved an
effective down-scaling to achieve this completion time. While complete sort sets allow for
comprehensive analysis, they also will likely result in increased difficulty in reaching desired
sample sizes.
When building sort sets out of a fraction of the total available sort items, a combination of presorting followed by randomization may prove an effective means to
produce meaningful results.
Presorting
Presorting serves as a means to screen your sort activity. Researchers can take larger
item sets (~>20) and perform a sort themselves. It may be useful to be more stringent in
your groups, placing item definitions in shared groups only if they appear to be saying
identical (rather than similar) ideas; this does not require them to be using the exact same
syntax. Presorting may be performed like an ordinary item sort like the final form, but can
also be done through A:B testing or through heuristic decisions. An individual item in each
group (some items may be singletons) can then be used as representative of all items in the
presort group. This form of presorting can condense a large number of sort items into a
smaller, but representative final sort set. Presorts should be completed by all researchers on a
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project to come to agreement on forming these condense representative groups. When randomizing final sort set items (see below), each condensed group can be considered as a single item to be possibly selected, with either a chosen or random constituent of that condensed group then used for the final sort set.
Randomization of Final Sort Set Composition
When forming a sort set out of a fraction of the full list of identified sort items, randomly selecting sort items should be done to remove any selective bias towards particular
definitions. Depending on the study, some particular sort items may be considered “must
haves”, but typically randomly selecting best allows for variation, providing redundant items
have been combined through presorting. Typically the simplest way to randomly select items
is to assign each item (or group of items) a number, and then use an available random
number generator.
Randomization of Final Sort Set’s Item Ordering
As sort items in the final sort activity are blinded to both their corresponding terms
they should be assigned alphanumeric identifiers for the purposes of data entry and analysis.
These should be randomized, or at least pseudorandomized. Using a feature of a definition
that is irrelevant to the numerical labels for analysis purposes, these should also be
randomized. In the GiL sort set, items were simply arranged by their source’s year of
publication, and then numbered. If year of publication is considered to possibly be a metric
in the resulting sorts, randomization can be done completely, as described above.
Sample Size
Limited research exists on meaningful sample sizes for item sort methods. Typically
20 to 30 sorts removes >90% of variation, with further sorts producing limited changes in
50 results (Tullis & Wood, 2004). In addition, some correlation has been shown between number of sort items and adequate sample size (Harloff, Stringer, & Perry, 2013). A major caveat to these studies were they were performed with other item sorts than IDSS. It is unclear at this time if IDSS would have any differences in meaningful sample sizes.
Presentation
The medium by which a sort is performed is primarily dependent on the available resources of the researcher. Online/electronic sorting allow for a streamlining of the acquisition and data collection from larger sample sizes, while traditional pen and paper sorts are less resource dependent.
Electronic Sorting
Numerous sites and software packages for item sorting exist, frequently free to try for limited sort set sizes and sample sizes. Drawbacks to electronic sorts is primarily a lack of direct observation/instruction/assistance, which can contribute to larger numbers of incomplete sorts, however this can easily be compensated for by the larger sample sizes. The major constraint on using this format is the cost of use. When constrained by budgetary limitations, pen and paper car sorts are typically preferable.
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Pen and Paper sorting variations
Traditionally semantic sorts utilize cards that each have a sort item listed on them.
This allows for sorters to readily move cards around, form piles, and then readily record
their groupings. This form of card sorting also allows for researchers to easily aid
participants as needed, and perform debriefing interviews after sort activity completion if
desired. This form of traditional card sort works well when researchers are recruiting
participants individually and can adequately provide supervision during each individual
sorting activity.
Alternatively, as described for the biomimicry sort, the decks of cards containing sort
items can be converted into a large display, allowing numerous participants to simultaneously
complete the sort activity even with limited space. Sort items can be laid out as placards on a
table, affixed to a wall or board, and participants can simply write down their groups to complete the activity.
Conclusions
Following two IDSS collections and analyses, the broad range of applications and
implementations has become apparent. Beyond the conventional item sorts using physical or
electronic cards, other venues for participants can lend themselves to alternative modes of
presentation. Variations in presentation and construction have numerous tradeoffs that can
have effects (of significances unknown) on the successful collection of IDSS data. Further
analysis of these specific variables could prove useful in further refining the IDSS technique
for further use in future semantics research.
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CHAPTER V
GAMEFUL ADVANCE ORGANIZERS
The next three chapters of this dissertation describe integration of game design principles with and education theory to build tools for formal and informal science environments. Each of these products was generated from scratch to meet specific learning outcomes and/or to convey specific concepts. These include:
• The AB Williams Forest Challenge, an advance organizer for the A.B. Williams
Curriculum as part of the CMNH Archives’ project Discover-Explore-Connect:
Engaging with the Environment through Historical Records in the Natural Sciences, in
this chapter. It is part of a larger publication found in the Journal of Interactive
Technology & Pedagogy (Wasman et al., 2019)
• The Pleistocene Preserve lab module, developed to convey population growth
and carrying capacity concepts, published in Evolution: Education and Outreach (
Beatman & Duff, 2019) for ready-use by instructors in intro level university
biology courses (and adaptation for more advanced courses as well as high
school students, and in current use in the University of Akron’s Natural
Science: Biology laboratory course, in Chapter VI.
• Erie, a full-fledged game about how runoff contributes to harmful algal
blooms in Lake Erie, designed in collaboration with a biomimicry fellow for
Great Lakes Biomimicry, in chapter VII.
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In the context of this dissertation, a central premise was the appeal and function of
Games in Learning processes to not only improve motivation and engagement in learning, but also to directly convey complex biological processes. As an extension of the use of manipulatives in early mathematics education, physical games with explicit moving and interacting parts can allow for the direct visualization and understanding of how parts of a system interact and contribute to their net results. Unlike digital games, which use computing power to invisibly process elaborate interactions, analog games require players to directly manipulate both stochastic and deterministic mechanisms to generate outcomes, and this manipulation can directly contribute to understanding.
A.B. Williams Forest Challenge
The A.B. Williams Forest Challenge was developed as a component of a number of educational products that emerged from the Cleveland Museum of Natural History
Archives’ project Discover-Explore-Connect: Engaging with the Environment through Historical Records in the Natural Science. This project digitized 2 linear feet (two feet of front shelf space) of archived material originating from A.B. Williams the Cleveland Metroparks first naturalist to allow access for use. One product of this project was the Discover-Explore-Connect curriculum. As part of its development, a request was put to the author to contribute a gameful experience to it, to meet three specific goals:
1. Provide students with an introduction to using dichotomous keys
2. Familiarize students with the general layout of the area being visited during the
curriculum’s field trip(s)
3. Introduce students to the basics of surveying a location for its biodiversity
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As such, the following basic elements were developed: Cards depicting key features
(leaves and stem budding patterns) of common trees found by A.B. Williams as indicated in his robust personal records, in proportions in each forest community matching that of A.B.
Williams’s records; a map depicting the layout of the woods, along with indications of the different communities found within; a means of measuring (through numbers on cards) and tracking (by a simple table) the number of trees of each type found in each forest community.
The decks of cards were produced using extant art assets produced by A.B. Williams, with additional art created when needed by Wendy Donkin, as well as additional details taken from Williams’ records to create population data. The distribution of cards for each tree was based upon the number of that tree in proportion to other trees. However, due to the number of different trees varying over several orders of magnitudes, the number of cards was proportionate not simply to the number of each type of tree, but instead the order of magnitude. While not originally intended, this scaling in many ways corresponds with the perception of younger children of number scaling on a number line as being logarithmic, the import of which is not entirely clear in this application, but may have relevance in other contexts which can be pursued in future projects.
Thus, trees that occurred fewer than ten times would occur on a single card (which indicated the total number of that tree in that community), while cards such as beeches would occur five or more times in many of the decks, with pseudorandom numbers on each card in that deck summing up to Williams’ total numbers for each type of tree in each community. In this way, the cards’ frequency in decks loosely corresponded to their actual proportions while at the same time keeping deck sizes relatively small, and providing raw data to determine the total counts of trees in each community.
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The components were designed such that they could be printed onto plain paper and
inserted into card sleeves with appropriate backings (a common practice in hobby game
design to allow easy and robust prototyping), or onto heavy cardstock to produce somewhat
less durable, but effective cards. Cards were then used in conjunction with a simplified
dichotomous key that requires only the information on the cards (namely leaf structures and
budding patterns) to identify all of the relevant trees down to either genus or species. As
understanding how to navigate a dichotomous key is the most difficult part of performing a
survey to learn, especially for amateurs, this component was by and large the central focus in
the activities development from start to finish.
Below is the relevant excerpt from the manuscript Branching Out: Using Historical
Records to Connect with the Environment, published in The Journal of Interactive Technology and
Pedagogy (Wasman et al., 2019):
Lesson Plans for ecological literacy
The goal of the Discover-Explore-Connect curriculum is to teach middle school and
high school students the skills necessary to study the natural world around them. Through a
series of lessons, students learn orienteering, species identification, mapping, taking field
notes, and collecting data, with A.B. Williams as their guide. The curriculum is divided into
three major sections: Skills Development and Game-Based Learning; Field Trip to A.B.
Williams Memorial Woods; and the Land Ethic.
The lesson plans in the Skills Development section introduce students to primary
sources and teach them how to identify and analyze different types of primary sources, such
as maps, photographs, and written documents. Students also learn how to interpret and use
different types of information that can be found in scientific documents, such as charts, data
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tables, maps, graphs, and diagrams. Additional lesson plans guide students in making
observations, keeping field notes, finding their way around a map, and recording
observations on a map. Subsequent lessons help students build skills in identifying different
species of wildflowers, birds, and trees.
A unique addition to the curriculum is a game that was developed as an orientation
for field trips to the A.B. Williams Memorial Woods in the North Chagrin Reservation of
the Cleveland Metroparks. Well-designed games have been shown to facilitate engagement
with learning (Dickey, 2005; Abdul Jabbar & Felicia, 2015) and improve meaning-making for
the players (Ermi & Mayra, 2005). The game was designed to deliver three main learning
outcomes: familiarize students with the layout of the A.B. Williams Memorial Woods;
provide a practical tutorial in using dichotomous keys to identify wildlife; and prepare
students to survey plant and animals communities in the forest. By simulating the types of
activities that will take place on their visit to the forest, students should be better prepared
and focused during the field trip (Falk & Dierking, 2000).
For the game’s design and construction, the A.B. Williams Memorial Woods was split into
five regions based on different forest communities: Beech-Maple Association; Northwest
Forest; Southeast Forest; Spurs and Ravines; and Swamp Forest (Figure 5.1).
Using A.B. Williams’ historical survey data of tree distributions, decks of cards were
constructed for each of these regions. Each deck contains between 17 and 39 cards.
Individual tree species for each region are depicted on the cards using illustrations of leaves
that were drawn by A.B. Williams and are part of the digitized collection; drawings of twigs
and buds were created by our project’s graphic designer to provide additional information for species identification. Each card provides enough information for the tree species to be identified using the provided dichotomous key or a field guide, along with a symbol to allow
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for easy sorting of cards based on region, and a number value to indicate how many individual trees that card represents (Figure 5.2)
.
Figure 5.1 Game map showing the different regions of the A.B. Williams Memorial Woods in the North
Chagrin Reservation of the Cleveland Metroparks
For each deck, the number of cards depicting the same tree species is based upon the
proportionate number of those trees surveyed in that region by Williams in the 1930’s
(Williams, 1936). To keep the decks at workable sizes while allowing for representation in
the deck of all the tree species in each region, the ratio of number of cards to total count of
corresponding tree species is arranged upon a log scale, which results in the number of cards
per species corresponding roughly with order of magnitude rather than actual counts.
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Figure 5.2 Example of card showing a single species of tree to be identified
Accompanying these decks are a specially constructed dichotomous key (Figure 5.3) and data collection sheets for student groups to record distribution data (multiple cards of the same species have different numbers to simulate the actual counting activity). Instructors also have an answer and scoring sheet that allows them to track student success rates and completion times, and to check on each group’s work.
Gameplay requires students to work together in teams to effectively and efficiently identify the trees in each region. The game can be played while sitting at a desk, but we encourage teachers to set up the classroom as the forest and have students move around from community to community. The instructor serves as a scorekeeper to track both accuracy and speed, and each student group competes to be best at identifying and surveying tree species. As players progress through surveying each region’s deck, they will grow more proficient in using the dichotomous key to identify trees, and they will acquire information
59 on the distributions of individual tree species throughout the A.B. Williams Memorial
Woods.
Figure 5.3 Dichotomous key to be used for identifying trees
Future work will not only further develop this game, but also expand upon it to include modules that examine wildflowers and songbirds, using resources made available through the digitized A.B. Williams archival material. This game is designed in such a way that it can easily be adapted to any location that has robust biological survey data, with limited adjustment to its design. The game is currently being developed to work as a standalone boxed experience that can be played like a traditional board/card game as opposed to a classroom activity.
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The Discover-Explore-Connect curriculum was introduced at a teacher workshop held at CMNH in February 2018. The 18 teachers who attended the workshop included classroom teachers, homeschool parents, university/college educators, and informal science educators (i.e., museum docents, library media specialists, park naturalists). Educators worked through several lessons and played a test version of the A.B. Williams Forest
Community Challenge Game. We received valuable feedback on the game and the curriculum, and this input will help as we continue to develop both for future use.
Following the workshop, we tested the curriculum and field trip with five classes of
10th-grade biology students from a school within the Cleveland Metropolitan School District.
The teacher had attended the workshop in February and, because her school has an extended academic year, she was able to work through some of the lesson plans and schedule a field trip. Project team members visited the school at the end of May and introduced the A.B. Williams Forest Community Challenge Game to each of the five biology classes during the school day. Fifty students learned how to use the dichotomous key to identify trees featured on the game cards. While challenging at first, by the end of each class we could see that the students had made a lot of progress. The game set the stage for the
June 22nd field trip to the A.B. Williams Memorial Woods. Team members planned a full day of field experiences at four different stations in the woods. The 27 students in attendance, accompanied by their teacher and three chaperones, were broken up into four groups. Each group was led by a member of the project team as they cycled through all four stations and completed the activities, which included counting and measuring trees within a designated area, honing observation skills while focusing on sounds, and learning about the Civilian
Conservation Corps’ efforts to build a shelter in the woods in 1933.
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The field trip was a rewarding experience for everyone. While leading a group into the forest, the leader held up a large leaf that had been found on the ground. One of the students immediately noticed its shape and started to identify it based on the dichotomous key used during the A.B. Williams Forest Community Challenge game in class. Following the field trip, the teacher solicited feedback from her students. One of the students said, “The field trip was very breathtaking…A.B. Williams-I feel like I was walking on his path [and] you saw his nature as it was…I feel like what he did was awesome.”
Our hope is that the Discover-Explore-Connect curriculum will be used in classrooms throughout the school year, so that by April or May teachers and students will be ready for at least one field trip to the A.B. Williams Memorial Woods. Lesson plans for the field trip include mapping and tallying trees and comparing those numbers with what
Williams observed in those same woods 80 years ago. Once back in the classrooms, students turn to lesson plans that help them reflect on the changes that have occurred in the forest ecosystem and encourage them to examine their relationship with the natural world around them.
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CHAPTER VI
MANIPULATIVES ILLUSTRATING LARGE-SCALE BIOLOGY CONCEPTS
The origins of what would become the Pleistocene Preserve lab module was a series of questions relating to flaws in Young Earth Creationists’ (YEC) models of speciation and biodiversity. Young Earth Creationism is a body of belief that, based on a literal interpretation of biblical passages, the Earth and life on earth are only ~6500 years old. In an effort to subvert scientific evidence profoundly challenging this hypothesis, the organizations which push this dogma have labored to incorporate scientific information and/or “scientific information” to create explanations for the existence of the current global biodiversity in both such a limited time span, as well as with a number of constraints on mechanisms of biodiversity (typically mutations are viewed solely as deleterious elements in organisms, making the normal evolutionary processes nonfunctional in the YEC mindset). An initial query was whether the current state of human genetic diversity could conceivably occur within such a limited time span, especially considering that the biblical depiction of humanity involves two crucial genetic bottlenecks, namely at the time of creation as recorded in
Genesis, and following the global deluge of Noah’s day. From this idea, a project was conceptualized in which a simulated gene sequence would, using all available information on mutation rate, be used to generate both rates of deleterious and nondeleterious single nucleotide polymorphisms, to determine how much genetic disparity could be generated both between members of a modern population that has only had close to six millennia of
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time, and the disparity between these modern individuals and their earliest ancestors (the
initial bottleneck population from the deluge).
From this projects conception, the focus shifted instead to examine a more drastic
version of this basic problem of producing diversity, namely generating large numbers of
species from a single biblical kind. A kind is a clade of animals which have descended from a single pairing of animals (or set, depending on the group, biblically) preserved on the ark. In the YEC model, most major mammal groups, typically at the taxonomic family level, are singular kinds. The most profoundly condensed speciation timeline exposes itself with the kind that contains the modern elephants and their extinct predecessors. While there are only three extant species of elephant, the fossil record contains over 185 recognized species of proboscideans, the group that contains mastodons, gomphotheres, as well as close relatives to modern elephants and the early precursors to the megafauna which compose the majority of this taxa/kind (Shoshani & Tassy, 2005).
Below is the abstract and body of the manuscript, Pleistocene Preserve: A Population
Growth Problem of Mammoth Proportions, published in Evolution: Education & Outreach (Beatman
& Duff, 2019). The supplementary materials of the manuscript can be found in Appendix C.
Abstract
A number of processes that involve large numbers are critical to civic scientifc literacy, including many biological topics. Understanding the basic causes of such large-scale processes, such as population growth, speciation, and extinction, are key to engaging evolution and ecology learning. Here we present a teaching module which uses manipulatives to addresses one such topic, population growth, utilizing charismatic organisms, mammoths. The module involves an engaging hypothetical scenario, the
64 restoration of mammoths from extinction (de-extinction) in a captive population being grown to be placed in zoos globally. The module explores both population growth modeling and carrying capacity in relation to both modern elephant conservation and human population growth. We include detailed laboratory instructions for both students and instructors. While we designed and implemented it in a non-majors biology course, further extensions are detailed to utilize more robust modeling and complex scenarios for inquiry- driven majors biology and advanced population ecology courses. This module allows for exploration of a number of concepts within population growth, with natural lead-ins to additional topics. We make the case for mammoths and elephants more generally as charismatic organisms for which students’ familiarity can leverage engagement with many important biological concepts, in this case population growth.
Keywords: Elephants, Mammoths, population growth, de-extinction, carrying capacity, education, deep time, number literacy, manipulatives
Background
Humans have demonstrable difficulties engaging with concepts that involve large numbers, and by extension, large scales in time and space exceeding those they encounter in their daily lives (Catley & Novick, 2009). This becomes increasingly relevant when observing the challenges in engaging the public in the narratives of such biology topics as evolution, biodiversity, conservation, ecology, and population growth, where appreciating the actual scale of the numbers involved is essential (Cheek, 2012; Tibell & Harms, 2017).
Misconceptions undermine both environmental literacy (Zimmermann, 1995) and the wider understanding of scientific processes (Schoon & Boone, 1998). These misconceptions are a
65 major threat as the gap grows between current public understanding of science and the required civic scientific literacy needed for meaningful discourse in government science policy decisions.
At an early age, humans view relative numbers on a logarithmic scale, with the proportionate size (logarithmic scale) of different numbers being visualized rather than the absolute relative size (a linear scale) (Siegler & Opfer, 2003). As children age, they transition from logarithmic to linear as they become familiar with larger numbers. While this transition has been documented in adults and larger numerosities (Rips, 2013; Landy, Charleworth, &
Ottmar, 2014), the majority of studies have focused on child development and smaller numerosities (Siegler, Thompson, & Opfer, 2009). This in turn presents challenges, as humans are dependent on an approximate number system to gauge the relative sizes of larger numbers (above ~5-7) (Hyde, 2011; Nieder & Dehaene, 2009); the accuracy of this system becomes increasingly unreliable at larger number scales, and is not applicable to appreciating the actual numerosities involved (Siegler & Opfer, 2003; Landy, Silbert, &
Goldin, 2013). The lab presented here serves to aid students in recognizing and overcoming these hurdles.
Understanding population growth is a prominent process that involves conceptualizing large numbers. The mechanics, scale, and affecting factors in population growth are fundamental not only in environmental literacy, but also in appreciating large- scale systems including ecology, extinction, and the roles human activity, habitat loss, and climate change play in biodiversity loss and conservation efforts (Trombulak et al., 2004; Jha
& Bawa, 2006). Human population growth is an important concept for students to understand and grapple with, but the direct social relevance and conflicts with religious
66 dogmas can produce problematic cognitive dissonance which can deter students from engaging with the material.
Visualizing the Problem: Elephants and Deep Time
Many commercially available population growth lab modules consist of human- focused population growth mathematics exercises with limited-to no hands-on/experiential learning content (Smith, 2010). A solution to engaging lay audiences with this population growth, while sidestepping the dissonance that can occur with a focus on humans, is to use charismatic non-human models. Elephants and their kin are used here in a teaching laboratory module focusing on using population growth to demonstrate processes which involve large scales and numerosities. The historical, temporal, and geographical ranges of proboscidean groups (See Supplemental file 3 for more details) across their 60 my history exquisitely illustrate a number of concepts which feature the intrinsic psychological challenges involved for biology students and lay audiences of grasping the immense scale, both in terms of space, count of individuals, and time, implicit as the result of considering these animals through deep time. Furthermore, elephants and their kin, as large, prominent, and charismatic animals (Leader-Williams & Dublin, 2000) with a robust fossil record and long lifespan, can be used to explore and model scientific concepts for science communication and education settings.
In addition, the concept of de-extinction, made popular from numerous magazines articles (from such publications as Popular Science, Discover, and Science) and the Jurassic
Park franchise, is incorporated to spark engagement in the classroom, and a willingness to explore the challenges and problems of population growth.
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Pleistocene Preserve: A Simulation Exercise Demonstrating Population Growth
The Pleistocene Preserve laboratory module (PP) is a manipulatives-based simulation exercise designed for use in either introductory biology lab courses or in lecture settings.
Taking as little as one hour to complete in the form presented here, it examines multiple dynamics of population growth and their potential consequences using a hypothetical mammoth de-extinction scenario. While this lab is not about the efficacy, ethics, or plausibility of bringing back mammoths, it does touch on some of these issues incidentally and instructors can choose to explore these concepts further if they wish (See supplemental file 2: Overview guide for instructors). In the configuration presented here and the supplemental material the lab addressed the following learning objectives: A) Understand what exponential population growth is and how to identify when it is occurring; B)
Understand what carrying capacity is and what conditions affect carrying capacity; C) Be able to identify factors that may limit exponential population growth; D) Understand that exponential growth is possible but not biologically realistic; E) Recognize that each species have different population growth rates; F) Connect knowledge gained from studying mammoths to issues involving human population growth and carrying capacity.
In the manipulative-based simulation, students initially determine how long it would take to grow a mammoth population from a single resurrected mating pair to a population in which every (AZA accredited) zoo in the country houses their own pair of breeding mammoths for further captive breeding and rewilding. Students engage in a hands-on exercise using manipulatives to process and calculate population growth through the use of a simple exponential growth model in a resource unlimited environment. It allows them to trace the number of mammoths at any given age interval and the number of new mammoths
68 born in each time interval. Following 100 years of the simulation, the students can then plot the population growth, and use it to determine doubling time. They can then use this to calculate the time required to reach various population landmarks.
The simulation allows students to visualize what exponential growth is and how it works, how population doubling times can be determined from growth data, and how carrying capacities determine the limits to population size. It covers topics corresponding to
NGSS HS.Interdependent Relationships in Ecosystems (HS-LS2-1, HS-LS2-2, HS-LS2-6,
HS-LS2-7, HS-LS2-8, and HS-LS4-6), as well as Ohio’s Learning Standards B.DI.2 and
ENV.ES.1. Prior to working through the simulation module, instructors may choose to use a presentation (Supplemental file 3) to provide students introductory questions and information relating to population growth broadly, as well as the natural history information that informs the simulation’s construction. This presentation provides students with basic reproductive data for mammoths approximated from the life histories of extant species
(aggregated from Loxodonta cyclotis, L. africana, and Elephas maximus), and then walks them through the construction and use of the simulation.
Materials
The only materials needed for this activity are enough copies of the population tracker (see supplemental file 1) that students can form groups of two to three, a means to record population sizes and growth over 100 years (the base simulation operates in ten year intervals) and plastic counting cubes or a functional equivalent such as beads or dry beans.
The countables are used to track the number of mammoths at any given age interval, and the tracker enables students to distinguish the ages of each subset of mammoth and the number of reproductively active animals.
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Simulation Layout
In the module, students are trying to determine how long it would take to produce enough mammoths that every zoological park in the United States would have a mating pair, followed by the world (in a simple scenario where individual zoo mammoths have reproductive access to one another). The number of zoos is an analog for total carrying capacity for the mammoth population, and allows a simple target-driven approach to thinking about growth rates.
Students use a Mammoth Age Tracker sheet (Supplement 1B) to track the number of mammoths at any age, in ten-year intervals, and a supply of plastic cubes or similar manipulatives to use as counters, and a table to fill out with data. The module utilizes manipulatives to visually convey population size, with the aging and growth of the population being directly handled by students allowing a clear connection between the underlying ideas and the physical changes the simulation maps out. The Mammoth Age
Tracker is split into six spaces. Each space represents a ten year interval, with the first two spaces covering the period when they are still calves and do not reproduce. The remaining four spaces (20, 30, 40, and 50) cover the rest of the mammoths’ lifespan, during which they can reproduce, producing two more mammoths in every ten year (one space) interval.
For this exercise, each cube represents two mammoths that were born over the course of ten years (elephants give birth every five years, so in ten years they can give birth to both a male and female; this is a simplified model assuming that males and females are born alternating, rather than randomly with a 50:50 distribution). The rationale for having a single cube represent a pair of mammoths is to both reduce the amount of individual manipulatives needed for the simulation, as well as to simplify the workings of mating pairs;
70 students do not need to consider or track the sex of individual elephants. At the start of the simulation, a single cube, representing the starting pair of mammoths at the start of the simulation, is placed in the second space, to simulate the initial pair having just reached mating age, and the female in the pair about to give birth to the first of this new population of mammoths.
Students advance this cube to the next space in line, representing the passing of ten years, during which two more elephants (represented again, by a single cube) are born and placed in the first space of the tracker. Each time you move all of the mammoths on the track, ten years have passed, and any mammoth pair that has moved into a space on the mature portion of the track gives birth to a new pair. (Figure 1) Students can in each ten year interval count the total number of cubes on the tracker, including those just born, and enter that number into their data table. This continues for ten ten-year intervals so students have population growth data for the first century of the simulation, which they graph out to allow them to roughly calculate the population’s doubling time.
Doubling time is estimated by choosing a time point on their data curve, measuring the number of mammoths at that time, finding the intercept point of the curve for double that number, and then measuring the amount of time passed. They can then use this number to roughly estimate how much time will be needed for the population to hit their target numbers (233 for AZA zoological parks, 10,000 zoos worldwide)
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Figure 6.1 Overview of initial five steps of the simulation. Every decade each mating pair produces one offspring of each sex. Each cube represents one of these pairs of mammoths.
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An additional set of questions follows, which relates the exercises to extant elephant populations, how carrying capacity can be impacted, and the major causes of issue in modern conservation efforts.
Following the end of exercises, a final series of questions highlight the relevance of carrying capacity and population growth to mammoth population size and conservation.
This in turn is then leveraged to also examine how human population size and carrying capacity relate to the materials covered. The initial exercise serves as a backdoor to the impact of human populations. The role of humans in global climate change, ecosystem decline, and habitat destruction are frequently hammered into students without meaningful redress for the cognitive dissonance created. Students’ personal behavior often remains unchanged due to a sense of helplessness or pointlessness in trying to affect global problems, which makes disengaging from the material the common source of dissonance relief
(Festinger, 1962; Cooper, 2007).
Extensions
The complete PP provided only addresses population growth of a single species under optimal conditions. This enables the instructors of general education courses to draw clear conclusions with respect to the primary learning objectives of the PP module while also allowing students to further explore, through exercise questions what the limits of population growth are and what might change the rates of population growth. The PP exercise can be completed in one hour or less making it easily accessible in a laboratory context or as a guided activity in a classroom environment. This lab can be readily extended to further explore the primary learning objectives but also to incorporate numerous additional related biological and ethical principles.
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Figure 6.2 Example of data collected from the PP simulation which students will use to estimate population growth. In this version, the doubling time was calculated using the fifty year time-point and population
numbers to determine that in 15 years the population doubles. Student error will likely lead to variation in this
number within acceptable ranges for the exercise
Faced with estimating population growth of mammoths students should wonder:
How might these growth estimates compare to other organisms? Obviously fruit flies can
increase in population size at a must faster rate but why and how much faster? We suggest,
for advanced biology classes, running the same simulation exercise with another organism
either after the mammoth simulation lab or side-by-side with the mammoth simulation. For
example, this could be done with groups each performing the simulation on different species
or every group could do the mammoth simulation and then could be asked to find natural
history information on a second species and design a simulation that would allow them to
collect data and estimate population growth rates for that other species.
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We suggest using common and easily recognized organisms such as deer, dinosaurs,
eagles or rabbits as examples. The population growth simulation can be altered most simply
by adjusting the following parameters, 1) time to first offspring, 2) # of offspring per female
and 3) interval between offspring. In each case you assume equal numbers of male and
female offspring and you track the female reproduction to estimate total population size. For example, with rabbits one could assume they reach maturity in one year, have 6 offspring (3 females) per female and a lifespan of 6 years (5 reproductive years). The scorecard would be
adapted by changing the intervals from 10 years to 1 year and population growth would be
simulated out for only 10 years of data to be able to measure the growth rate.
Each of these simulations raises interesting ecological and population biology
questions because each species has different exponential growth rates. By contrasting and
comparing a number of additional biological principles can be explored. The PP module
simulation can be adapted to allow students to experience exponential growth and appreciate
some aspect of the relationship of time and large numbers. However, experiencing ideal
growth rates should help them realize that such rates are rarely sustained in the real world.
Population growth rates are affected by numerous internal and external factors. The
questions at the end of the lab seek to have students think through some of the possible
factors that could affect growth rates; a natural extension of the PP module would be to
have students actually modify the input values--the assumptions that allow ideal growth rates
to occur--and run simulations which allow them to measure the effects that such changes
make.
As an example, predation is an important factor that increases mortality rate and thus
decreases population growth rates. A simple model of predation (not scaled with density
75 effects) can be simulated by adding in the assumption that a fixed number of all offspring will not survive to reproductive stage. Usually in elephants, adult size protects from predation, and so subadults are most vulnerable. In our mammoth example if we assume that death before maturation is 20% then we would simulate this by the removal of two of every ten offspring that are produced. This can be done in a number of ways such as using a random number generator (either electronic or a 10-sided die) to screen immatures as they pass the age threshold to maturity, or simply removing the last two of every ten that pass the age threshold. Predation could also include poaching by humans. A scenario for class based on real-world data for elephants might be: if 1 of every 10 mature elephants is killed in a single time interval, how would that affect the doubling time of the elephant population?
Running the simulation again by including this variable will allow students to compare theoretical growth rates with a population growth rate that is modified by a biologically realistic variable. Questions would then include comparing those rates and how much of an effect the variable would have on populations over long periods. Many additional factors which affect mortality and birth rates can be simulated using real-world data either provided by the instructor or obtained by students from the literature.
The PP module can readily be adapted for advanced college courses using an inquiry format. Our module leads students stepwise through the process of doing the simulation but advanced students in an inquiry laboratory or classroom setting can be first presented with a question such as how long would be required to fill 500 zoos with a pair of mammoths starting with just a single sexually mature pair? Without providing them any background information or possibly only limited natural history information, individual or groups of students would then be required to construct a population growth model with the goal of
76 allowing the students to self-discover what information they might need to produce a robust population growth model including age of reproductive maturity, senescence, death, mortality, and starting population (and others) to see how changes alter growth rates and population size. Subsequently students can be provided information to plug into their model, run the simulations, and collect data. Following the idealized simulation under optimal growth conditions, students can propose scenarios/conditions that could occur (e.g. climate change/glaciation, increased human predation, changes in life history), and then propose changes in growth conditions that would correspond with such scenarios to observe the results. This allows students to directly manipulate both hypothetical and real data (e.g. actual current elephant population sizes from recent timepoints to highlight current decline).
Preliminary Student Assessment
The PP module was initially utilized in an undergraduate general education biology class at
The University of Akron in the summer of 2018. The authors introduced the lab and observed and interacted with students as they participated in the laboratory exercise. The lab coordinator and teaching assistant assigned to the lab provided follow-up written feedback about student reactions to the lab. In the following fall semester changes to the lab were implemented based on the pilot lab experience, primarily streamlining and clarifying the lab instructions to allow students to complete the activity more independently.
Supplemental Files:
1. A) Pleistocene Preserve laboratory module for college Gen-Ed or college-prep High-
school biology courses. Included are worksheets, directions and questions for
students. B) Simulation exercise worksheet to print for students.
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2. Overview Guide for Instructors: Additional background information useful for
teaching the lab and for expanding its content.
3. Pre-lab PowerPoint presentation: A suggested PPT presentation to provide pertinent
background information and provide an overview of the laboratory procedures.
Notes are included with the slides as a guide for presenters in the manuscript
proper’s additional files.
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CHAPTER VII
PHILOSOPHY AND PRACTICE: DESIGNING FOR COMPLEX PROCESSES
The process of making games in learning and the correlation to their quality is typically dependent on individual game authors’ knowledge (KEEPCOOL, Eisenack, 2013;
Go extinct, Pennisi, 2016; Wingspan, Roberts, 2019). Of note is that each product is reported
on primarily in the context of their base existence, or general implementation, rather than
the process of design involved. This reflects that game-design is often a self-taught skill,
supplemented by exposure to existing games, or a connection to a community of fellow
game designers. Limited access to these resources can typically lead to unsophisticated
Games in Learning (GiL) product design and further disconnect with quality as judged by
the larger community or, in our point of interest, educational outcomes. As game typically
falls outside of the purview of academic endeavors (Kwok, 2017), the knowledge and
resources associated with it too are hidden to academics pursuing game design. Improving
this access, or awareness of access, could be a potent contributor to games in learning quality
and complexity, especially when dealing with the types of products described within this
dissertation. This chapter seeks to elaborate on the processes of game production, with a
focus on specific design philosophies, how they aid the game-making process, and provide
examples of their application. This chapter explores the making of the game Erie to
demonstrate these ideas.
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Making Games
Games typically go through multiple phases, which can be roughly separated into
Conceptualization, Design, and Development.
The Conceptualization phase is where the initial ideas regarding a game’s theme and/or mechanic are built up. Certain games originate from a compelling thematic idea, while others begin with the actual game mechanics which will engage the player. Naturally,
games also arise from mechanics and themes which are linked. In the context of Games in
Learning products, the theme, namely the learning objectives, provides the seed of the
concept. Before reaching the design phase, building an idea of the general mechanics and
presentation of the knowledge content will speed along the Design phase.
The Design phase comprises of the building of the game proper, starting with the
drafting of the various components, working through the construction of prototypes, and
leading into the beginning of playtesting in Development. Designing a game involves
building the fundamental mechanism and mechanics which they comprise that interact in the
final product. The end goal of the Design phase is a game that can be played, not necessarily
a game that plays well, completely, or is enjoyable whatsoever. The purpose of design is to
create a product that can be further improved upon in development.
Besides the production of art assets and other elements directly related to the
publication/release of the final product (which falls outside the purview of this chapter),
development mainly comprises of playtesting. Playtesting is the process by the designer(s)
and/or developer(s) having playtesting groups play the game to provide feedback on a
game’s fundamental flaws, potentially broken (overpowered or underpowered) strategies,
rulebook clarity, and difficulty(both in terms of the success rate for players, the sense of
challenge, and the accessibility to different age ranges). In GiL contexts, playtesting should
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also include assessment of whether the game successfully delivers its learning outcomes.
Typically each round of playtesting leads to further refining of the game’s rules, mechanics,
balance, and overall design, an iterative design process that requires new prototypes to be
constructed and further playtesting with each new version. In commercial circles, playtest
groups are incentivized to freely provide their feedback over multiple playtests by being
provided gratis copies of the game upon completion. In less commercial settings such as
developing GiL products, playtesting may be constrained to in-house testing by designers
and colleagues. Playtesting is essential in developing games, but has in academic circles been
infrequently acknowledged or pursued, with few individual examples of the idea being
pursued or exemplified, most notably in Eisenack’s KEEPCOOL (Eisenack, 2013; Salen &
Zimmerman, 2006)., Fundamentally, all games need playtesting.
In game design, two simple design philosophies are “fail faster”, and “keep it simple,
stupid (KISS)”. Fail Faster is a basic principle to playtest designs as quickly and frequently as
possible in order to assess quality. This essentially serves to minimize wasted time and effort
spent in developing concepts, mechanisms/mechanics, or rules that prove ineffective,
overly-complex, unpopular or unintuitive to players, or simply nonfunctional. While one may
feel a need to build a completed game before beginning playtesting, individual mechanics can be playtested in isolation for initial assessment, which can further contribute to the quality of the initial “fully-assembled” game.
KISS is a fundamental design concept, in analog games it emerges in a simple
premise: A game should be composed of few, simple mechanics. Through the interaction of
simple elements complexity, and thus the game’s function and appeal, can emerge. While there
are endless exceptions to this rule, at the start, games should be built first out of simple
81 pieces, and then complexity can be layered upon it as its foundation is solidified and balanced (again, through playtesting).
Design Philosophy
Game. Puzzle. Toy. What is a game? Something that is widely considered to be intrinsically engaging, is playful or gameful activity, within the perceptual framework of play or games (an elaborate concept, best discussed elsewhere; in short, what is play for the bully is not play for the victim). Play, describing an activity with playful elements, shifts along an axis of both structuredness and interaction. Activities are relatively structureless past-times, spinning until dizzy, running for the sake of running, they typically come from an entirely internal drive. Further along this spectrum are puzzles, either mental or physical; a crossword puzzle has a fixed solution requiring certain knowledge, a mountain can be climbed similarly.
Puzzles can easily be modified into a form of play consisting of conflict, competition, and/or cooperation; examples include races (mental and physical: both foot races or races to complete a jigsaw puzzle); these are typically things thought of as contests. Finally at the level of game, whether athletic, party, video, or board, the state of the competition, the puzzle, the activity, is intrinsically affected by the actions of the participants, which can often include the game itself. In basketball one not merely needs to dribble a ball across a court and throw it into the hoop, they must do so while members of the other team oppose this action, and the player and their teammates must also prevent their opponents from doing likewise. In games of Risk, Monopoly, Catan (to cite games most familiar to broader audiences), etc. the player is not merely moving about within a puzzle they must solve, they must try and attain a winning state while other players also act, either with or against their endeavors.
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Because well-designed games are, due to their level of interaction and complexity,
stochastic (a jigsaw puzzle, in contrast, has a single final state, and so the solution for a single
puzzle will never change: it is deterministic) and produce rapid feedback, they fit neatly into
the cognitive evaluation theory model of intrinsic motivation. The complex non-solvable
state of the game allows for player autonomy, the rapid feedback to decisions allows for a quick development and distinction of competence, and the interactions with both the dynamics of the game’s mechanics and the other players provide relatedness. A well designed
game of any kind can produce an intrinsic motivation in the player to continue playing it.
Prototyping
Prototyping has been well discussed and elaborated upon in other sources, primarily
with a focus on a digital endproducts (Fullerton, 2014; Bond, 2014). Regardless of desired
outcome, most games begin as pen and paper prototypes, which is typically the primary form
of prototypes for hobby games. Prototypes are typically printed boards, cards, figures, etc., along with generic placeholders for thematically bound things (cubes to represent various resources, figurines to represent players or characters, etc.). Cards are typically printed on copy paper, and then inserted into card sleeves along with normal game cards (frequently
Magic the Gathering™ cards; commons can usually be bought from local dealers for a few cents per card) to give them the rigidity of standard cards, commercially available plastic cubes and dice can be easily acquired for additional components.
Beginner’s frequently build initial prototypes with a quality of visuals and components far greater than necessary, particularly considering that early prototypes are rapidly tested, adjusted, deconstructed, and rebuilt. Early prototypes should be devoid of all but the most simple art (many designers utilize the simple images available from The Noun
Project for iconography and symbology to aid in gameplay) Many of the physical elements
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for paper prototypes can be arranged and templated electronically, allowing for far more
rapid iteration of changes in mechanics, components (card contents and number, board
layouts, etc.), and rules.
Playtesting
Playtesting is the fundamental element of iterative design. Only a foolish writer would believe their first draft to be their final, and just so a game designer would be a fool to
stop at their first version of a game. Playtesting can be performed alone by the designer,
playing as all players at the table, with a group of colleagues with an eye for weakness and
flaws (as this is what you are in fact testing for), or with complete strangers, perhaps without
even your presence (what is called blind-playtesting). Friends and family are often one’s first
available resource for playtesters, but should not be relied on, as they will frequently temper
their criticism in favor of enthusiastic support. The moment a single element of a game has
been prototyped, playtesting can begin. A complete game can allow for a more robust
perception of a games total quality, but individual facets can and should be tested to make
sure they are streamlined and balanced as desired. Playtests can be used to provide feedback
for later iterations, and depending on the designer plays can be analyzed both qualitatively
and quantitatively. While the use of spreadsheets and hard math can be of great use for
balancing a game, many designers rely solely on instinct and repeated plays to develop
balance; either can be adequate.
Playtests do not only test the game’s mechanics and balance, but also are a means to
determine if the game achieves its objectives, whether it be merely to have fun or to convey
the desired learning outcomes. Having playtesters who are both familiar and unfamiliar with
both game design and the educational content can serve well to measure the game’s
development for both of these facets. The rule documents on how to play should also be
84 playtested, and the game should be honed before its final development for publication to assure that the rulebook explains how to play the game so anyone could learn to play solely by reading it, and that any unusual interactions that could occur are accounted for. This parallels in many was the QA process for digital games, hobby games should be thoroughly vetted to be certain that there are no exploits to the rules, or means in which the game can be broken (whether it be unbeatable strategies or chains of actions that produce unlimited resources).
Application in Practice
The game Erie, was developed originally as a collaborative project to fulfill a desire of teachers to include water quality topics in curricula. The goal was to develop a board game that would convey the relationship of runoff on the development of harmful algal blooms in
Lake Erie, and the resultant increased efforts needed in water treatment to adequately handle these challenges.
Content expertise was provided by Ashley Bair and Lamalani Siverts, both of the
University of Akron’s Integrated Bioscience program, to aide my effort to construct the mechanisms of the game. Three major concepts were identified that informed the game’s construction: 1) The generation of runoff by industrial and agricultural sources produces increased algal growth; 2) the ratio of nitrogen to phosphorus in the runoff, in conjunction with weather/temperature, determining whether algal blooms (both including disproportionate concentrations of cyanobacteria and not) occur; 3) the role of water treatment facilities in processing lake water to be potable.
First Versions
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Each of these concepts were used in the generation of the central mechanics of the game. Both Ms. Siverts and myself simultaneously developed general sketches of the appearance and flow of the game (Figure 7.1).
1. Runoff was simulated as being dictated by both the number and proximity of
various types of land use. Each type of land would produce certain amounts of
runoff of both types (Nitrogen and Phosphorus), which could be influenced by their
adjacency to certain land types, and would be produced in rows, with each row
having ~4-6 tiles representing different land usages
a) Each row on the part of the board depicting land use would be increasing
distances from the lake itself represented further distance from the lake itself,
and so the total nitrogen and phosphorus runoff from each row would move
toward the lake over several rounds of play, combining with the new runoff
produced by each successive row in each successive round, to simulate the
delay in runoff reaching the lake from further sources.
2. Lake The total amount of runoff and the proportion of this runoff (N:P), dependent
on the types of land usage, their arrangement, and thus the resultant produced
Nitrogen and Phosphorus (represented in this part of the board as counters of
different colors) would then be evaluated through the use of a track found in part of
the lake portion of the board. This track would use both the amounts of N and P
and their ratio to generate proportion of normal (nonharmful) algae and
cyanobacteria which would be added to the lake itself (again representing normal
algae and cyanobacteria with different colored counters.
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a) The lake was represented by a number of zones on the board, each
containing portions of the counters produced track; in any given round one
zone would be used as the point source for the water treatment complex (3)
3. Treatment As part of the players’ available actions (which would primarily involve
zoning in the portion of the board contributing to runoff (1)), players could improve
upon and spend resources towards water treatment, increasing the amount of algae
that could be taken in at a time, how much could be treated using resources
(Powdered Activated Carbon, or PAC), how much PAC could be stockpiled, and its
effectiveness/efficiency.
Figure 7.1 Early concept sketches of Erie. These early drawings feature all of the major elements of the subsequent versions, including tile spaces for land-use, runoff, N:P ratios, temperature and weather, water treatment, and public health, as well as features such as public perception and funding which would be removed in later versions.
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This layout produced three internally-complex game-components. Each area was notably difficult to design in conjunction with the others due to the challenge of making its interactions mutable by the players (as this was the primary area directly affected by player- actions); it led to an overly-elaborate system whose operations proved prohibitively difficult to design as a functional (let alone engaging) player/learner experience. Additional game components were also sketched out, including having public perception of water quality also impact gameplay, and a separate element of available funding, which would also interact with the three core concepts (Figure 7.2).
This initial conceptualization of Erie proved challenging to assemble, especially as each area was mostly isolated from the next. Thinking of the Fail Faster philosophy, this version was both time-intensive and laborious to get close to a playable state. Without it being playable, and thus playtestable, much of the actual mechanics conceived for this design version were scrapped, and the design process started over.
Final Version
In line with KISS, this meant reducing each of the three areas in complexity.
Fundamentally, the vast majority of the game was changed to be contained within the zoning portion (the area depicting land usage) of the original concept, with tile numbers and proximity directly affecting nearly all other components. As such, the game was changed to the following basic mechanics:
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Figure 7.2 Initial computer-drafted board for Erie. Land use and runoff occurs in tiers, with N and P being discrete units in the runoff/algae mechanics. Water Treatment is divided into three components that can each be improved. Public perception is still a planned component, although its actual mechanisms are still unclear.
1. In randomized subsets, tiles, representing agricultural, industrial, wetland, and water
treatment land use, would be placed onto the board
2. The proximity and connectedness (based on border color of individual tiles, also
randomized) of tiles would produce different interactions and effects
3. These interactions and effects would dictate all other actions deterministically, save
for certain randomized elements (namely, weather effects, tile placement each round,
end of round tiles, and precise distribution of algae types treated each round)
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4. Have the sole effective strategy of the game is to reduce the amount of runoff,
develop water treatment facilities, and limit decreases in the N:P ratio in the runoff.
Once the game had been reduced to a single set of interactions producing all game
effects, balancing and altering the game surrounding this mechanic became profoundly
easier, and the game was able to rapidly be developed to a point where the game could be
easily modified using only changes to the games rule document (rather than components and
composition) and its graphic design. While the tiles were somewhat labor/time-intensive to
produce from chipboard and sticker paper, the remaining products (cards and board) could
be easily printed on printer paper, and thus could be easily modified as the game was further playtested and adjusted. As part of the KISS rationale, the games complexity primarily arose by having each type of tile interact in different ways both with like tiles and different tiles.
While agricultural tiles were best played in large connected clusters, adjacent to wetlands, wetlands need to be kept distant from industry tiles, and water treatment solely need to be placed on specific points on the board, the blue lines found on the board’s grid (Figures 7.3 and 7.4). Thus, while the primary player interaction with the game is rearranging tiles, the way they can interact, and the constant decision-making needed to optimize this
arrangement for all tile types in play, produces a desirable complexity.
Initial internal playtesting allowed for modification of the total counts of each tile type to
fine-tune the difficulty/success rate. In line with competence as a component of intrinsic motivation as outlined in Self-determination theory (described in more detail in Chapter II),
success should be neither nearly guaranteed nor nigh-impossible; balancing a cooperative
game such as Erie necessitates making the randomization of tiles drawn produce variably
difficult puzzles. As a functional takeaway from playtesting, the original square tiles were
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Figure 7.3 Final Erie board left side. This part of the board depicts the time track (the six rounds/months that the game takes place in), The spaces for the decks and discard piles for the city planning cards and funding cards, a round summary reference, and part of the zoning area. Color and graphics have been added for thematic purposes for playtesting with Camp Bioscience children.
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Figure 7.4 Final Erie board right side. This part of the board features the weather track, the remaining zoning tile spaces, the tracks for N:P ratio (which impacts the ratio of normal algae to HAB in the lake), the temperature track (which impacts how much algae must be treated in a round), and the tracker for treating algae cubes when drawn from the lake. Color and graphics have been added for thematic purposes for playtesting with Camp Bioscience children.
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replaced with rectangular ones to allow more space for tile-specific rules to be included on
each individual tile, as well as to facilitate ease of manipulating them on the board. Tinkering with the starting composition of the lake bag, which contains a number of algae cubes of a certain ratio, also contributed to both changing difficulty, as well as how finely its contents
could change. In earlier versions, the number of cubes being placed in the bag each round
was four total, allowing only five different ratios of cubes being added each round; adjusting
this to cover a larger gradient of ratios (changing it from 4:0-0:4 to 8:0-0:8) smoothed
gameplay significantly.
Following initial playtesting, the game’s art assets were developed to be presentable
as better than a standard prototype (added color, improved iconography, professional-
looking construction) for use in the Summer Bioscience camp developed by Carrie Buo of
the Integrated Bioscience program at the University of Akron as part of her dissertation
research. The game was played by two groups of six children, ages 8-10, with mixed amounts
of engagement and understanding, but a general appreciation of how to play the game.
This set of playtests served exceptionally useful in pegging the target age of the game as
being approximately 12+ (although serviceable for these younger children).
Without proper debriefing, however, learning from the game was not expected or
accomplished (Thatcher, 1990; Crookall, 2010). A number of games already exist that
contain elements of biology or broader science topics, but these rarely provide a framework
for possible or intended learning outcomes, which greatly impedes their ability to be used or
be useful as science communication tools.
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CHAPTER VIII
CONCLUSION
Educational games research has the task of connecting learning theory to the reliable production of learning outcomes, especially for use in authentic settings. In order to do so, I argue, research needs to overcome a major obstacle: It must improve how design is conceptualized and discussed. In particular, educational games research must clarify definitions and develop robust ways to share products and processes associated with design so that the community may reliably produce, use, and test educational games and their associated theories. (Gaydos, 2015)
The degree of sophistication of game-design is still limited by the skill and knowledge of those involved in their construction. This expresses itself through the limited variety of game-forms found in gamification products, a focus on memorization and recall in educational games, and a lack of recognition of the potential of games to directly convey ideas and concept, rather than simply serve as a form of flash cards for content review. The growing communities of game-designers that have arisen as a result of the increasing commercial demand for analog games can provide an increasingly accessible resource for educators and education researchers interested in using games not only in more complex ways, but to explore more complex content. For scientists aiming to become more skilled science communicators, recognizing the value of work done in fields including educational psychology, formal and informal education, and understanding how it applies to science communication practices is essential.
The work described in this dissertation seeks to illuminate the beginning of the work that must be done to build the resources and skills needed for constructing Games in
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Learning products that meet the needs of biology and science educators who wish to more
effectively deliver the increasingly complex content needed for audiences to achieve civic
scientific literacy. Bridging the gap between scientists and nonscientists requires that the transfer of skills and knowledge from those who study science communication to the scientists who desire to be science communicators themselves. Games provides a method to demonstrate concepts and ideas in in hands-on simulations that can allow for experiential
learning at home or in the classroom, which can help bridge understanding for concepts that are otherwise abstract enough to prove difficult to intuitively understand.
Implementation
From the work done in this dissertation, a number of important takeaways were
found for the different types of stakeholders involved in GiL work. My recommendations,
by community of practice, are as follows:
Educators
Alternate approaches to Review
While frequently Jeopardy-style review sessions are used in formal environs as direct
quiz/test prep, the potential of using games that are thematically connected to the material
of interest, even without direct learning objective connections, may be serviceable as a
rewarding means of providing students an end of section review, especially if enriched with
instructor provided questions and prompts to further incorporate relevant learned materials.
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In-house design and development
As discussed in Chapter VII, making proper use of game-design knowledge and
practice is essential for building new games or gameful experiences for learning contexts.
Prototyping
While making a presentable endproduct for your audience is a desirable outcome, early development should minimize devoting resources to such work. Building prototypes that are visually unappealing but work for the purposes of play is more than enough for playtesting purposes. Hobby game stores and Hobby craft stores are amenable sources for card-stock and other materials that will be useful for quickly producing cheap prototypes.
Playtesting
Audiences may be the most accessible playtesting community available to designers in education settings, which may be discouraging for producing an effective learning tool.
Keep in mind that testing game mechanics or gameful experiences can be done alone, playing the part of multiple roles, or with assistance from friends, colleagues, coworkers, or volunteers, depending on the venue the product is being developed for, and what audiences are available. In formal settings, fellow instructors or outside-of-class student volunteers can serve as playtesters. In informal settings, institutional volunteers or interested guest can also provide playtesting and feedback.
Collaboration
Local hobby-game communities also frequently house communities of game-
designers, which often provide playtesting resources and can serve as a community of
practice to connect with in developing Games in Learning products. In addition,
familiarizing oneself with the range of styles of modern hobby gaming can be in itself a
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useful resource for conceptualizing ways to better mesh your desired learning outcomes with
game mechanisms.
Game Designers
Framing
Learning Objectives
When developing a game with the explicit goal of having educational content, learning objectives are a must. Learning objectives, measurable skills or blocks of knowledge, are fundamental to providing framing for Games in Learning much as theme and mechanics do in regular games. Learning objectives also can serve as building blocks for the mechanics to be incorporated into the game as one designs it.
Assessment
As learning objectives need to be measurable, incorporating an assessment
component, either intrinsically (as part of the game itself) or extrinsically (this could be
thought of as an added element to playtesting, asking what playtesters have learned, and
comparing this to learning objectives), is essential. As this dissertation was designed to
identify the gaps in integrating game-design and education, assessment was not performed,
but its necessity for ensuring effective GiL design cannot be understated in spite of its
absence here.
Debriefing
Especially without intrinsic assessment, providing a postgame discussion explicitly
highlighting how the game (and its mechanics, when applicable) connect to the learning
objectives is essential. Educators will be far more appreciative of a game with learning
content in it if the learning objectives, connections, and assessment are included.
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Collaboration
Educators are trained in how people learn, how to teach people, and especially how to construct material to address learning outcomes, need for assessment, etc. Just as this dissertation has shown how GiL designers frequently have limited experience with game design, take advantage of the educational design experience of this community of practice.
Educators’ instructional knowledge can serve useful not only in developing games in learning, but improving the construction of rulebooks to facilitate player comprehension and use, and recognizing and addressing UI and accessibility issues which may go unnoticed by designers.
Final Thoughts
Further work must be done to better describe, understand, and refine game design in education and education research so that games can be better developed and established as proven implements in education, science communication, outreach, and more. While the delineation of useful design methods and practices can and will continue to be sourced from existing communities of practice, Games in Learning must develop as its own perspectives on design improve and flourish. Successes in the field are increasingly appearing in the literature but more work must be done to assure that each success carries with it new lessons for future designs, applications, implementation, and evaluation. As a potent tool for science education and communication, games carry with them their own principles of design that must be incorporated into their didactic applications. The coming years will reveal the transdisciplinary potential of Games in Learning as being greater than the sum of its disparate parts.
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REFERENCES
Chapter II Abdul Jabbar, A.I., and Felicia, P. (2015). Gameplay Engagement and Learning in Game- Based Learning: A Systematic Review. Review of Educational Research, 85(4), 740-779. Bazzaz, F., Ceballos, G., Davis, M., Dirzo, R., Ehrlich, P. R., Eisner, T., ... & Mooney, H. A. (1998). Ecological science and the human predicament. Science, 282(5390), 879-879. Becker, K. (2005). How Are Games Educational? Learning Theories Embodied in Games. In Proceedings of the 2005 DiGRA International Conference: Changing Views: Worlds in Play. Bell, A. (1994). Climate of opinion: public and media discourse on the global environment. Discourse & Society, 5(1), 33-64. Bourgonjon, J., De Grove, F., De Smet, C., Van Looy, J., Soetaert, R., & Valcke, M. (2013). Acceptance of game-based learning by secondary school teachers. Computers and Education, 67, 21-35. Bybee, R.W., Taylor, J.A., Gardner, A., Van Scotter, P., Powell, J.C., Westbrook, A., & Landes, N. 92006). The BSCS 5E instructional model: Origins and Effectiveness. Colorado Springs, Co: BSCS, 5, 88-98 Connolly, T. M., Boyle, E. A., Macarthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature review of empirical evidence on computer games and serious games. Computers & Education, 59(2), 661-686. Corti, K. (2006). Games-based Learning: a serious business application. PIXELearning (Vol. 34(6)). Csikszentmihalyi, M. (1990). Flow: the psychology of optimal experience. 1990 New York. NY Harper & Row Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York, Plenum. Dickey, M. (2005). Engaging by Design: How Engagement Strategies in Popular Computer and Video Games Can Transform Instructional Design. ETR&D 53(2): 67-83. Diewald, S., Möller, A., Roalter, L., Stockinger, T., & Kranz, M. (2013, October) Gameful Design in the Automotive Domain-Review, Outlook and Challenges. In Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, (pp.262-265). ACM Eisenack, K. (2013). A Climate Change Board Game for Interdisciplinary Communication and Education. Simulation & Gaming, 44(2-3), 328-348. Ermi, L., and Mäyrä, F. (2005). Fundamental Components of the Gameplay Experience: Analysing Immersion. In Proceedings of the 2005 DiGRA International Conference: Changing Views: Worlds in Play, 15-27.
99
Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25-39. Falk, J. H., & Dierking, L. D. (2000). Learning from Museums: Visitor experiences and the making of meaning.Rowman & Littlefield Festinger, L. (1962). A theory of cognitive dissonance (Vol. 2). Stanford university press. Fischhoff, B. (2013). The sciences of science communication. In Proceedings of the National Academy of Sciences of the United States of America, 110(Supplement 3), 14033-14039. Gaydos, M. (2015). Seriously Considering Design in Educational Games. Educational Researcher, 44(9), 478-483. Gee, J. P. (2007). What video games have to teach us about learning and literacy. Palgrave Macmillan. Glaze, A. L., & Goldston, M. J. (2015). US science teaching and learning of evolution: A critical review of the literature 2000-2014. Science Education, 99(3), 500-518. Gredler, M. (2009). Learning and instruction: Theory into Practice. Prentice hall. Huizinga, J. (1955). Homo ludens : a study of the play-element in culture. Boston Beacon Press. Kapp, K. M. (2012). The Gamification of Learning and Instruction: Game based methods and strategies for training and instruction. Wiley. Kwok, R. (2017). Game On. Nature, 547, 369-371. Lepper, M. R. (1988). Motivational Considerations in the Study of Instruction. Cognition and Instruction, 5(August 2014), 289-309. Lim, C. P. (2008). Spirit of the game: Empowering students as designers in schools? British Journal of Educational Technology, 39(6), 996-1003. Lubchenco, J. (1998). Entering the Century of the Environment: A New Social Contract for Science. Science, 279(5350), 491-497. McKeachie, W. J., & Lin, Y. G. (2002). Creationist vs. evolutionary beliefs: Effects on learning biology. The American Biology Teacher, 64(3), 189-192. Miller, J. D. (1998). The measurement of civic scientific literacy. Public understanding of science, 7, 203-223. Miller, J. D. (2016). Civic scientific literacy in the United States in 2016. International Center for the Advancement of Scientific Literacy: Ann Arbor, MI, USA. Moyer, P.S. (2001). "Are we having fun yet? How teachers use manipulatives to teach mathematics." Educational Studies in mathematics 47, no. 2, 175-197. Murray, M., Mokros, J., & Rubin, A. (1999). Mathematically rich, equitable game software. Mathematics Teaching in the Middle School, 5(3), 180-186.
100
Norman, D. (2013). The design of everyday things: Revised and expanded edition. Basic books. Packer, J., & Ballantyne, R. (2004). The synergy of education and entertainment. Annals of Leisure Research, 7(1), 54-71. Pine, B. J., & Gilmore, J. H. (1999). The experience economy: work is theatre & every business a stage. Harvard Business School Press. Proctor, M. D., & Marks, Y. (2013). A survey of exemplar teachers’ perceptions, use, and access of computer-based games and technology for classroom instruction. Computers and Education, 62, 171-180. Ramirez, D., & Squire, K. (2014). Gamification and Learning. In The Gameful World: Approaches, Issues, Applications, (pp. 629-652). Rennie, L. J., & Williams, G. F. (2006). Communication about science in a traditional museum: visitors’ and staff’s perceptions. Cultural Studies of Science Education, 1(4), 791- 820. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. The American Psychologist, 55(1), 68-78. Ryan, R. M., Rigby, C. S., & Przybylski, A. (2006). The motivational pull of video games: A self-determination theory approach. Motivation and Emotion, 30(4), 347-363. Salen, K., & Zimmerman, E. (2004). Rules of Play: Game Design Fundamentals. MIT press. Shaffer, D. W., Squire, K. R., Halverson, R., & Gee, J. P. (2005). Video games and the future of learning. Phi delta kappan, 87(2), 105-111. Spranger, M. (1989). Role of education in policies and programs dealing with global climate change. Sea Grant Network Reprint, University of Washington Taber, K. S. (2006). Beyond constructivism: The progressive research programme into learning science. Studies in Science Education, 42, 125-184 Tankard Jr, J. W., & Ryan, M. (1974). News source perceptions of accuracy of science coverage. Journalism quarterly, 51(2), 219-225. Taspinar, B., Schmidt, W., & Schuhbauer, H. (2016). Gamification in education: a board game approach to knowledge acquisition. Procedia Computer Science, 99, 101-116. Thompson, P. W. (1992). Notations, conventions, and constraints: Contributions to effective uses of concrete materials in elementary mathematics. Journal for research in mathematics education, 123-147. Trumbo, C. (1996). Constructing climate change: claims and frames in US news coverage of an environmental issue. Public understanding of science, 5, 269-283. Vygotsky, L. S. (1978). Mind in society: The development of higher mental process. Harvard university press.
101
Weber, J. R., & Schell Word, C. (2001). The Communication Process as Evaluative Context: What Do Nonscientists Hear When Scientists Speak? BioScience, 51(6), 487-495. Wideman, H. H., Owston, R. D., Brown, C., Kushniruk, A., Ho, F., & Pitts, K. C. (2007). Unpacking the potential of educational gaming: A new tool for gaming research. Simulation & Gaming, 38(1), 10-30 Wiles, J. R., & Alters, B. (2011). Effects of an educational experience incorporating an inventory of factors potentially influencing student acceptance of biological evolution. International Journal of Science Education, 33(18), 2559-2585.
Wilson, K. M. (1995). Mass media as sources of global warming knowledge. Mass Comm Review, 22, 75-89. Wilson, K. M. (2000). Communicating climate change through the media. Environmental risks and the media, 201-217.
Chapter III Abt, C. C. (1968). Games for learning. In Simulation games in learning (pp. 65-84). Sage Abt, C. C. (1970). Serious Games. Viking Annetta, L. A. (Ed.). (2008). Serious Educational Games: From Theory to Practice. Sense. Annetta, L. A. (2010). The “I’s” have it: A framework for serious educational game design. Review of General Psychology, 14(2), 105-112. Bellotti, F., Berta, R., De Gloria, A., Ott, M., Arnab, S., de Freitas, S., & Kiili, K. (2011). Designing Serious Games for Education: from Pedagogical principles to Game Mechanisms. In Proceedings of the 5th European Conference on Games Based Learning (Vol. 2, pp. 1-9). Academic Publ. Ltd. Beatman, T.R., & Duff, R.J. (2019). Games in Learning: Shedding Light on a Problematic Taxonomy. EAI Endorsed Transactions on Serious Games (Online First) Blank, A. (1999). Why Do New Meanings Occur?: A Cognitive Typology of the Motivations for Lexical Semantic Change. In Historical Semantics and Cognition (pp. 61-89). Bogost, I. (2007). Persuasive Games: The Expressive Power of Videogames. MIT Boocock, S. S., & Schild, E. (Eds.). (1968). Simulation Games in Learning. Sage Publications, Inc. Breuer, J., & Bente, G. (2010). Why so serious? On the relation of serious games and learning. Eludamos. Journal for Computer Game Culture, 4(1), 7-24. Caponetto, I., Earp, J., & Ott, M. (2014). Gamification and Education: A Literature Review. In Proceedings of the European 8th Conference on Games Based Learning (Vol. 1, pp. 50-57).
102
Corti, K. (2006). Games-based Learning: a serious business application. Informe de PIXELearning , 34(6), 1-20. Crookall, D. (2010). Serious Games, Debriefing, and Simulation/Gaming as a Discipline. Simulation & Gaming, 41(6), 898-920. Deterding, S. (2013). Gameful Design for Learning. T & D, 67(7), 60-63. Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From Game Design Elements to Gamefulness: Defining “Gamification”. In Proceedings of the 15th International Academic MindTrek Conference: Envisioning Media Environments, 9-15. Dichev, C., Dicheva, D., Angelova, G., & Agre, G. (2014). From gamification to gameful design and gameful experience in learning. Cybernetics and Information Technologies, 14(4), 80- 100. Diewald, S., Möller, A., Roalter, L., Stockinger, T., & Kranz, M. (2013). Gameful design in the automotive domain: review, outlook and challenges. In Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 262-265). ACM. Djaouti, D., Alvarez, J., & Jessel, J.-P. (2011). Classifying serious games: The G/P/S model. In Handbook of research on improving learning and motivation through educational games: Multidisciplinary approaches (pp. 118-136). Egenfeldt-Nielsen, S. (2005). Beyond Edutainment: Exploring the Educational Potential of Computer Games. (Doctoral dissertation). Gordon, A. K. (1970). Games For Growth; Educational Games in the Classroom. Science Research Associates. Gredler, M. (2009). Learning and instruction: Theory into Practice. Prentice hall. Griepp, M. (2016). Hobby Games Market Hits $700M. ICV2. Hecker, C. (2010). Achievements considered harmful. In game Developers Conference 2010. Hirumi, A., Appelman, B., Rieber, L., & Van Eck, R. (2010). Preparing Instructional Designers for Game-Based Learning: Part 1. TechTrends, 54(4), 27-38. Ke, F. (2016). Designing and integrating purposeful learning in game play: a systematic review. Educational Technology Research and Development, 64(2), 219-244 Kiryakova, G., Angelova, N., & Yordanova, L. (2014). Gamification in Education. In Proceedings of the 9th International Balkan Education and Science Conference. Michael, D. R., & Chen, S. L. (2006). Serious Games: Games That Educate, Train, and Inform. Thomson Course Technology. Prensky, M. (2001). Digital Game-Based Learning. New York: McGraw-Hill
103
Rughiniș, R. (2013). Gamification for Productive Interaction Reading and Working with the Gamification Debate in Education. In The 8th Iberian Conference on Information Systems and Technologies CISTI 2013, 1-5. Seaborn, K., & Fels, D. I. (2015). Gamification in theory and action: A survey. International Journal of human-computer studies, 74, 14-31. Slussareff, M., Braad, E., Wilkinson, P., & Strååt, B. (2016). Games for Learning. In Entertainment Computing and Serious Games (pp. 189-211). Springer, Cham. Susi, T., Johannesson, M., & Backlund, P. (2007). Serious Games-An Overview. Elearning, 73. von Wangenheim, C. G., & Shull, F. (2009). To game or not to game? IEEE Software, 26(2), 92-94. Wessa, P., & Office for Research Development and Education. (2017). Hierarchical Clustering (v1.0.4) in Free Statistics Software (v1.2.1). Wilkinson, A. M. (1991). The scientist's handbook for writing papers and dissertations. Prentice Hall PTR. Wilkinson, P. (2016). Brief history of serious games. In Entertainment computing and serious games (pp. 17-41). Springer, Cham. Wu, W. H., Hsiao, H. C., Wu, P. L., Lin, C. H., & Huang, S. H. (2012). Investigating the learning-theory foundations of game-based learning: A meta-analysis. Journal of Computer Assisted Learning, 28(3), 265-279. Zyda, M. (2005). From visual simulation to virtual reality to games. Computer, 38(9), 25-32.
Chapter IIII Bedwell, W. L., Pavlas, D., Heyne, K., Lazzara, E. H., & Salas, E. (2012). Toward a taxonomy linking game attributes to learning: An empirical study. Simulation & Gaming, 43(6), 729-760. Bissonnette, S. A., Combs, E. D., Nagami, P. H., Byers, V., Fernandez, J., Le, D., ... & Tanner, K. D. (2017). Using the biology card sorting task to measure changes in conceptual expertise during postsecondary biology education. CBE—Life Sciences Education, 16(1), ar14. Blanchard, S. J., & Banerji, I. (2016). Evidence-based recommendations for designing free- sorting experiments. Behavior research methods, 48(4), 1318-1336. Chaffin, R., & Herrmann, D. J. (1984). The similarity and diversity of semantic relations. Memory & Cognition, 12(2), 134-141. Coxon, A. P. M. (1999). Sorting data: Collection and analysis (Vol. 127). Sage.
104
Faiks, A., & Hyland, N. (2000). Gaining user insight: a case study illustrating the card sort technique. College & research libraries, 61(4), 349-357. Harloff, J., Stringer, A., & Perry, J. (2013). Sample size requirements for stable clustering of free partition sorting data. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 117(1), 93-105. Hider, P. (2009). Library resource categories and their possible groupings. Australian Academic & Research Libraries, 40(2), 105-115. Nielsen, J., & Sano, D. (1995). SunWeb: User interface design for Sun Microsystem's internal web. Computer Networks and ISDN Systems, 28(1-2), 179-188. Roth, R. E. (2013). An empirically-derived taxonomy of interaction primitives for interactive cartography and geovisualization. IEEE transactions on visualization and computer graphics, 19(12), 2356-2365. Rugg, G., & McGeorge, P. (1997). The sorting techniques: a tutorial paper on card sorts, picture sorts and item sorts. Expert Systems, 14(2), 80-93. Steinberg, D. D. (1967). The Word Sort: An instrument for semantic analysis. Psychonomic Science, 8(12), 541-542. Tullis, T., and Wood, L. (2004) How Many Users Are Enough for a Card-Sorting Study?. In Proceedings UPA (Vol. 2004).
Chapter V Abdul Jabbar, A.I., and Felicia, P. (2015). Gameplay Engagement and Learning in Game- Based Learning: A Systematic Review. Review of Educational Research 85(4), 740-779. Beatman, T.R., & Duff, R.J. (2019). Pleistocene Preserve: A Population Growth Problem of Mammoth Proportions. Evolution: Education & Outreach, 12(16). Dickey, M. (2005). Engaging by Design: How Engagement Strategies in Popular Computer and Video Games Can Transform Instructional Design. ETR&D 53(2): 67-83. Ermi, L., and Mäyrä, F. (2005). Fundamental Components of the Gameplay Experience: Analysing Immersion. In Proceedings of the 2005 DiGRA International Conference: Changing Views: Worlds in Play, 15-27. Falk, J. H., & Dierking, L. D. (2000). Learning from Museums: Visitor Experiences and the Making of Meaning. Rowman & Littlefield. Hayhoe, D. (1990). Sorting-based menu categories. International Journal of Man-Machine Studies, 33(6), 677-705 Wasman, W., Beatman, T.R., Donnelly, S., Flinn, K.M., Spencer, J., and Trimbath, R.J. (2019) Branching Out: Using Historical Records to Connect with the Environment. The Journal of Interactive Technology & Pedagogy, 14.
105
Williams, A.B. (1936). The Composition and Dynamics of a Beech-Maple Climax Community. Ecological Monographs 6: 317-408.
Chapter VI Beatman, T.R., & Duff, R.J. (2019). Pleistocene Preserve: A Population Growth Problem of Mammoth Proportions. Evolution: Education & Outreach, 12(16). Catley, K. M., & Novick, L. R. (2009). Digging deep: Exploring college students’ knowledge of macroevolutionary time. Journal of Research in Science Teaching, 46(3), 311-332. Cheek, K. A. (2012). Students’ understanding of Large Numbers as a Key Factor in their Understanding of Geological Time. International Journal of Science and Mathematics Education, 10, 1047-1069. Cooper, J. (2007). Cognitive Dissonance: 50 years of a classic theory. Sage. Festinger, L. (1962). A theory of cognitive dissonance (Vol. 2). Stanford university press. Hyde, D. C. (2011). Two Systems of Non-Symbolic Numerical Cognition. Frontiers in Human Neuroscience, 5, 1-8. Jha, S., & Bawa, K. S. (2006). Population Growth, Human Development, and Deforestation in Biodiversity Hotspots. Conservation Biology, 20(3), 906-912. Landy, D. H., Charlesworth, A., & Ottmar, E. (2014). Cutting in line: discontinuities in the use of large numbers in adults. In Proceedings of the 36th Annual Conference of the Cognitive Science Society (Vol. 36, pp. 815-820). Landy, D., Silbert, N., & Goldin, A. (2013). Estimating large numbers. Cognitive Science, 37(5), 775-799. Leader-Williams, N., & Dublin, H. T. (2000). Charismatic Megafauna as “flagship species.” In A. Entwistle & N. Dunstone (Eds.), Priorities for the Conservation of Mammalian Diversity: Has the Panda Had Its Day? (pp. 53-81). Cambridge University Press. Nieder, A., & Dehaene, S. (2009). Representation of Number in the Brain. Annual Review of Neuroscience, 32(1), 185-208. Rips, L. J. (2013). How many is a zillion? Sources of number distortion. Journal of Experimental Psychology: Learning Memory and Cognition, 39(4), 1257-1264. Schoon, K. J., & Boone, W. J. (1998). Self-efficacy and alternative conceptions of science of preservice elementary teachers. Science Education, 82(5), 553-568. Shoshani, J., & Tassy, P. (2005). Advances in proboscidean taxonomy & classification, anatomy & physiology, and ecology & behavior. Quaternary International, 126, 5-20. Siegler, R. S., & Opfer, J. E. (2003). The development of numerical estimation: Evidence for multiple representations of numerical quantity. Psychological Science, 14(3), 237-243.
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Siegler, R. S., Thompson, C. A., & Opfer, J. E. (2009). The logarithmic-to-linear shift: One learning sequence, many tasks, many time scales. Mind, Brain, and Education, 3(3), 143- 150. Tibell, L. A. E., & Harms, U. (2017). Biological Principles and Threshold Concepts for Understanding Natural Selection: Implications for Developing Visualizations as a Pedagogic Tool. Science and Education, 26(7-9), 953-973. Trombulak, S. C., Omland, K. S., Robinson, J. A., Lusk, J. J., Fleischner, T. L., Brown, G., & Domroese, M. (2004). Principles of conservation biology: Recommended guidelines for conservation literacy from the Education Committee of the Society for Conservation Biology. Conservation Biology, 18(5), 1180-1190. Zimmermann, M. (1995). Science, Nonscience, and Nonsense: Approaching Environmental Literacy. Johns Hopkins University Press
Chapter VII Bond, J. G. (2014). Introduction to Game Design, Prototyping, and Development: From Concept to Playable Game with Unity and C. Addison-Wesley Professional. Crookall, D. (2010). Serious games, debriefing, and simulation/gaming as a discipline. Simulation & gaming, 41(6), 898-920. Eisenack, K. (2013). A Climate Change Board Game for Interdisciplinary Communication and Education. Simulation & Gaming, 44(2-3), 328-348. Fullerton, T. (2014). Game design workshop: a playcentric approach to creating innovative games. AK Peters/CRC Press. Gaydos, M. (2015). Seriously Considering Design in Educational Games. Educational Researcher, 44(9), 478-483. Kwok, R. (2017). Game On. Nature, 547, 369-371. Salen, K., & Zimmerman, E. (2006). The game design reader. USA: MIT Sida.(2007). Thatcher, D. C. (1990). Promoting learning through games and simulations. Simulation & Gaming, 21(3), 262-273.
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APPENDICES
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APPENDIX A
CHAPTER III SUPPLEMENT
Card Sort Definitions This table corresponds to the item definitions indicated in the card sort cluster analysis found in Figure 3.2. Each row provides the number pseudorandomly assigned to each item definition (ordered by first author’s surnames alphabetically), the definition extracted from the text, the author(s), the year of publication, and the term the definition belongs to.
Definition Information # Definition Authors Year Term games [that] have an explicit and carefully thought-out educational purpose and are not intended to be played 1 Abt 1970 Serious Games primarily for amusement. This does not mean that serious game are not, or should not be, entertaining. electronic/computer-access games that are not designed 2 for commercial purposes but rather for training users on a Annetta 2010 Serious Games specific skill set electronic/computer-access games that are not designed Serious 3 for commercial purposes but rather for training users on Annetta 2010 Educational K-20 content knowledge Games games that are expressly designed with explicit educational Bellotti et al. 2011 4 purposes Serious Games introduction of game elements in the design of learning Games for 5 Bellotti et al. 2013 processes Learning a hybrid genre that relies heavily on visual material, on Buckingham 6 narrative or game-like formats, and on more informal, less 2000 Edutainment & Scanlon didactic styles of address the process of entertaining people at the same time as you McIntosh et 7 are teaching them something, and the products, such as 2017 Edutainment al. television programmes or software, that do this The application of gaming technology, process, and design to the solution of problems faced by businesses and other organizations. Serious games promote the transfer and Cook 2005 8 cross fertilization of game development knowledge and Serious Games techniques in traditionally non-game markets such as training, product design, sales, marketing, etc. about leveraging the power of computer games to captivate 9 and engage end-users for a specific purpose, such as to Corti 2006 Serious Games develop new knowledge and skills
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provide structured and immersive problem-solving experiences that enable the development of both Ke (citing 2016 Learning 10 knowledge and ‘ways of knowing’ to be transferred to Shaffer) (2006) Games the situations outside of the original context of gaming or learning Deterding Gameful 11 the design goal of designing for gamefulness 2011 et al Design Deterding 12 use of game design elements in non-game contexts 2011 Gamification et al Deterding 13 the design strategy of using game design elements 2011 Gamification et al activity structures in which players use a body of Devries and Learning 14 knowledge or set of skills as resources in their 1973 Edwards Games competition with other players designing systems that are intrinsically motivating and Dichev et Gameful 15 fun to use, by applying those techniques that game 2014 al. Design designers use to keep the players immersed and engaged The use of game design elements in non-game contexts Diewald et Gameful 16 with the goal of achieving long-term effects based on 2013 al. Design intrinsic motivation any piece of software that merges a non-entertaining Djaouti et 17 2011 Serious Games purpose (serious) with a video game structure (game). al. computer games developed for educational use or titles Egenfeldt- Educational 21 often finding their way to educational settings both the 2005 Computer Nielsen fake, bad, ambitious and superb Games Traditional non-electronic game-like activities developed Egenfeldt- Educational 20 for educational use spanning board games, simulations, 2005 Nielsen Games role-playing games etc. a sub-group of educational computer games that are heavily criticized. Typically edutainment titles are characterized by using quite conventional learning Egenfeldt- 19 2005 Edutainment theories, providing a questionable game experience, Nielsen simple gameplay and often produced with reference to a curriculum the overarching perspective of games for something else Egenfeldt- 18 2005 Serious Games than just entertainment Nielsen games which provide users with specific skills development or reinforcement learning within an 22 ESRB 2013 Edutainment entertainment setting, where skill development is an integral part of product a type of computer-based instruction designed to Hannafin & 23 1988 Edutainment motivate the gamer using game characteristics Peck any interactive, digital game that is designed specifically Instructional 24 to facilitate the achievement of a specified set of learning Hirumi et al 2010 Games outcomes that meet educational goals a form of service packaging where a core service is enhanced by a rules-based service system that provides Huotari and 25 feedback and interaction mechanisms to the user with an 2011 Gamification Hamari aim to facilitate and support the users’ overall value creation
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a process of enhancing a service with affordances Huotari and 26 for gameful experiences in order to support user’s 2012 Gamification Hamari overall value creation use of gaming attributes (assigned challenge and a compelling form of positive and/or negative Susi et al. (cited 2007 27 reward system) to overcome a designated problem from I/ITSEC Serious Games (2006) or deficiency, and provide appropriate feedback to Conference) the user about their efforts. using game-based mechanics, aesthetics and game 28 thinking to engage people, motivate action, Kapp 2012 Gamification promote learning, and solve problems. the use of ideas and ways of thinking that are inherent in games. Game inspired design does not Gameful 29 Kiryakova et al 2014 express in adding game elements, but rather in Design using of playful design. games designed for a specific purpose related to training, not just for fun. They possess all game 30 Kiryakova et al 2014 Serious Games elements, they look like games, but their objective is to achieve something that is predetermined. games that targets the acquisition of knowledge as its own end and foster cognition that is either Learning 31 Klopfer et al. 2009 generally useful or useful within an academic Games context Koivisto & 32 the phenomenon of creating gameful experiences 2014 Gamification Hamari the use of game metaphors, game elements and ideas in a context different from that of the games 33 Marczewski 2013 Gamification in order to increase motivation and commitment, and to influence user behavior a collective term referring to digital games that are purposefully designed to help players learn about a Martinez-Garza Games for 34 particular topic. A digital game is an interactive 2015 et al Learning form of entertainment in which a player’s experience is mediated by computer software. games that do not have entertainment, enjoyment, 35 Michael & Chen 2005 Serious Games or fun as their primary purpose a sub-category of serious games. They do not only focus on imparting knowledge and raising Persuasive 36 awareness about a topic or an issue, but also on Orji et al 2013 Games attitude or behavior change in a desirable direction, e.g. towards a more healthy lifestyle The use of computer game and simulation 37 approaches and/or technologies for primarily non- Corti 2006 Serious Games entertainment purposes Digital Game any marriage of educational content and computer 38 Prensky 2001 Based games; any learning game on a computer or online Learning simple gameplay to support productive interaction Gamification 39 Rughiniș 2013 for expected types of learners and instructors (of education) games specifically designed for learning as opposed Games for 40 Slussareff et al 2016 to the use of games in learning Learning a learning process that uses as the main Game-based 41 pedagogical tool a specific game which helps to Sousa & Rocha 2017 Learning arise and develop skills games that engage the user, and contribute to the 42 achievement of a defined purpose other than pure Susi et al 2007 Serious Games entertainment (whether or not the user is
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consciously aware of it) a branch of serious games that deals with Game-based 43 Susi et al 2007 applications that have defined learning outcomes Learning a class of games in which people, as a side effect of Von Ahn & Games with a 44 playing, perform tasks computers are unable to 2008 Dabbish Purpose perform Von game applications that have defined learning Game-based 45 Wangenheim & 2009 outcomes Learning Shull 46 the process of making activities more game-like Werbach 2014 Gamification game-design elements are added in the hopes of incentivizing a particular process thereby adding 47 Wiggins 2016 Gamification intrinsic motivation in a given gamified process which invariably uses extrinsic rewards games are used in the classroom to enhance learning and teaching; the use of actual games in Game-based 48 education; the intentional use of digital or non- Wiggins 2016 Learning digital games or simulations for the purpose of fulfilling one or more specific learning objectives the outcome of integrating effective learning principles into game environments for the purpose Game-assisted 49 Wu et al. 2012 of utilizing engaging elements of games as a means Learning for improving the quality of education a mental contest, played with a computer in accordance with specific rules, that uses entertainment to further government or corporate 50 Zyda 2006 Serious Games training, education, health, public policy, and strategic communication objectives
Definitions Bibliography
Abt, C. C. (1968). Games for learning. In Simulation games in learning (pp. 65-84). Abt, C. C. (1970). Serious Games. Viking Annetta, L. A. (Ed.). (2008). Serious Educational Games: From Theory to Practice. Sense. Annetta, L. A. (2010). The “I’s” have it: A framework for serious educational game design. Review of General Psychology, 14(2), 105-112. Bellotti, F., Berta, R., De Gloria, A., Ott, M., Arnab, S., de Freitas, S., & Kiili, K. (2011). Designing Serious Games for Education: from Pedagogical principles to Game Mechanisms. In Proceedings of the 5th European Conference on Games Based Learning (Vol. 2, pp. 1-9). Academic Publ. Ltd. Bellotti, F., Kapralos, B., Lee, K., Moreno-Ger, P., Berta, R., & Barreto, A. B. (2013). Assessment in and of Serious Games: An Overview. Advances in Human-Computer Interaction, 11. Buckingham, D., & Scanlon, M. (2000). That is edutainment: media, pedagogy and the market place. In International Forum of Researchers on Young People and the Media, Sydney.
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Cook, D. (2005). Serious Games: A broader definition. Retrieved September 5, 2017, from http://www.lostgarden.com/2005/05/serious-games-broader-definition.html Corti, K. (2006). Games-based Learning: a serious business application. Informe de PIXELearning , 34(6), 1-20. Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From Game Design Elements to Gamefulness: Defining “Gamification” Dichev, C., Dicheva, D., Angelova, G., & Agre, G. (2014). From gamification to gameful design and gameful experience in learning. Cybernetics and Information Technologies, 14(4), 80-100. Diewald, S., Möller, A., Roalter, L., Stockinger, T., & Kranz, M. (2013). Gameful design in the automotive domain: review, outlook and challenges. In Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (pp. 262-265). ACM. Djaouti, D., Alvarez, J., & Jessel, J.-P. (2011). Classifying serious games: The G/P/S model. In Handbook of research on improving learning and motivation through educational games: Multidisciplinary approaches (pp. 118-136). https://doi.org/10.4018/978-1- 60960-495-0.ch006 Egenfeldt-Nielsen, S. (2005). Beyond Edutainment: Exploring the Educational Potential of Computer Games. Entertainment Software Rating Board. (2013). Entertainment Software Rating Board. Retrieved September 5, 2017, from http://www.esrb.org/ratings/ratings_guide_gamecenter.aspx Hannafin, M. J., & Peck, K. L. (1988). The design, development, and evaluation of instructional software. Macmillan. Hirumi, A., Appelman, B., Rieber, L., & Van Eck, R. (2010). Preparing Instructional Designers for Game-Based Learning: Part 1. TechTrends, 54(4), 27-38. Huotari, K., & Hamari, J. (2011). "Gamification” from the perspective of service marketing. CHI 2011 Workshop Gamification, (January), 11-15. https://doi.org/table of contents ISBN: 978-1-4503-1637-8 doi>10.1145/2393132.2393137 Huotari, K., & Hamari, J. (2012). Defining gamification. In Proceeding of the 16th International Academic MindTrek Conference on-MindTrek ’12 (p. 17). https://doi.org/10.1145/2393132.2393137 Kapp, K. M. (2012). The Gamification of Learning and Instruction: Game based methods and strategies for training and instruction. Wiley. Ke, F. (2016). Designing and integrating purposeful learning in game play: a systematic review. Educational Technology Research and Development, 64(2), 219-244.
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Kiryakova, G., Angelova, N., & Yordanova, L. (2014). Gamification in Education. Proceedings of the 9th International Balkan Education and Science Conference. Klopfer, E., Osterweil, S., & Salen, K. (2009). Moving learning games forward. Cambridge, MA: The Education Arcade. Koivisto, J., & Hamari, J. (2014). Demographic differences in perceived benefits from gamification. Computers in Human Behavior, 35, 179-188. Marczewski, A. (2013). What’s the difference between Gamification and Serious Games? Retrieved August 30, 2017, from https://www.gamasutra.com/blogs/AndrzejMarczewski/20130311/188218/Whats_the _difference_between_Gamification_ and_Serious_Games.php Martinez-Garza, M. M., & Clark, D. B. (2015). Games for Learning. In Encyclopedia of Science Education (pp. 437-440). McIntosh, C., Willis, J., & Girbau, N. M. (Eds.). (2017). Cambridge Business Dictionary: Meanings & Definitions. Dictionary.cambridge.org. Michael, D. R., & Chen, S. L. (2006). Serious Games: Games That Educate, Train, and Inform. Thomson Course Technology. Orji, R., Mandryk, R. L., Vassileva, J., & Gerling, K. M. (2013). Tailoring persuasive health games to gamer type. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems-CHI ’13, 2467-2476. https://doi.org/10.1145/2470654.2481341 Prensky, M. (2001). Digital Game-Based Learning. New York: McGraw-Hill.
Rughiniș, R. (2013). Gamification for Productive Interaction Reading and Working with the Gamification Debate in Education. The 8th Iberian Conference on Information Systems and Technologies CISTI 2013, 1-5. Shaffer, D. W. (2006). Epistemic frames for epistemic games. Computers and Education, 46(3), 223-234. Slussareff, M., Braad, E., Wilkinson, P., & Strååt, B. (2016). Games for Learning. In Entertainment Computing and Serious Games (pp. 189-211). Springer, Cham. Sousa, M. J., & Rocha, Á. (2017). Game based learning contexts for soft skills development. Advances in Intelligent Systems and Computing, 570, 931-940. Susi, T., Johannesson, M., & Backlund, P. (2007). Serious Games-An Overview. Elearning (Vol. 73) von Ahn, L., & Dabbish, L. (2008). Designing games with a purpose. Communications of the ACM, 51(8), 57 von Wangenheim, C. G., & Shull, F. (2009). To game or not to game? IEEE Software, 26(2), 92-94.
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Werbach, K. (2014). (Re)Defining Gamification: A Process Approach. In International Conference on Persuasive Technology (pp. 266-272). Springer, Cham. Wiggins, B. E. (2016). An Overview and Study on the Use of Games, Simulations, and Gamification in Higher Education. International Journal of Game-Based Learning, 6(1), 18-29. Wu, W. H., Hsiao, H. C., Wu, P. L., Lin, C. H., & Huang, S. H. (2012). Investigating the learning-theory foundations of game-based learning: A meta-analysis. Journal of Computer Assisted Learning, 28(3), 265-279. Zyda, M. (2005). From visual simulation to virtual reality to games. Computer, 38(9), 25-32.
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APPENDIX B
CHAPTER IIII SUPPLEMENT
Card Sort Definitions This table corresponds to the item definitions indicated in the card sort cluster analysis found in Figure 4.1. Each row provides the number pseudorandomly assigned to each item definition (ordered by first author’s surnames alphabetically), the definition extracted from the text, the author(s), the year of publication, and the term the definition belongs to.
Definition Information # Definition Authors Year Term
Rawlings et 1 the imitation of a product or process found in nature 2012 Biomimicry al inspiration from biological systems to maximize durability Bio Inspired 2 and efficiency, and from technical systems to drive Trotta 2011 Design innovation. 3 the abstraction of good design from nature Vincent 2003 Biomimetics
the translation of knowledge from biological research into 4 Speck 1999 Biomimetik technical applications (translated from the German) wide-spread movement in design for environmentally- Bio Inspired 5 conscious sustainable development that often results in Helms et al. 2009 Design innovation the mimicking of nature to make a customisable, synthetic Rawlings et 6 2012 Biomimicry alternative, tailored for a desired application al a new discipline that studies natures best ideas and then 7 Benyus 1998 Biomimicry imitates the designs and process to solve human problems Vanaga & 8 when a biological principle is the source for design ideas 2015 Biomimicry Blumberga is to see how organisms have adapted for survival, taking into account both environmental and physiological selection 9 Vincent 2016 Biomimetics pressures, and to transfer those adaptations, in appropriate form, into a technical context. Decoding of 'inventions of animate nature' and their 10 Biokon.de 2007 Biomimicry innovative implementation in technology It is an approach to problems of technology utilizing the 11 Vincent 2009 Biomimetics theory and technology of the biological sciences. uses analogies to biological systems to develop solutions for Bio Inspired 12 Helms et al 2009 engineering problems Design the technical emulation of biological forms, processes, 13 Benyus 2013 Biomimicry patterns, and systems
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a measure to achieve ecologically sustainable products, i.e. 14 the products that do not harm the environment through their Ivanic et al 2015 Biomimicry
production, usage or disposal [design] inspired by or based on biological structures or Merriam- Bio Inspired 15 1992 processes Webster [design]
Definitions Bibliography
Benyus, J. M., & Morrow, W. (1998). Biomimicry: innovation inspired by nature. Perennial.
Benyus, J. M. 2013. A biomimicry primer. In Biomimicry Resource Handbook: A Seed Bank of Best Practices. Biomimicry 3.8.
“Bioinspired,” Merriam-Webster dictionary, 1992. Available: https://www.merriam- webster.com/dictionary/bioinspired.
Biokon.de. (2019). BIOKON - What is Bionics. Available at: http://www.biokon.de/en/bionics/what-is-bionics/
Helms, M., Vattam, S. S., & Goel, A. K. (2009). Biologically inspired design: process and products. Design studies, 30(5), 606-622.
Ivanić, K. Z., Tadić, Z., & Omazić, M. A. (2015). Biomimicry–an overview. The holistic approach to environment, 5(1), 19-36.
Rawlings, A. E., Bramble, J. P., & Staniland, S. S. (2012). Innovation through imitation: biomimetic, bioinspired and biokleptic research. Soft Matter, 8(25), 6675-6679.
Speck, T. (1999): Botanik. In: Lexikon der Biologie, Bd. 2, Heidelberg. http://www.biologie.uni-freiburg.de/biomimetik/PDF/wissen.pdf
Trotta, M. G. (2011). Bio-inspired design methodology. International Journal of Information Science, 1(1), 1-11.
Vanaga, R., & Blumberga, A. (2015). First steps to develop biomimicry ideas. Energy Procedia, 72, 307-309.
Vincent, J. F. (2003). Biomimetic modelling. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 358(1437), 1597-1603.
Vincent, J. F. (2009). Biomimetics—a review. Proceedings of the institution of mechanical engineers, part H: Journal of Engineering in Medicine, 223(8), 919-939.
Vincent, J. F. (2016). The trade-off: a central concept for biomimetics. Bioinspired, Biomimetic and Nanobiomaterials, 6(2), 67-76.
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APPENDIX C
CHAPTER VI SUPPLEMENT
Supplemental File 1: Pleistocene Preserve: A Population Growth Simulation
Agenda: • Review some basic ideas about conservation and populations growth. • Perform the population growth class activity • Collect and record data from activity • Answer questions in lab report
Glossary: Carrying Capacity-The maximum size a population can reach that the environment can sustain indefinitely given the amount of food, water, habitat, etc., available De-extinction-creating an organism or population of organisms belonging to an extinct species Evolutionary fitness-successfully passing along traits (genes) via reproduction Exponential Growth-Growth where the increase in the total number of organisms is proportionate to the current size of the population Extinction-The total elimination of a group of organisms, normally a species Functional Extinction-The reduction in size of a group of organisms to the point where reproduction and recovery is impossible Generations-The time from the birth of one generation to the birth of the next generation; typically slower for larger organisms, faster for smaller Heredity-the biological process whereby genetic factors are transmitted from one generation to the next Natural selection-A process in which individuals that have certain inherited traits tend to survive and reproduce at higher rates than other individuals because of those traits. Population-All the organisms of a species in a particular geographical area that can reproduce with one another Reproduction-Formation of new cells or new organisms, passing along genes Rewilding-Introducing a sufficient number of organisms into their former environment to reestablish a population Simulation-A representation of a situation or problem with a similar but simpler model or a more easily manipulated model in order to determine experimental results.
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Background Information:
Populations of living organisms, whether single-celled bacteria, redwood trees, or mice, have the ability to grow exponentially. Exponential growth is why populations grow at the rates they do, until growth is limited. For any given population, this limit is called the carrying capacity. The speed of exponential growth is constrained by the rate at which a particular species can reproduce, and plays a huge role in the ability of a population to recover from population losses. One way to calculate growth rates is to subtract the number of individuals that die each year in a population from the number of individuals born each year. If there are more being born than dying, the population is growing and vice versa.
Mammoths went extinct ~4500 years ago from the combination of habitat loss due to changes in climate and predation by humans. Due to their charismatic nature, the occurrence of well-preserved frozen specimens, and public interest, there has been much discussion in certain circles of targeting this group for de-extinction, resurrecting an organism/specie that has gone extinct typically through clonal processes using an Asian elephant mother (Asian elephants are very closely related to mammoths). There are a number of obstacles beyond the complex biology of de-extinction that would make this process both complex, expensive, difficult, and time-consuming. Most notably, there is the challenge of producing sufficient numbers of an extinct organism to allow a stable population to be reestablished.
Elephants live approximately 60-70 years, and become sexually mature around age 13 (just like humans!). Because they compete for females, males typically don’t get to mate until they’re at least 25, as bigger, older, healthier elephants outcompete younger, smaller elephants. Gestation takes nearly two years, and mothers feed and wean their calves over several years (~3) before mating again. Female elephants can have calves their entire adult lives, although many enter a state of reproductive senescence in the last decade or so, during which they no longer have any more offspring.
Learning objectives: 1. Understand what exponential population growth is and how to identify when it is occurring 2. Understand what carrying capacity is and what conditions affect carrying capacity 3. Be able to identify factors that may limit exponential population growth 4. Understand that sustained exponential growth is possible but not biologically realistic 5. Recognize that each species have different population growth rates 6. Connect knowledge gained from studying mammoths to issues involving human population growth and carrying capacity.
Laboratory Directions
Assignment: You are on a team planning to target mammoths for de-extinction. You are in charge of coordinating the growth of this population, and to save on initial resources, you want to start by producing just two mammoths, a single mating pair. The first thing you need to do, is to determine how long it would take, from this single pair, to produce enough new mammoths to begin rewilding them. Your initial target is to produce enough mating pairs that every
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accredited zoo in America (233) each have a mating pair, as a foundation for further breeding and reintroduction to the wild.
For the sake of simplicity we will assume that each zoo will have just two adult mammoths (a male and a female), and when offspring reach reproductive age they are moved to zoos without mammoths.
Your primary task is to figure out how long it would take before every zoo has at least two mammoths.
On the attached page are two worksheets to help you perform these calculations.
Laboratory handout
You have a Mammoth Age Tracker sheet to track the number of mammoths at any age, in ten-year intervals, a supply of plastic cubes to use as counters, and a table to fill out with data.
The Mammoth Age Tracker is split into six spaces. The first two spaces, labeled 0 and 10, represent the first ten years of a mammoth’s life, when they are still calves and do not reproduce. The remaining four spaces (20, 30, 40, and 50) cover the rest of the mammoths’ lifespan, during which they can reproduce, producing two more mammoths in every ten year (one space) interval.
For this exercise, each cube represents two mammoths that were born over the course of ten years (elephants give birth every five years, so in ten years they can give birth to both a male and female; this is a simplified model, so we’re assuming that males and females are born alternating, rather than randomly with a 50:50 distribution).
To begin your population grown simulation, place one cube in the age 10 space. This represents the starting pair of mammoths at the start of our simulation. The female is just about to give birth to the first of this new population of mammoths! You have a table where you can track the total number of mammoths in your population every ten years.
Because our mammoths are producing a pair of mammoths every ten years, your population will only experience meaningful changes in population size every decade in this activity. Each time you move all of the mammoths on the track, ten years have passed. After each move, count up the total number of cubes, including those just born, and enter that number into the data table.
While you’re doing the activity, pay attention and take note of how many young mammoths (10 or younger) there are in comparison to adult mammoths.
1. Take the cube representing the starting pair of mammoths and move it one space (ten years) forward, to the age 20 space. You’ll note that this space and those following it have a different symbol. As soon as pair of mammoths (represented by a single cube, remember!) crosses from the age 10 to the age 20 space, they are old enough to have baby mammoths!
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As soon as mammoths move from age 10 to age 20, immediately place a single cube on the Age 0 space, this represents the two mammoths that were born in that ten year jump. Record in your table that the total number of cubes at year 10 is 2. 2. Keep going. Every time you move all of the cubes one space on the Age Tracker, ten years pass. Your initial pair is now 30, and your newer pair will be 10. Since your initial pair are adults, they’ve had another pair of mammoths, so put a new cube on the Age 0 space. After 20 years you now have three total cubes. 3. Do the same thing you did before, move ALL the cubes forward one space (ten years). Now the pair that were ten have passed into adulthood, and so now you have two pairs of mammoths old enough to reproduce, so add TWO cubes to the age zero space. Don’t forget to record your population on the table. 4. Keep going! Every time, move all the cubes on the board forward, and then add one cube for every cube that has moved to a space age 20 or higher. When cubes move off the board, to age sixty, those mammoths have died. Make sure that you don’t count these mammoths in your population count for that year. 5. Repeat moving the cubes forward, giving birth to new mammoths, and having old mammoths die, until you reach year 100. Don’t be surprised that the number of mammoths is going to get pretty big!
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Table 1: Record the results of your population growth experiment here. Year females Total mammoths
0 1 2
10 2 4
20 3 6
30 5 10
40
50
60
70
80
90
100 Population at 100 years
Now you have data for how many pairs of mammoths you would have after 100 years. Remember, this was a simulation where every mammoth was born healthy, there were no dangers to the mammoths at any age, they never got sick, and every mammoth found a partner to mate with. Don’t forget that each cube you counted is two mammoths.
On the next page there is a sheet of graph paper, with the axes already labeled. The y-axis (going up and down) is labeled for the mammoth population size. The x-axis (side to side) is how much time has passed. Make a line graph showing the growth of your total mammoth population over this 100 year period (NOT THE NUMBER OF CUBES). Try to make a continuous curved line connecting the data points (don’t simply connect each point to the next with a straight line).
Image sources: Mammoth in population growth chart: The Royal BC Museum in Victoria (Canada). The display is from 1979, and the fur is musk ox hair. Photo by Flying Puffin (MammutUploaded by FunkMonk) [CC BY-SA 2.0 (https://creativecommons.org/licenses/by-sa/2.0)], via Wikimedia Commons
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Name______
Now that you have your graph, we can calculate how long it takes for the mammoth population to double in size.
1. Choose a single time-point (60, 70, or 80 years are good ones to choose), and check how many mammoths there are at that time. 2. Take that number (the number of mammoths at that time), and double it. 3. Go to the Y-axis (up and down) and find the point where that number would fall (the number from step two). 4. Now, draw a dotted line straight across (left to right) your graph from that point on the y- axis until you connect with your data line. 5. Now, from where that dotted line connects with your data line, draw a second dotted line straight down until you hit the x-axis. 6. Record what number (approximately) (this spot is on your x-axis). This is the time at which the population has doubled from the time-point you chose in Step 1. 7. Subtract your starting time-point (from Step 1) from the number this vertical dotted-line crossed your x-axis (from Step 6), and you’ll have how much time it took for your mammoth population to double in size. 8. You can double-check your math by doing this all again with a different starting reference point; the doubling time should be approximately the same each time.
With your doubling-time determined, now you don’t have to do the simulation to figure out how many mammoths there are past 100 years. Every time your doubling-time passes, the mammoth population doubles assuming exponential growth continues (in this simulation, it does!).
Now that you know this, you can calculate the answers to some of the lab activity questions below.
Questions: Q1. After 100 years what is the total size of the mammoth population? (Remember that the cubes you have counted each represent TWO mammoths).
Q2. How many years did you calculate is required for the population to double?
Q3. a. To fill all 233 zoos in the United States with a pair of mammoths (466 mammoths total) each how many additional doubling times need to pass after the 100 year point before you hit or exceed that target (You don’t need to calculate precisely how long it takes to reach exactly 466 elephants)? How many years total is this?
b.To fill all 10,000 zoos worldwide, how many years from the initial reproductive pair would be required (solve this the same way you did part a, but now your target number is much higher)?
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Q4. a. Assuming doubling time is the same as you have calculated, how long would it take to bring the mammoth population to 300,000, the current number of elephants in Africa (Again, you can solve this the same way you solved Q3)?
b. To 20,000,000, the estimated number of elephants in Africa 200 years ago?
Q5. What trait of the elephant’s life history do you think is the most important in limiting the natural growth rates of elephants/mammoths? (i.e. if you could change one thing about the elephants life-cycle which do you think would increase growth rates the most). Explain why. Why do you think the trait is like it is?
Q6. This simulation used a very simple model to chart mammoth population growth. Name two factors that the simulation doesn’t model that would make it more accurate. How might you incorporate them into the simulation?
Q7. It should be obvious that exponential growth of mammoths (or humans or any organism) can’t continue forever. List at least two things that you think would limit mammoth populations from growing continuously. Explain how these things would limit population growth.
Q8. Human populations are growing at a rate of 1.09% a year (e.g. a population of 1000 will result in a population of 1090 in the next generation). This is a doubling time of 64 years. What are some factors that you think are currently limiting human population growth today? Since populations continue to grow despite these factors limiting growth what other factors might ultimately bring human population growth to a halt?
Q9. If we were able to bring back species that have gone extinct, what are three challenges/problems with de-extinction you can come up with?
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Supplemental File 2: Pleistocene Preserve Lab Overview Guide for Instructors
This lab module is designed to allow students to better conceptualize exponential population growth, using a simple simulation of woolly mammoth life history to visualize the increase in population size over a period of a century. The associated slideshow presentation includes annotation providing explanations of concepts to be reviewed in conjunction with each slide.
CONSTRUCTION
The following section explains how to prepare the various materials for the lab module.
1. Print out the Population Tracker sheet in sufficient numbers that there is a sheet for
every pair of students. Laminating is recommended if repeated use is expected.
2. For each pair of students, provide the lab document as well as enough counting
cubes (or any small item that can be easily moved from section to section of the
Population tracker; cubes are recommended as they will not roll around)
3. The provided presentation introduces both the concepts of the lab and walks
students through the first five time intervals (40 years) of the simulation.
a. Each slide also has a comment section with supplemental
information to inform instructors on how to present the slides. It is
recommended that instructors familiarize themselves with this content
before teaching.
PRESENTATION
Learning objectives:
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A. Understand what exponential population growth is and how to identify when it is
occurring
B. Understand what carrying capacity is and what conditions affect carrying capacity
C. Be able to identify factors that may limit exponential population growth
D. Understand that exponential growth is possible but not biologically realistic
E. Recognize that each species have different population growth rates
F. Connect knowledge gained from studying mammoths to issues involving human population growth and carrying capacity.
Agenda:
1. Review some basic ideas about conservation and populations growth.
2. Present information on population growth, de-extinction, and the simulation
parameters
3. Perform first four intervals along with students using final slides of presentation.
4. Students perform the population growth class activity
5. Collect and record data from activity
6. Answer questions in lab report
Additional Background Information:
Mammoths and elephants in general
There are currently ~370k African elephants (Loxodonta cyclotis and africana,
recognized as separate species only in the past decades, Roca et al., 2001)), a decrease from
500k observed between 2007 and 2014 (Chase et al., 2016). Asian elephants (Elephas
maximas) number approximately 50-70k, including 16k in nonreproductive captive
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populations (Lair, 1997; Kempf & Santiapillai, 2000) however these numbers are outdated
and unreliable (Blake & Hedges, 2004; Sukumar, 2006). At the turn of the 20th century,
these numbers were close to 2m and 200k for Loxodonta and Elephas, respectively (Milner-
Gulland & Beddington, 1993; Sukumar and Santiapilliai, 1996). Prior to this, historical ranges
were even larger, and population sizes could have been larger by over a full order of
magnitude for both genera. Using historical habitat sizes, the combined carrying capacity in
Africa for both Loxodonta species is estimated to be ~27 million as recently as 1800 (Milner-
Gulland & Beddington, 1993). In the past six millennia, the habitable range of E. maximas has been reduced by 95%, suggesting a carrying capacity before major habitat loss of ~1 million (Olivier, 1978; Sukumar and Santiapillai, 1996). In both genera, a major component of their recent population decreases is attributed to the loss of habitat due to growth of human populations, and then only more recently compounded by the pressure of the ivory trade (Chase et al., 2016; Milner-Gulland & Beddington, 1993; Douglas-Hamilton, 1987).
The genus Mammuthus, the mammoths, alone, occupied northern Eurasia and North
America over a period of approximately 6my as the modern elephant genera established their populations in their original expanded home ranges (Rohland et al., 2010). In the past ~50ka alone, there have been identified over 4000 Mammuthus primigenius fossils across ~1500 localities in Eurasia (Puzachenko et al., 2017), representing an infinitesimally small fraction of the populations of those periods. What becomes evident is that prehistoric populations of extant elephants had much higher numbers over much of their existence, and these population sizes were likely reflected in many, if not all other proboscidea species.
Modern elephants reach the beginning of sexual maturity at approximately 12-15 years of age, however males typically do not begin to mate until approximately the age of 30
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due to competition with older males (Shoshani & Tassy, 1996). Gestation time for elephants
is approximately two years, followed by three years of weaning of the calves, and the average
interval between pregnancies is five years (Shoshani & Tassy, 1996). Similar numbers (with
times correlating roughly with animal body mass, as evidenced by numbers for E. maximas)
can be assumed for most other elephant species (Shoshani & Tassy, 1996). Ignoring the
times required for speciation (see next section), the condensed timeline of YEC describes
wooly mammoths (for sake of simplicity, all species within Mammuthus, not merely M.
primigenius) as only existing for a period of roughly two hundred years. This produces a
model delineating a time in which a population of a single species would form, grow, and
dwindle. This can then be tested using information from extant, closely related species’
information on population sizes and natural history (supporting population growth rate)
combined with taphonomy and fossil data.
Fossil records show that elephants and their extinct relatives, mammals classified in
the order Proboscidea, have existed since 60 MYA, consisting of as many as 185 presently
identified species spread across 42 genera. Since ~20 MYA, proboscideans spread out of
Africa into every continent except Australia and Antarctica, and served as ecosystem
engineers globally, playing a significant role in carbon cycling and reshaping ecological
succession patterns (Doughty, 2017; Jones et al., 1994; Haynes, 2012). Their evolution and
migration patterns coincide with a number of important events including continental drift,
climate change, and astronomical Milankovitch cycles (Bennett, 1990; Tassy and Shoshani,
1996, Van der Made, 2010).
For 20-35 million years, large proboscideans have been keystone species which have shaped the habitats they exist in. Throughout this time, multiple species have coexisted both
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geographically and temporally, with reduced resource competition due to niche partitioning,
both within genera and between families (Calandra et al., 2008; Rivals et al., 2015; Zhang et
al., 2017). These sorts of cohabitations, widespread distributions, and long occupation times
for each individual species speak to not only the prolific distribution of proboscideans across the planet, but also imply the sheer scale of their population sizes at any given time. Unlike modern elephant population loss, these species extinctions were caused by a conjunction of human hunting and climate change cycles that regularly would reduce habitable ranges of proboscideans, particularly in temperate climates.
De-extinction.
While not a focal point of the Pleistocene Preserve laboratory module provided instructors may be interested in exploring the science and ethics of de-extinction. 2014 story on PBS used the term “Pleistocene Park” and raised the question of re-introducing the Wooly
Mammoth. (https://www.pbs.org/newshour/science/welcome-pleistocene-park-russian-
scientists-say-high-chance-cloning-woolly-mammoth).
The following articles may be useful for reading assignments and for further in-class
discussion.
• Yin, Steph. "We Might Soon Resurrect Extinct Species. Is It Worth the Cost?". New York Times. 2017. Mar20. Minteer B. Is it right to reverse extinction?. Nature News. 2014 May 15;509(7500):261. • Robert A, Thévenin C, Princé K, Sarrazin F, Clavel J. De‐extinction and evolution. Functional Ecology. 2017 May;31(5):1021-31. • Shultz D. Should we bring extinct species back from the dead. Science. 2016. • Sandler R. The ethics of reviving long extinct species. Conservation Biology. 2014 Apr;28(2):354-60.
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Pleistocene Preserve Answers
Here are possible answers for the student questions found in the lab activity (Supplement
1A)
Q1. After 100 years what is the total size of the mammoth population? (Remember that the cubes you have counted each represent TWO mammoths).
Provided students have not made any errors in performing the activity, they will reach a final population at 100 years of 196 elephants.
Q2. How many years did you calculate is required for the population to double?
As students can choose from a range of times for determining doubling time in the activity, their answer can vary, but the typical doubling time will be approximately 15 years.
Q3. a. To fill all 233 zoos in the United States with a pair of mammoths (466 mammoths total) each how many additional doubling times need to pass after the 100 year point before you hit or exceed that target (You don’t need to calculate precisely how long it takes to reach exactly 466 elephants)? How many years total is this?
A single doubling-time will take the population well past this number. So approximately 115 years.
b.To fill all 10,000 zoos worldwide, how many years from the initial reproductive pair would be required (solve this the same way you did part a, but now your target number is much higher)?
This should require 7 doubling times from the 100th year, to reach a population of 25088 elephants; approximately 205 years
Q4. a. Assuming doubling time is the same as you have calculated, how long would it take to bring the mammoth population to 300,000, the current number of elephants in Africa (Again, you can solve this the same way you solved Q3)?
This would require five additional doubling times from the previous answer, taking us to a total of 280 years from time zero
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b. To 20,000,000, the estimated number of elephants in Africa 200 years ago?
Six additional doubling times, a total of 370 years.
Q5. What trait of the elephant’s life history do you think is the most important in limiting the natural growth rates of elephants/mammoths? (i.e. if you could change one thing about the elephants life-cycle which do you think would increase growth rates the most). Explain why. Why do you think the trait is like it is?
The biggest limitation to growth rate for elephants is the age at which they can begin reproducing, which correlates pretty well with their large size. Either growing to their adult size faster, or having a smaller final adult size would both be great answers. Having offspring more frequently would also be a suitable answer, although the gestation rate again is directly tied to the size of newborn elephants. Anything to cut down on elephant size would likely lead to faster population growth.
Q6. This simulation used a very simple model to chart mammoth population growth. Name two factors that the simulation doesn’t model that would make it more accurate. How might you incorporate them into the simulation?
Predation (primarily affecting young elephants), sickness, food limitations, poaching (primarily affecting adult elephants), actual randomized sex of offspring, access to mating partners/fragmentation of habitat, natural disasters (drought, flooding, etc.)
Predation, sickness, and poaching are all variations discussed above. Randomizing sex of offspring would necessitate making each elephant born its own token, and its sex could be represented by different colored tokens. Number of newborn elephants would be determined by the number of adult females.
Q7. It should be obvious that exponential growth of mammoths (or humans or any organism) can’t continue forever. List at least two things that you think would limit mammoth populations from growing continuously. Explain how these things would limit population growth.
This question differs from question 6 in that it is asking about the real world effects. Especially it addresses density dependent effects, namely depletion of resources by overgrazing or habitat loss, encounters with humans, Needed available space per elephant becomes a constraint once populations reach a certain size, students should be able to connect this idea to being what carrying capacity looks like in the real world.
Q8. Human populations are growing at a rate of 1.09% a year. This is a doubling time of 64 years. What are some factors that you think are currently limiting human population growth
133 today? Since populations continue to grow despite these factors limiting growth what other factors might ultimately bring human population growth to a halt?
This question seeks to connect the lesson to human populations, and human population growth to question 7. Expect much of the same answers as question 7, the goal of this question is to have students recognize that the ideas of exponential growth and carrying capacity apply to more than just elephants.
Q9. If we were able to bring back species that have gone extinct, what are three challenges/problems with de-extinction you can come up with?
How do we know the de-extinct animal is what it just like the extinct animal? Can we acquire intact DNA? What are the difficulties of the actual initial cloning process? Can we find an organism that will enable us to de-extinct our organism of interest? Does the environment the organism lived in still exist? What happens to the organisms in the environment they’re reintroduced to? How much would this cost? Do we have any right to bring an organism back?
Supplement 2 References:
1. Bennett KD. Milankovitch cycles and their effects on species in ecological and evolutionary time. Paleobiology. 1990; 16(1):11-21.
2. Blake S, Hedges S. Sinking the flagship: the case of forest elephants in Asia and Africa. Conservation Biology. 2004 Oct; 18(5):1191-202.
3. Calandra I, Göhlich UB, Merceron G. How could sympatric megaherbivores coexist? Example of niche partitioning within a proboscidean community from the Miocene of Europe. Naturwissenschaften. 2008 Sep 1; 95(9):831-8.
4. Chase MJ, Schlossberg S, Griffin CR, Bouché PJ, Djene SW, Elkan PW, Ferreira S, Grossman F, Kohi EM, Landen K, Omondi P. Continent-wide survey reveals massive decline in African savannah elephants. PeerJ. 2016 Aug 31; 4:e2354.
5. Doughty CE. Herbivores increase the global availability of nutrients over millions of years. Nature ecology & evolution. 2017 Dec; 1(12):1820.
6. Douglas-Hamilton I. African elephants: population trends and their causes. Oryx. 1987 Jan; 21(1):11-24.
7. Haynes G. Elephants (and extinct relatives) as earth-movers and ecosystem engineers. Geomorphology. 2012 Jul 1; 157:99-107.
8. Jones CG, Lawton JH, Shachak M. Organisms as ecosystem engineers. InEcosystem management 1994 (pp. 130-147). Springer, New York, NY.
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9. Kemf E, Santiapillai C. Asian elephants in the wild. WWF-World Wide Fund for Nature; 2000.
10. Lair RC. Gone astray. The care and management of the Asian elephant in domesticity. RAP; 1997.
11. Milner-Gulland EJ, Beddington JR. The exploitation of elephants for the ivory trade: an historical perspective. Proceedings of the Royal Society of London. Series B: Biological Sciences. 1993 Apr 22; 252(1333):29-37.
12. Olivier R. Distribution and status of the Asian elephant. Oryx. 1978 Nov; 14(4):379- 424.
13. Puzachenko AY, Markova AK, Kosintsev PA, van Kolfschoten T, van der Plicht J, Kuznetsova TV, Tikhonov AN, Ponomarev DV, Kuitems M, Bachura OP. The Eurasian mammoth distribution during the second half of the Late Pleistocene and the Holocene: Regional aspects. Quaternary International. 2017 Jul 25; 445:71-88.
14. Rivals F, Mol D, Lacombat F, Lister AM, Semprebon GM. Resource partitioning and niche separation between mammoths (Mammuthus rumanus and Mammuthus meridionalis) and gomphotheres (Anancus arvernensis) in the Early Pleistocene of Europe. Quaternary International. 2015 Aug 27; 379:164-70.
15. Roca AL, Georgiadis N, Pecon-Slattery J, O'brien SJ. Genetic evidence for two species of elephant in Africa. Science. 2001 Aug 24; 293(5534):1473-7.
16. Rohland N, Reich D, Mallick S, Meyer M, Green RE, Georgiadis NJ, Roca AL, Hofreiter M. Genomic DNA sequences from mastodon and woolly mammoth reveal deep speciation of forest and savanna elephants. PLoS biology. 2010 Dec 21; 8(12):e1000564.
17. Shoshani J, Tassy P, editors. The Proboscidea: evolution and palaeoecology of elephants and their relatives. Oxford: Oxford University Press; 1996.
18. Sukumar R. A brief review of the status, distribution and biology of wild Asian elephants Elephas maximus. International Zoo Yearbook. 2006 Jul 1; 40(1):1-8.
19. Sukumar R, Santiapillai C. Elephas maximus: status and distribution. The Proboscidea: Evolution and Palaeoecology of Elephants and their Relatives. Oxford University Press: New York. 1996:327-31.
20. van der Made J. The evolution of the elephants and their relatives in the context of a changing climate and geography. book:" Elefantentreich—Eine Fossilwelt in Europa", Chapter: The evolution of the elephants and their relatives in the context of changing climate and geography, Publisher: Landesamt für Denkmalpflege und Archälogie Sachsen-Anhalt & Landesmuseum für Vorgeschichte, Halle, Editors: D. Höhne & W. Schwarz. 2010.
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21. Zhang H, Wang Y, Janis CM, Goodall RH, Purnell MA. An examination of feeding ecology in Pleistocene proboscideans from southern China (Sinomastodon, Stegodon, Elephas), by means of dental microwear texture analysis. Quaternary International. 2017 Jul 25; 445:60-70.
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Supplemental File 3
The additional
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APPENDIX D
IRB EXEMPTIONS FOR CHAPTERS III AND IIII
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