Participatory Simulations:
Vanessa Colella
MIT Media Laboratory
20 Ames Street, E15-120H
Cambridge, MA 02139 USA
vanessa@media.mit.edu
Please reference
as:
Colella, V. (2000). Participatory Simulations: Building collaborative understanding through immersive dynamic modeling. Journal of the Learning Sciences, 9(4), 471-500.
Abstract
This article explores a new way to help people understand complex, dynamic systems. Participatory Simulations plunge learners into “life-sized,” computer-supported simulations, creating new paths to scientific understanding. By wearing small, communicating computers, called Thinking Tags, students are transformed into “players” in a large-scale microworld. Like classic microworlds, Participatory Simulations create a scenario, mediated by a set of underlying rules, that enables inquiry and experimentation. In addition, these new activities allow students to “dive-into” a learning environment and directly engage with the complex system at hand. This article describes and analyzes a set of Participatory Simulations that were conducted with a group of high school biology students. The students’ experiences are tracked from their initial encounter with an immersive simulation through their exploration of the system and final description of its underlying rules. The article explores the educational potential of Participatory Simulations. The results of this pilot study suggest an opportunity to further investigate the role that personal experience can play in developing inquiry skills and scientific understanding.
The students in a science classroom are chattering away as they play with the latest computer simulation. A virus is about to wipe out a small community. Will the inhabitants discover a way to survive? A small group of students in one corner stare intently at a computer, waiting for the results. As they wait, the virus mysteriously infects a few players on the other side of the classroom. Shrieks echo through the room as each new set of red lights indicates that another player has succumbed to the disease. Each player struggles to evade the spreading disease. Without warning, red lights emblazon the whole population. The disease has run its course.
Think for a moment about the image that story conjures up for you. If you pictured this game unfolding, you might have pictured groups of students huddled around a desktop computer playing the latest simulation game—a sort of ‘SimVirus’ or new virtual reality ‘Outbreak.’ Perhaps a few students sat close to the monitor while others jumped around behind them as their “players” fell ill. Perhaps a few fought for control of the mouse as they tried in vain to save their “player.” Children playing such a game would observe the results on screen and then decide how to use that information to better understand the simulation model.
Much of our imagination about how computers can be used to enable new kinds of learning in the sciences is constrained by the box and monitor motif of the computer. However, the game described above is not played on a computer, at least not a traditional computer. This article explores Participatory Simulations, in which students become players in unique, “life-sized” games that are supported by small, wearable computers.
Participatory Simulations take the simulation off of the computer screen and bring it into the experiential world of the learner. The students above are not just watching the simulation; in a very real sense they are the simulation. By wearing small computers called Thinking Tags, the students each become agents in the simulation. The students do not need to struggle to keep track of which player is sick, for the flashing red lights belong to their classmates. The questions that follow—Who got them sick? When? How? Why?—are not merely part of examining a computer model, they are part of discovering the underlying mysteries of their very own viral epidemic.
Participatory Simulations build on the characteristics of microworlds, in which models can be executed, and augment them with the affordances of real world experience. These new environments are a kind of role-playing game that combines the immediacy of real-life adventure with the consistent rules and structure of microworlds. Participants experience a computer-supported simulation of a system and then collaboratively explore its dynamics. In keeping with the calls for inquiry-based science, developing skills for systems thinking, and fostering collaborative learning in science classes (National Committee on Science Education Standards and Assessment, 1996; Project 2061, 1993), this project explores how learning takes place in the environment created by a Participatory Simulation.
Designing Experiences
There is a long history of theoretical claims that children construct their own knowledge through experience (Dewey, 1916; Dewey, 1988; Montessori, 1912; Papert, 1980; Tanner, 1997). Many educators have taken up the task of designing educative experiences, often selecting or creating particular materials to enable an experience. When developing his concept of kindergarten, Friedrich Froebel pioneered the idea that particular objects, which he called “gifts,” could be given to children in order to stimulate certain kinds of exploration. He argued that these gifts would provide experiences for children that would likely lead to certain kinds of cognitive development (Brosterman, 1997).[1] Much of his notion of kindergarten focused on how the orderly delivery of the gifts would enable children to build knowledge in a coherent fashion. Years later, Vygotsky wrote extensively on the notion that tools (like Froebel’s gifts) could enrich and broaden both the scope of activity and the scope of thinking of the child (Vygotsky, 1978). Other researchers have even speculated about the ways in which the objects present in the environment could actually induce development (Fischer, 1980).[2]
Computers fit right into this lineage. Even before the prevalence of personal computers, Seymour Papert envisioned a future in which computer-based tools would provide children with a whole range of transformative developmental experiences (Papert, 1980). He imagined that constructions within these powerful computing engines would become fodder for children’s imaginative and intellectual ruminations, much like gears (his own childhood tool) had become for him. The fact that computers could take on so many different roles, potentially a role per child, was especially exciting.
Much effort has been expended to build computational tools that provide opportunities for children to engage in experiences, which would not be accessible to children without those tools (Resnick et al., 1998). Virtual communities offer places for children to construct alternate realities (Bruckman, 1998); computer-based modeling environments enable the design and construction of complex paper sculptures (Eisenberg & Eisenberg, 1998); microcomputer-based labs facilitate children’s collection of scientific data (Tinker, 1996); and Newtonian-based environments allow exploration of the laws of physics (White, 1993). Each of these computerized tools supports exploration, investigation, or creation—activities central to an educative experience. The next section describes microworlds, the computer-based tools that provided the conceptual and computational frameworks for the development of a new class of educational experiences called Participatory Simulations.
Microworlds were originally conceived to give children a sort of computational sandbox—a world in which they could manipulate “objects” on the computer screen. In a real sandbox, children use buckets, shovels, and sand to create miniature castles. While creating these sandcastles, children often grapple with concepts like shape and scale. What base supports the tallest sandcastle? How big should two pebbles be if they are meant to represent a prince and a princess? A computerized sandbox offers more than just a sandbox on a screen. In a microworld—as in the real world—a child can take actions that have discernible effects on the world. But in a microworld, the child also has some access to the formal rules that govern his actions. Microworlds offer a non-formal entry into a world based on formal, logical constructs.
Picture a girl playing with a toy horse in her room. She can move the horse around and even have it “talk” to other animals in the barnyard. The horse might “gallop” and “trot” as she alters the speed with which she flies the horse around her play space. In a microworld, her horse could still move around in space, talking to other animals, but she might begin to investigate the mathematical relationship between the horse’s two speeds. Depending on the microworld, the computer might even show her an equation that relates those speeds. Or she could make the galloping speed dependent on the trotting speed. Certainly, she could perform similar mental operations in the real world, but the microworld can provide a seamless transition from the non-formal, naïve operations in the real world to the formal descriptions and investigations of those operations in the microworld. In fact, research has suggested that microworlds whose formal descriptions closely mirror children’s experience with patterns and activities can be better learning environments (diSessa, 1988).
Most often, a microworld focuses on a specific set of formal rules, constraining the types of actions a child can take but providing an opportunity to learn more about the rules governing those actions. Roschelle (1996) describes one such learning activity, during which two girls build up an understanding of the Envisioning Machine, a microworld that facilitates exploration of velocity and acceleration. Like many microworlds, the Envisioning Machine provides “an intermediate level of abstraction from the literal features of the physical world” (p. 241). The computer becomes a bridge linking the patterns and activities in the microworld (in this case, motion of a ball or particle) with the formal expression of those patterns and activities (arrows representing velocity and acceleration), by connecting pattern and activity to representations of the underlying processes. This bridge enables children to interact with both the processes and patterns they observe and the formal systems that govern those patterns and processes. Much as Froebel’s gifts facilitated specific activities and, in so doing, helped children develop new understandings, microworlds can broaden the range of activities and thoughts in which children can engage.
Benefits of microworlds.
Teaching often involves creating and organizing special experiences to help children learn certain ideas. The flexibility of microworld environments opens up the range of possible experiences that can be created. Some researchers have claimed that “the computer is… more flexible and precise in crafting experiences that can lead to essential insights” (diSessa, 1986, p. 224). Teachers and researchers have constructed microworlds that make possible countless experiences, from exploring geometric relationships to building interactive river ecosystems. For example, different microworlds enable children to focus an exploration on particular aspects of physics (The Envisioning Machine), mathematics (Logo), or politics (SimCity). One class of microworlds, which enable focused exploration of complex, dynamic systems, has gained mainstream popularity in the past few years. Game software like SimCity (1993) and SimLife (1992) helped generate popular interest in complex systems. Programs like Model-It (Jackson, Stratford, Krajcik, & Soloway, 1994), Stella (Roberts, Anderson, Deal, Garet, & Shaffer, 1983), StarLogo (Resnick, 1994), and Sugarscape (Epstein & Axtell, 1996) enable users to experiment with complex systems and develop better intuitions about the mechanisms that govern dynamic interactions.
Microworlds let children experiment with real concepts in play space, or as Pufall (1988) said, they create “a context within which children can think about discrete space as real and not about discrete space as an abstraction from the analogue worlds of sensory-motor experience” (p. 29). With microworlds, learning experiences are no longer constrained by what the real world has to offer. We can both limit and augment the real world, sometimes creating simplified spaces for exploring complex topics, other times creating wholly new experiences on-screen. Pufall (1988) further speculated that the new interactions microworlds enable might “alter children’s patterns of development, by allowing [them] to interact in ways [they] cannot interact with the ‘real’ world.”
Building
on microworlds.
Microworlds introduced many benefits for learning and presented some new challenges as well. Without trying to exhaustively cover the benefits of learning in the physical world, it is worth mentioning that there are human ties to interactions in real space that are lost in cyber-learning. Though some users become enamored of the machine (Turkle, 1984), others feel distanced from the patterns and processes they observe on a computer screen. For some people, this distance leads to a general distaste for the ‘cold,’ unemotional world of computing (Turkle & Papert, 1992). Others are inclined to believe everything they see on a computer, not questioning the validity or appropriateness of simulation results. Sociologist Paul Starr (1994) witnessed one user’s lack of intellectual curiosity about the underpinnings of SimCity and another group’s disinterest in rigorously questioning the assumptions underlying a computer model designed to forecast future health care costs. In SimCity, the underpinnings of the model are hidden from the user, perhaps stifling curiosity. But the assumptions in the health care model were readily accessible, suggesting that developing a full understanding of a computer model is a formidable task.
As much research on microworlds has shown, these challenges are not insurmountable. Many microworld environments engage students in deep reasoning and sophisticated analysis (e.g., Eylon, Ronen, & Ganiel, 1996; Goldman, 1996; Papert, 1980; Roschelle & Teasley, 1995; Rothberg, S., & Awerbuch, 1994; Schoenfeld, 1990; Tabak & Reiser, 1997; White, 1993). Microworlds enable a diverse set of experiences, encouraging children to broaden the scope of their intellectual investigations. Effective microworlds don’t turn learners’ “experience[s] into abstractions. [Instead, they turn] abstractions, like the laws of physics, into experience” (diSessa, 1986, p. 212). By actualizing these experiences, microworlds enable learners to directly experience simulations. Or, more precisely, they enable users to enjoy experiences with those simulations that are as direct as we can make them (diSessa, 1986).
In the past, direct interaction with a simulated environment meant manipulating agents or parameters in a microworld or controlling an avatar in a virtual world. New technology allows us recast the notion of “directly” interacting with a computationally simulated experience. We can now deploy simulations in the real world, facilitating a more personal experience for learners. Our aim is that, just as microworlds have greatly enhanced the learning experiences available to students, Participatory Simulations will provide another range of learning experiences, upon which students and teachers can draw.
Another
Way to Learn from Experience.
Participatory Simulations facilitate another way for learners to collaboratively investigate the relationship between patterns and processes in the world and the rules that give rise to those patterns and processes. Participatory Simulations build on the characteristics of microworlds, in which models can be executed, and augment them with the affordances of real world experience, enabling learners to become the participants in computer-supported simulations of dynamic systems in real space. Small, distributed computers create a life-sized microworld by deploying consistent, computational rules in real space. Learners can experience and influence this simulation directly. This interaction, though still mediated by technology, is qualitatively different from other technology controlled role-playing games that facilitate interaction through avatars or with the components of a microworld. Participants’ personal connections to the educational situation enable them to bring their previous experiences to bear during the activity, establish strong connections to the activity and the other participants, and, we hope, draw upon their experience in the future.
Participatory Activities
The Participatory Simulations Project investigates how direct, personal participation in a simulation leads to a rich learning experience that enables students to explore the underlying structure of the simulation. The idea to use direct, personal participation to help children (or learners) gain a new perspective or build a better understanding is not a new one. Dewey emphasized the value of personal participation in educative experiences throughout the curriculum. In the social sciences, perspective-taking activities are quite common (Seidner, 1975). Students might be asked to take on the role of community activists or politicians and simulate a debate on the future of the logging industry. This debate gives the participants a way to represent the characters and think about how the various characters might feel about an issue.
Activities like these are less common in the sciences, where the mechanisms to be studied are not human feelings and behavior but concepts like planetary motion or molecular interactions. Nonetheless, students sometimes take on those kinds of roles as well, perhaps pretending to be planets in orbit, in an effort to illustrate those phenomena. However, these activities are very different from their social science counterparts. While the social science activities might help the students to think about how a politician, for instance, would feel and behave under certain circumstances, the science activities don’t necessarily help students to think about the underlying mechanisms of processes like planetary motion. Role-playing activities attempt to create links between personal experience and a deeper understanding of why that experience happened, yet the science-based activities often end up being little more than large-scale illustrations.
Researchers have attempted to connect personal and physical interactions to underlying (non-human) mechanisms in a variety of ways. Papert (1980) tried to forge links between human action and the rules of Turtle Geometry by asking children to pretend they were the turtle and then translate that understanding into a symbolic representation of the instructions for the turtle’s movement. Resnick and Wilensky (1998) expanded upon this idea, involving large groups of people in activities to help them gain a richer understanding of the rules governing emergent systems. Recently, Wilensky and Stroup (1999) developed a network architecture that gives students control over individual agents in a simulation environment. Researchers in systems dynamics also use group activities to help learners develop systems thinking capabilities (Booth Sweeney & Meadows, 1995, 1996; Meadows, 1986; Senge, Roberts, Ross, Smith, & Kleiner, 1994). Participatory Simulations build on microworlds and these group activities, using wearable computers to create an explicit link between personal experience in real space and the underlying rules that mediate those experiences (Colella, 1998; Colella, Borovoy, & Resnick, 1998).
The Participatory Simulations Project
The Participatory Simulations Project looks specifically at how a new kind of learning environment can motivate learners, facilitate data analysis, collaborative theory-building and experimental design, and lead to a richer understanding of scientific phenomena and the processes of scientific investigation. By involving a large number of students in a physical experience of a simulation, the project brings a microworld off of the computer screen and into the participants’ space. Our aim is to establish if, and if so how, participation in these activities promotes the development of both the motivation and ability to engage in scientific thinking.
The Participatory Simulations Project is an extended research endeavor, studying how personal exploration of life-sized, computer-supported simulations can help participants develop inquiry skills and scientific understanding. Thousands of people have participated in various activities at schools, in workshops, and at conferences. In each activity, people use small, wearable computers to become agents in the simulation. For instance, during the pond ecology simulation some participants become schools of “big fish” and others become schools of “little fish.” As they interact with one another, the big fish “eat” little fish and the little fish “eat” fish food. The Tags keep track of the number of fish in each school. Participants collaboratively investigate the ways in which cooperative and competitive behaviors alter the dynamics of the ecological system and change the carrying capacity of the pond. Similarly, the tit-for-tat game allows participants to experience game theory from a first-person perspective. Together, they can explore how cooperative behaviors evolve over time. In the virus activity described in this article, participants interact as a disease moves through their community. The group works to analyze the disease dynamics and establish how the behavior of individuals influences the outcome of the simulation. In all of these Participatory Simulations, people collaboratively explore the system by changing their own behavior, collecting data, running experiments, and observing the effect that their behavior has on the dynamics of the system.
We use small, wearable computers called Thinking Tags to enable direct participation in the simulation. The Tags collect information for the participants (like how many other players they have met) and help them to interpret the state of other players (for example, whether someone is “sick” or “healthy”). Unlike the traditional notion of wearable computing, which focuses on connecting users to an external network like the web, the Tags connect all of the participants in their own discrete network, which facilitates inter-user connectivity and provides the computational support for the simulation. Rather than just transforming the experience of an individual, Participatory Simulations transform the interactions among people by linking them through a personalized network of communicating computers. Participants become players in a computationally-mediated system comprised of people and their small, personal computers.
Participatory Simulations are supported by a variation of the Thinking Tag technology developed at the Media Lab (Borovoy, McDonald, Martin, & Resnick, 1996). The Tags are used to transform each participant into an “agent” in a simulation of a dynamic system. In these decentralized simulations, no one Tag acts as a server and no large (traditional) computer is necessary to run, experiment with, or analyze the system. We developed a new version of the Thinking Tags[3] to facilitate collaborative analysis of many iterations of the simulation. As in the original Thinking Tag design, we took care to ensure that the enhanced information display would not interfere with participants’ social interactions (Borovoy, Martin, Resnick, & Silverman, 1998; Borovoy et al., 1996; Ishii, Kobayashi, & Arita, 1994; Ishii & Ullmer, 1997).
Like the original Thinking Tags, the Tags
built for Participatory Simulations are complete, albeit miniature, computers
with input and output devices and displays for the user. Each Tag possesses an infrared transmitter
and receiver, allowing it to dynamically exchange information with all other
Tags in the simulation. As the
simulation is running, the Tags are constantly exchanging information via
infrared, though this exchange is invisible to the participants. The Tags have two display devices, a
double-digit number pad and five bicolor LEDs (See Figure 1). During the simulation the information
displayed on the Tags changes, and participants watch the Tags to discover
information about themselves and about other players. A resistive sensor port acts as an input device, allowing users
to attach small tools to their Tags and enabling them to “dial-in” information
or change the program their Tag is running.
This carefully chosen set of inputs and outputs provides a rich set of
user interactions, both during the simulation and during the subsequent
analysis.
The three-week long pilot Participatory Simulations Study described in this article took place in an urban public high school classroom. All of the students volunteered for the project and were told that they would be participating in a project to learn about dynamic systems in science. Class time for five days over a three-week period was devoted to activities associated with the Participatory Simulations Study.
The chosen Biology class consisted mainly of tenth grade students, who were described by their teacher as traditionally poor performers in science class. Sixteen students, seven girls and nine boys, participated in the study. The teacher also participated in the activities, and on day four a student teacher observed the class and participated in the activities. The researcher (author) was the facilitator of the classes. In addition, two students videotaped the activities.[4]
Activities
Aside from a very brief introduction to the researcher and the basic operations of the Tags, the students’ first experience in the Participatory Simulations Study was playing a disease simulation game. The context was set for the first simulation by giving the students a challenge: meet as many people as they could without getting sick. They were told that one of the Tags contained a virus and that they could elect to stop meeting people anytime they wanted simply by turning their Tag around to face their stomachs (or turning it off) and sitting down. The students were told nothing about how the virus moved from one Tag to another, nor were they told anything about the degree of contagiousness, the possibility for latency, or any other underlying rule that could affect the spread of the disease, leaving them in an ambiguous situation. None of the students’ questions about the behavior of the virus was answered. Instead, they were given the opportunity to experience and explore the disease simulation for themselves.
The students participated for 45 to 55 minutes on each of four days and 90 minutes on the last day. The project had three phases. On the first day (phase 1) students were introduced to the researcher and a few other examples of technology that operate on the same general principles as the Tags (Resnick et al., 1998). On days two, three, and four (phase 2) students participated in disease simulations, or “games,” and analyses of those simulations. This phase had three distinct components: the initial disease simulation, the discussion of that simulation, and the development and execution of experiments to test hypotheses about that simulation. The students completed six disease games over the course of the three days, with the discoveries from one simulation leading to the design of the next. Finally, on day five (phase 3) students reflected on their experiences in the Participatory Simulations Study and asked to participate in one final simulation game.
The Disease Simulation
Throughout this pilot study, the underlying rules of the simulation[5] were kept constant. The rules of the disease simulation were:
· The virus was latent (invisible) for approximately three minutes,
· Any person whose Tag had the virus, even if it was not visible, could infect another person’s Tag,
· The probability for infection when meeting an infected Tag was 100%,
· People with Tags numbered 1 or 2 in the ones position (1, 2, 11, 12, 21, etc.) were immune to the virus, and
· Immune Tags were not carriers of the disease.
During the
simulation, the number pad displayed the number of different people with whom
each participant had interacted, and the five LEDs flashed red when the Tag was
sick. The Tags also tracked information
that the participants could access after the simulation, including the ID
numbers of all of the individuals with whom a person interacted, the time of
all interactions, and the ID number of the individual responsible for
infection. During the study reported in
this article, students accessed the stored data only to confirm their final
hypotheses. In some of our other
Participatory Simulations projects, this data has been aggregated, displayed
through StarLogo, and used for more in-depth analyses of disease transmission.
Bringing new computational tools into a classroom can fundamentally alter the structure of the class’s interactions. The unit of analysis in the Participatory Simulations Study was not the individual child nor the individual child plus the tool, but the whole cognitive system in the classroom (Newman, 1990; Salomon, 1993). Newman defines the cognitive system:
The teacher creates a social system in the classroom that supports certain kinds of discourse and activities; students collaborate within the system, contributing observations, answers, and concrete products such as texts, projects, and data. The cognitive system includes the externalized tools, texts, data, and discourse, all of which is produced by and for the activities (p. 187).
During the Participatory Simulations Study, attention was paid to how all aspects of the learning environment (the group of students, their conversations, and the tools they employed) contribute to building scientific understanding.
This study analyzed conversations and explicit collaborative discussions during the activities. The main source of data for the Participatory Simulations Study was a complete videotape log of the sessions that, in particular, aimed to capture all of the whole-group conversations. In addition, audiotape backups were made of every session and facilitator logs were kept throughout the project. Students were occasionally asked to write down their ideas about the simulation dynamics, and all of those student responses were kept. We examined the data to find evidence of our four main aims: During the activities, were students engaged in the simulation? Could students identify and analyze evidence from the simulation? Were they able to design experiments, predict outcomes, and run experiments to confirm or deny their ideas? Did students carry out their investigations in a scientific manner?
Analysis of Classroom Activities
In the Participatory Simulations Project, we aimed to motivate students by giving them a real experience that was mediated by a set of underlying formal rules. One measure of success of a Participatory Simulation, then, is the extent to which students felt as though they actually experienced the simulation. In this case, we judged the experiential quality of the simulation by observing the extent to which students suspended their disbelief and acted as though they were in the midst of an epidemic striking the members of their small community.
The following episode depicts some of the excitement and tension that permeated the learning environment:
Episode 1
Doug: I got it from her.
Student: You all got the virus!
Stacy: I’m dead.
Doug: (to Tony) Oh, you got the virus now.
Tony: (looking at Tag) You got it started.
Rick: (singing) I ain’t got the virus.
Student: I’m healthy.
Meredith: (holding Tag up) I don’t have the virus.
Researcher: Who in this room met the most people?
Chorus
of students: I have 14, I got 16, I got 13 with no virus, me too, I got 14 with no virus.
Student: I need some medicine.
Students displayed a robust and persistent willingness to suspend their disbelief and behave as though the simulation activity was real. The learning environment promoted a strong connection between the students and the simulation. When Stacy exclaimed that she was “dead,” she was not talking about an external agent or avatar—she was talking about herself in the simulation. Similar references occurred throughout the study, as when a student declared that he needed medicine.
This level of engagement permeated the next four days of the project. As each game unfolded, the students once again had a “real-life” experience of an epidemic invading their small community. Their task was not to mentally construct the dynamics of an epidemic from a written description or a set of equations. Instead, they needed to figure out what was happening in their community. The activity “aroused curiosity, strengthened initiative, and set up desires and purposes” in the students, propelling them to develop an understanding of the simulation environment (Dewey, 1988, p. 20). This compelling, interpersonal experience is one of the key components of the Participatory Simulation and set the stage for the learning activities that followed.
Though engagement in the immersive experience is an integral and important component of Participatory Simulations, the immersive component per se does not determine the activity’s educational value. The experience’s potential for leading to growth rests on its ability to allow the students to problematize their indeterminate situation (and later to inquire into its underlying structure). In this case considerable learning occurred as students were able to step back from their immediate experience and analyze the situation. Ackermann (1996) described this process as “diving-in” and “stepping-out,” as students move back and forth between full immersion in a problem and thinking about a problem. Similarly, Sterman (1994) distinguished between the features of learning in and about dynamic systems.[6] Many scientific problems offer the chance to step outside of the problem and think clearly about it. Few problems that are appropriate for study at a high school level offer the chance to dive so convincingly into a problem. Participatory Simulations create a unique opportunity for students to enjoy both of these important perspectives during the processes of defining and solving problems.
The notion of diving into a scientific problem in order to better understand it has not always been highly valued by researchers. The scientific community has traditionally valued detached, objective modes of experimentation, at the expense of more “connected” methods; however, some examples from scientific practice indicate that a revaluation of connected science may be in order.[7] Participatory Simulations can bring connected science to the classroom without forcing students to abandon the exploration of scientifically important problems. As students collected data and designed experiments, they remained in touch with the problem at hand. A non-trivial characteristic of the Participatory Simulations environment made this connection possible—the students were collecting data about and experimenting on their own behavior.