Documents
Resources
Learning Center
Upload
Plans & pricing Sign in
Sign Out

aera

VIEWS: 20 PAGES: 28

									AUGMENTED REALITY SIMULATIONS ON HANDHELD COMPUTERS

Eric Klopfer Teacher Education, Massachusetts Institute of Technology

Kurt Squire MIT Comparative Media Studies

Henry Jenkins MIT Comparative Media Studies

For correspondence, please contact Kurt Squire (ksquire@mit.edu), Building 14N-213, 77 Massachusetts Avenue, Cambridge MA.

This research was supported with a grant from Microsoft - MIT iCampus as a part of the Gamesto-Teach Project. The authors would like to thank Randy Hinrichs at Microsoft Research for his support of this project, as well as Kodjo Hesse, Gunnar Harboe, and Walter Holland for their hard work in the development of Environmental Detectives.

RUNNING HEAD: AUGMENTED REALITY

AUGMENTED REALITY SIMULATIONS ON HANDHELD COMPUTERS

Introduction The use of computer simulations is changing the very nature of scientific investigation (Casti, 1998), and providing us unique insights into the way the world works (Wolfram, 2002). Scientists can now experiment in a virtual world of complex, dynamic systems in a way that was impossible just years ago. These tools have led to discoveries on topics ranging from the origins of planets to the spread of diseases through human populations. Simulations have also changed the way that science is taught. Many teachers use simulated systems on desktop computers to allow their students to conduct explorations that otherwise would be too time-intensive and costly (Feurzeig & Roberts, 1999). To date most computer simulations have been tethered to the desktop, as they have relied on the processing power of that form factor. As we move simulations from the desktop to more ubiquitous and increasingly powerful portable devices, we could simply port existing tools to this new platform. This change in form factor alone would provide advantages in price and

accessibility to students. But, the move from the desktop to the handheld computer provides other potential advantages, which make this an especially attractive platform for studying simulations. In order to fully capitalize on the handheld form factor, we should harness other features of handhelds including:      portability –can take the computer to different sites and move around within a site social interactivity – can exchange data and collaborate with other people face to face context sensitivity– can gather data unique to the current location, environment, and time, including both real and simulated data connectivity – can connect handhelds to data collection devices, other handhelds, and to a common network that creates a true shared environment individuality – can provide unique scaffolding that is customized to the individual’s path of investigation. Implicit to our research is the belief that a powerful handheld learning environment might capitalize on the portability, social interactivity, context sensitivity, connectivity, and individuality of ubiquitous devices to bridge real and virtual worlds. This platform will enable the development of ―augmented reality‖ simulations, that is simulations that provide a virtual context layered on top of a real-world context. The handheld computer can be viewed as a

window into the virtual context that is sensitive to information being supplied to it by the real world. Past research in simulations and games in science education have highlighted the importance of the broader pedagogical context in shaping activity and learning. Smith, diSessa and Roschelle (1993) show how using computational tools for visualizing and simulating phenomena can provide an experiential framework for investigating and testing their existing scientific understandings (see also Colella, Klopfer, & Resnick, 2001; Friedman, & diSessa, 1999; Stratford, Krajcik, & Soloway, 1998). White and Frederickson (1999) describe the importance of developing and fostering metacognition (e.g. Brown, 1991) in the use of the Thinker Tools curriculum, illustrating how tools designed to support metacognition can scaffold inquiry and have a dramatic impact on the learning process. In their investigation of students’ use of an ozone simulation tool within a problem-based learning context, Squire, MaKinster, Barnett and colleagues (2002) extended this argument further, arguing for the importance in understanding and accounting for classroom cultures in affecting how modeling and simulation activities are situated, and ultimately, the kinds of learning outcomes that are produced through simulation practices.

Theoretical Approach Over the past decades, a growing number of educational theorists and researchers in the learning sciences have argued for the importance of understanding cognition in context (e.g. Brown, Collins, & Diguid, 1989; Barab & Kirschner, 2001; Cognition and Technology Group at Vanderbilt, 1990; Greeno, 1998; Kirshner & Whitson, 1996). Whereas traditional cognitive models treat the workings of the mind as somewhat independent, a host of emerging, complementary approaches to cognition treat cognition and context as inextricably linked. How these different approaches construct the notion of context depends on their underlying theoretical framework. Conceptualizing Situated Cognition Barab and Duffy (1999) note two particular families of approaches to understanding situated cognition: 1) Educators such as Resnick (1987) and the CTGV (1990) who, troubled by students’ inability to use school knowledge to inform their thinking outside of school contexts, have developed instructional approaches which broaden the learning environment to include complex problem-solving tasks, access to tools and resources, and substantial opportunities for collaboration. Theorists from these perspectives have adopted what might be considered cognitive constructivist approaches, developing pedagogies such as anchored instruction, problem-based

learning, or goal-based scenarios as models for thinking how we might expand cognition. Barab and Duffy label these approaches ―practice field‖ metaphors for instruction, arguing persuasively that these approaches are linked primarily by pragmatic concerns about the quality of schooling, and emerge from a common psychological approach which rooted in acquisition models of learning, has been concerned with the inert knowledge problem and the ―transferability‖ of knowledge across contexts (Bransford, et al., 1999; Whitehead, 1929). Practice Field Metaphors for Learning. Barab and Duffy (1999) describe eight features of effective practice field learning environments, culled through a review of approaches to problembased learning, anchored instruction, and goal based scenarios: Doing domain-related practices, ownership of the inquiry, coaching and modeling of thinking skills, opportunity for reflection, illstructured dilemmas, support the learner rather than simplify the dilemma, collaborative and social work, motivating learning context. In a similar review, Jonassen (1999) uses a spatial metaphor for talking about constructivist learning environments, describing the environment in terms of a problem space, which is situated within and mediated by supporting tools and resources and social / cultural contexts. It is beyond the scope of this paper to compare these approaches to understanding the design of practice field, or problem-based learning environments. However, we do want to highlight the agreement across these approaches in the importance of facilitating student ownership over the problem space (helping students perceive problem authenticity) and the role that tools and resources play in mediating students’ relationship with the problem. Jonassen desecribes how tools such as Model-It (Spitulnik, Studer, Funkel, Gustafson, & Soloway, 1995), Climate Watcher (Eleson, Pea, & Gomez, 1996), or Computer-Supported Intentional Learning Environments (CSILE; Scardamalia, Bereiter, & Lamon, 1994) mediate how students’ encounter dilemmas, collaborate in solving problems, and represent problem solutions. Increasingly, educators and cognitive scientists are recognizing the role of computer tools in mediating activity both inside and outside of the classroom (Solomon, 1993). Learning as Participation. Barab and Duffy (1999) delineate a second, anthropologicallyoriented strand of research attacking this issue of contextuality, exemplified through the work of Lave and Wenger (1991). Barab and Duffy write, From this (anthropologically oriented) perspective, the main problem of practice fields is that they occur in schools rather than in the community through schools. This creates a bracketing off of the learning context from the social word through which the practices being learned are of value and of use. If interactions with the world produce meaning and identity, then educators need to place more emphasis

on the types of interactions and, hence, the identities are being created within the context of schools (p. 34). For anthropologists, notions of identity, participation, learning, and communities of inextricably linked (Wenger, 1998); learning occurs and is demonstrated through participation in communities of practice, participation in social practices and through which identities evolve. One important consequence of this conceptual shift is to broaden the ―transfer‖ problem to include issues around identity and power, to examine the transformative nature of schooling, and how individuals appropriate and resist schooling experiences within the broader trajectory of their identity development (e.g. Walkerdine, 1996). Core to this anthropologically-grounded notion of learning through participation in communities of practice is that learners are participating in communities of practice whose social functions transcend the boundaries of the school. That is, learners are engaged in social practices whose purposes are socially legitimate, organized around practices to meet individuals and communities needs to get ―real work done,‖ whether that be conducting scientific inquiry, researching social issues, or engaging in philosophical debate. Barab and Duffy (1999) synthesize anthropological approaches to understanding community, identifying three primary characteristics of community oriented approaches to learning: Communities have a common cultural and historical heritage, they situated learners as parts of interdependent social systems, and contain a reproduction cycle, the processes whereby newcomers are initiated into the communal system and mature into old-timers. Lave and Wenger (1991) use the term legitimate peripheral participation to describe how learning occurs within communal contexts; elsewhere, we have investigated how technologies can be used to link learners with established communities of practice, engaging learners in ―teleapprenticeships‖ or how technologies can be used to overcome the relatively confined nature of school and support the formation of communities of practice across social institutions (e.g. Squire & Johnson, 2000). Underlying this anthropological notion of learning through participation in communities of practice are differing notions of authenticity (Barab, Squire, & Dueber, 2000); practice fields may engage learners in ―authentic tasks‖, but this does not imply that learners are engaging in these tasks for authentic social purposes, or that learners necessarily perceive these tasks as authentic. Barab et al. (2000) describe this second, anthropologically grounded notion of authenticity as ―emergent‖ authenticity, treating authenticity as an emergent property constituted by interactions among individuals’ subjectivities and intentions, social practices, and broader communities of practice. Authenticity, which may seems like a relatively unified concept describing the fidelity between learning practices and social practices, becomes even more

complex when investigated within communities of practice designed for learning; As Barab, Barnett, and Squire (2002) describe in their study of a Community of Teachers, a learning community of pre-service teachers, ―authentic‖ practices, such as building a portfolio can be perceived as inauthentic if the process of portfolio-building, community’s criteria for success (i.e. what makes a good portfolio), or the entire practice of building a portfolio itself are not appropriated by members of the community. Barab and colleagues argue that the community is not characterized not by the features of the program (i.e. the seminars, classes, or portfolio), but by the unstable, dynamic tensions (e.g. Portfolio as supporting reflection vs. an accountability device) which shape activity. Using Wenger’s language of tensions (or illuminative dualities), Barab and colleagues unpack how the processes by which the community functions – how it determines its purpose, sets goals, and maintains its health – the community context, was itself the content that students were learning. In other words, it was through creating the processes by which the community operated (negotiating seminar topics, changing the constitution, evaluating portfolios) that learning occurred, and these processes themselves became a part of students’ cognition.

Situativity Theory Researchers from both ―practice field‖ and the ―participation in communities of practice‖ approaches have acknowledged the situated nature of knowing and articulated the need for documenting learning as it unfolds as a relationship among participants and their environment (Greeno 1998). Greeno introduces the notion of situativity as a way of understanding the problem space of a learning episode. Greeno describes problem spaces as, ―the understanding of a problem by a problem solver, including a representation of the situation, the main goal, and operators for changing situations, and strategies, plans, and knowledge of general properties and relations in the domain‖ (p. 7). Whereas traditional psychological models take the individual learner operating without regards to context, situativity theorists argue that there is no such thing as context-independent thought and behavior, and the central goals of educational psychology are to understand performance as it occurs in socially meaningful situations, accounting for multiperson communal structures, individuals’goals and intentions, and tools and resources which mediate action. Because learning is a process of creating meaning in situ, the environment plays an extremely important role in the processes of knowing and learning; the environment constrains activity, affords particular types of activity or performance, and supports performance (Dewey, 1938; Peirce, 1868/1992; Salomon, 1993) Knowing, learning, and activity can be conceptualized as a conversation with a situation (Schon, 1983; Winograd & Flores, 1987).

One way of describing how the environment affects our understanding is through Gibson’s notion of affordances (1979). Affordances are interactions between the attributes of and object and an organism that determine how the object can be used. So, for example, a chair affords sitting and a doorknob affords turning. While Gibson discussed direct perceptual affordances, Norman (1988) describes how learning to detect the affordances of objects is also a learned activity bound by cultural symbols. Designed objects often contain their own intelligence manifested in their design, and require knowledge of cultural symbols and conventions to be used (Lave, 1988; Pea, 1993). The process by which people learn to use these tools is referred to as appropriation (Lave, 1988). Measurement tools, such as rulers for example, include intelligence manifested themselves in cultural symbols and rules for measurement that must be appropriate in order to be used. As intelligent creatures, humans also create inscriptions to guide their thinking and off-load information into the world around them. Cognition can be termed situated, in that how people think, learn, act, and know is fundamentally rooted in the materials available in a situation. Tools and resources provide affordances in a situation that humans can detect, and as thinking agents, humans create, manipulate, and design tools and resources to solve problems and make their environment more conducive to problem solving. Restated, from a situated perspective, humans do not just use tools in their thinking, but instead, tools and resources are actually inseparable from thinking and activity (Barab et al., 1999; Dewey, 1938; Gibson, 1979; Greeno, 1998; Reed, 1991). Greeno and others (e.g. Kirshner and Barab, 2001), describe a core challenge of cognitive science as how to account for the evolving nature of problem spaces; as actors participate in problem spaces, they constantly negotiate and redefine the components of the problem space, such as redefining adapting goals, adopting new strategies, and even reconceptualizing the problem itself. Kirshner and Barab (2001) describe this constant as a part of dynamic learning environments, in that the environment itself is expected to evolve in reciprocal relations with human activity; in other words, the researcher is challenged with capturing not only the learners’ performance in situ, but how the environment co-evolves with the participants. How to capture an ecology, including the many tools, resources, and social structure that characterize any particular context of activity is challenging and still being negotiated in educational research (Engestrom & Cole, 1993; 1997). In describing a ―situation‖ as a unit of analysis, Cole (1995) concentrated on practice, activity, contexts, situations, and events. Similarly, Roth (1996) describes learning environments in terms of the practices, language, artifacts, activities, and resources in an environment. Researchers such as Cole (1996) have suggested Activity Theory (Engestrom, 1987) as

one method for understanding learning in its broader social contexts -- while also accounting for how tools and resources shape cognition and performance, remediating our fundamental understandings of objects of inquiry. Engestrom and Cole (1997) use activity theory as another theoretical lens for describing activity (Leont’ev, 1981). In activity theory, activity is defined as a subject acting upon an object, which is transformed by activity. This transformation process is mediated by artifacts (such as tools and resources), rules, community, and divisions of labor. In understanding the nature of an activity system, one must understand not only the environment, but the subject’s intentions in the environment are critical as well. Intentions constrain activity in the system establishing boundary conditions in which the remainder of the activity exists (Barab, Cherkes-Julkowski, Swenson, Garrett, Shaw, & Young, 1999). Understanding activity through a situated lens avoids environmental determinism. Rather, from the situated perspective, individuals’ intentions interact with the environment to produce activity. In figure 1, intentions might be thought of as the subject’s goals and intended transformations on the object transformations. Thus, activity is considered as a coupling between the individual and his/ her environment (Barab, et al., 1999; Dewey, 1981). In educational contexts, understanding intentions are important because this notion of intentions implies that educational environments need to recognize the goals and intentions that students bring into a learning environment, as these intentions constrain much of the activity which takes place in an environment (Barab et al, 1999; Bransford, Franks, Vye, & Sherwood, 1989; Lave, 1997; Walkerdine, 1997 ).

The Situativity of Augmented Realities We believe that the affordances of handheld computers can be used to create unique situationalities for learners, where they can collect data in doing investigations, access authentic tools and resources, and participate in collaborative learning practices while in the field. Whereas traditional desktop VR applications or 3D gaming technologies such as MUVEEs burden the computer with reproducing reality in 3D, augmented realities capitalize on the affordances of the real world, instead providing users layers of data that augment their experience of reality. As a result, simulations are untethered from the desktop and learners can participate in technologyenhanced investigations, location-based games, or participatory simulations. Because players are free to move throughout the world, novel opportunities exist for learners to interact with the physical environment, literally reading the landscape as they conduct environmental investigations or historical studies. Pocket PCs, which can display video, text, and host webs of information in intranets can create narrative contexts for users’ actions, similar to problem-based learning or anchored instruction environments, providing situated reasons for players to engage in

investigations. Our hope is that Environmental Detectives, which emphasizes the portability, social interactivity, context-sensitivity, connectivity, and individuality of handheld computers, will be the basis of a robust learning platform that evolves to support a suite of distributed educational applications where interacting with digital technologies fades into the background, and authentic scientific practices and a real world context, augmented by digital simulation technologies becomes the focal point of the activity system. New forms of collaboration can form, as students, connected via their handhelds investigate different resources, collaborate in asking questions, and negotiate the exploration of physical spaces.

Research Questions The purpose of this research project is to develop and examine an augmented-reality simulation platform that is designed from the ground up for handheld computers and draws on the unique affordances of handheld technologies. We were particularly interested in unpacking the contextuality of the augmented reality experience, studying how learning occurred through participation in a simulation distributed across both computer and non-computer-mediated spaces. We focused specifically on how the distribution of activities across computer mediated and real world spaces affected learners’ practices and understandings of scientific phenomena. How students interacted with the augmented reality simulation, and how students conceptualized the experience. How would students negotiate the simulated and the ―real-world‖ activities? Given the exploratory nature of this project, would students perceive the augmented-reality game as contrived or would students develop ownership over the game problem (e.g. Savery & Duffy, 1996)? Would students perceive the environmental engineering practices as having task authenticity (e.g Barab, Squire, & Dueber, 2000)? How would learning occur in this environment, and in particular, would students use information from surrounding environment (such as the physical terrain) in understanding the problem, and proposing solutions, as well? We were also interested in how students collaborated in this game, given the form factors of the Pocket PC and that it is distributed across both physical and virtual spaces. Would students controlling the Pocket PC show more engagement (and learn more from the experience) than those who were not interacting directly with the simulation? How would groups make decisions about data collection strategies? We were also interested in how participants reacted to the competitive and cooperative aspects of the exercise, and if any patterns of interaction arose across genders. Finally, we hoped that this research might uncover critical design factors for augmented reality simulations.

Context This study examines the implementation of a particular augmented-reality simulation, Environmental Detectives in three university classes and is a part of a larger design research agenda exploring the potential of this platform for supporting learning in environmental education (e.g. Collins, 1992), and other contexts that might benefit from the connection between real world distributed explorations and a virtual context . Environmental Detectives is an augmentedreality simulation game for the Pocket PC developed by our team with the .NET compact framework. Environmental detectives was designed in consultation with environmental engineering faculty and is matched to environmental engineering course syllabi as well as Advanced Placement environmental science standards and is designed to be used in high school and college courses.

Environmental Detectives In Environmental Detectives, participants working in teams of 3-5 students play the role of environmental engineers investigating a simulated chemical spill on a college campus. The activity space of Environmental Detectives can be profitably understood as an ill-defined problem space similar to problem-based learning (Jonassen, 1999). The precise location of the spill is unknowable to students, and there is no one perfect solution to the problem; each solution involves political, financial, and practical trade-offs that must be considered. Consistent with problem-based learning frameworks (e.g. Barron et al. 1998), students use Pocket PCs as tools for gathering data about the spill, and as a resource for accessing information about toxicology, hydrology, similar cases, and local environmental conditions.

Figure 1 – A screen shot (left) of Environmental Detectives. The red dot indicates the players current location and is guided by real world position as supplied by GPS. The pink markers represent locations of interviews, while the blue markers show where the player has already sampled the water. On the right is shown some of the textual resources that players can uncover.

The game begins with the introduction of the problem. Students watch a 60 second digital video-briefing from the University president where they are enlisted to investigate the spill of TCE, a carcinogenic degreasing agent which is commonly found in machine shops, cafeterias, and hospitals. The goal of the game is to locate the source of the spill, identify the responsible party, design a remediation plan, and brief the president of the University on any health and legal risks so that he will be prepared for a meeting with the EPA – all within two hours. At the end of the game, students make a five minute presentation to their peers outlining their theory behind the spill. The game is designed to be flexible so that teachers may have students report to a third party who judges a winner, have their peers vote on the best solution, choose winners themselves, or choose no winner at all, if they prefer a less competitive experience. The spread of TCE is simulated on a location-aware Pocket PC, which functions as a tool which students can use to investigate the TCE spill. Each Pocket PC is equipped with a GPS device, which allows players to sample chemical concentrations in the groundwater depending on their location. So, for example, if the player is standing at point a, which is near the source of the spill (See Figure 1), she might take a reading of 85 parts per billion, where as a student standing on the opposite end of campus (point b) might take a reading of 10 points per billion. Players are

given three reusable drilling apparatuses which they can use to drill for water samples. After drilling for a sample, players must wait three minutes for the sample to return, meaning that students can only take three samples at a time, and are forced to develop sampling strategies in order to optimize the amount of ground that they can cover in limited time. Because the GPS data is only accurate within 10 meters, there is some built-in error to the collected readings as well. Environmental Detectives contains a multimedia database of resources which students can use to learn more about the chemical make-up of TCE, where TCE is found on campus, the health risks associated with exposure to TCE, how TCE flows through ground water, relevant EPA regulations TCE, remediation strategies for cleaning up TCE, and the political and economic consequences of EPA violations on campus. Students access these resources by obtaining interviews from virtual university faculty and staff who we have spread across campus at locations roughly corresponding with actual operations. Because time is limited and there is not enough time to interview everyone or to drill more than a handful of wells, students must make choices between collecting interviews, gathering background information, and drilling wells, adjusting and reprioritizing goals as new information becomes available. The current version of Environmental Detectives takes about 2 hours for college students to complete (though we have a similar approach in the works for high school students), although a teacher might extend or shorten the game in order to meet her classroom needs. In its current form, Environmental Detectives includes a 20 minute game setup and discussion, approximately 90 minutes of game play, and an anticipated 30 minutes of discussion. The game is designed to be flexibly adaptive, so that teachers might easily add extension activities (such as exploring the properties of TCE, the health effects of TCE, hydrology, water treatment plans, or similar cases) or remove activities as local conditions suggest (See Squire, MaKinster, Barnett, et al., 2003). As a part of our design research agenda, we hope to use this study to identify what aspects of the experience teachers and students find valuable as we flesh out the design of Environmental Detectives to include increased functionality, such as a more dynamic hydrology simulation, peer-to-peer communication capabilities, or wireless internet capabilities.

Figure 2 – Players beginning a round of Environmental Detectives spot their current location on a handheld computer and await readings from a recently placed sample.

Participants We examined Environmental Detectives being used in three separate classes at a private technical university in the Eastern United States. One class was a freshmen environmental engineering course; the other two classes were undergraduate technical writing courses, grounded in writing about research experiences. In both classes, the game was used to introduce students to issues around conducting real-world environmental investigations and used as a prelude for a larger research project. All three classes were two hours in length. Two of the classes took place during regular class time, while the third was done outside of class time. In the environmental engineering course, nine students played Environmental Detectives as an optional activity designed to help them conceptualize a larger environmental engineering study. Roughly twentyfive students participated in each of the two technical writing classes (for a total of 50 students), and the activity was designed to help students understand research methods as well as the tradeoffs associated with conducting any research project.

Methodology Consistent with Cobb (2000), this study took place within a broader design experiment context examining the potential for augmented reality simulations to support learning, in which we are designing instructional materials, researching how they are used in classroom contexts, and then redesigning them in response to our findings. In this study, we used a naturalistic casestudy methodology (Stake, 1995) to gain a holistic view of the activity that unfolded during gameplay, understand how learning occurred through participation in these activities, and remain responsive to unanticipated issues which might arise during the research. Because we were

interested in accounting for student-computer, student-student, and classroom culture – student interactions, we employed quasi-ethnographic techniques designed to capture student actions at the molar level (Goodwin, 1994). Following Guba and Lincoln’s (1983) recommendations, we used observations, interviews, and document analysis to triangulate data and increase the trustworthiness of our assertions.

Data Sources Observations. Four trained researchers attended each session, and a trained researcher followed each student team during the game, video-taping the group and documenting student practices in field notes. Consistent with other researchers studying problem-based learning environments (e.g. Barrows, et. al, 1998; Nelson, 1999), we paid special attention to student discourse, examining how students framed the initial problem, constructed goals of the activity, negotiated information in groups, planned activities, and developed shared understandings. We also recorded students’ inscriptions, physical gestures, and interactions with the Pocket PC. Interviews. We used informal, non-structured interview questions during the exercise to confirm observations, clarify students’ goals and intentions, and learn more about students’ handheld-mediated activities. Although the researchers were clearly participant-observers in the activity, they attempted to remain unobtrusive whenever possible. We also conducted a twenty minute focus-group exit survey to probe students’ experiences in depth to document their thoughts, feelings, and attitudes toward the experience. Document Analysis. Consistent with Silverman (1993), we gathered and analyzed data emerging from students’ activity. We collected students’ inscriptions and notes that were created in the context of inquiry (see Roth, 1996). Finally, sketches, diagrams, and notes created during the presentations were collected or recorded.

Data Analysis Two researchers viewed and analyzed all researcher field notes, video tapes, and students’ projects using the constant comparative method (Glaser & Strauss, 1967), to generate relevant themes from the data. Consistent with design experiment methodology (Cobb, 2000) and Stake’s responsive method (1995), we paid special attention to unexpected and unintended consequences, given the exploratory nature of this research. After each round of video tape viewing, we developed emergent hypotheses, re-examining and refining these hypotheses as we watched subsequent tapes looking for disconfirming evidence or counter-hypotheses. We then wrote a case study for one group as a means of communicating to the reader the flavor of the

activity, and give the reader a basis for generating contrary explanations and interpretations of the activity (although we recognize that our own perceptual filters will shape and constrain any subsequent interpretations). We present these assertions and themes as petite generalizations, generalizations which have explanatory power for us in understanding how learning occurred for these students through participation in Environmental Detectives and offer guidance in redesigning the curriculum and designing future augmented reality simulations. The situationality of these results are constrained by the unique nature of these students, the university culture in which each class is situated, and the particular interactions which we arose, and consistent with Stake’s (1995) case study approach, we believe that the reader is best situated to apply these findings to his or her own situations.

Results This study reviews findings developed using Environmental Detectives in three classrooms with 13 groups and 59 students. Rather than attempt to capture and present each student’s, or even each group’s experience with Environmental Detectives, we will provide a short depiction of how students have been interacting with Environmental Detectives, describing noteworthy student interactions, and how these events affected the design evolution of Environmental Detectives. Next, we present the experiences of one group in closer detail, as an attempt to both present the data upon which we will rest our conclusions, as well as situate the reader within the experience of Environmental Detectives to provide a vicarious experience of the activity.

Case Study All of the student groups started out in the center of the only large grassy area on campus, known as Killian Court. Students were given instructions on the operation of the GPS and the Pocket PC, and performed the required GPS calibration. This process took most of the groups at least 5 minutes to accomplish. One group walked toward the main part of campus. They were tailed by a researcher carrying a camera who answered technical questions when necessary. As the group walked, one of the student inquired, ―How many samples do we need?‖ It was not clear whether the question was addressed to the researcher or the rest of the group, but no one responded to the question. The student with the Pocket PC had previous GPS experience and started to guide the group. He drilled for one sample and then walked to nearby locations to take two more samples,

the maximum amount of concurrent samples permitted. He chose a triangular configuration, though when another student asked why he chose this arrangement, he cited no particular reason. As the students waited for the required three minutes between sample drilling and reading, they retraced their steps. Finally the sample was retrieved and the reading was 88. One of the other students in the group asked whether 88 was good or bad. One student hypothesized that the number could be a percentage, but none of the other students in the group could answer definitively. Regardless, they decided that their next step was to collect more data. As they walked to collect the other two drill rigs, one of the students not holding the Pocket PC asked what their data looked like. The student with the handheld described their current readings by pointing to the three locations in physical space (as opposed to showing on the handheld) and citing the readings. Students debated the meanings of these readings. One student hypothesized that the readings were in parts per million. The student holding the Pocket PC suggested that they should go in a particular direction, pointing into the distance, declaring, ―I’m guessing that higher numbers are greater so let’s head in the direction of the higher numbers.‖ They walked several hundred yards through several buildings in the direction of the higher number and placed more drills. After several minutes the readings from this second round of drill placements returned from the lab. One of the students noted that the new readings were very high in one direction. They walked in the direction of the higher readings, as if following a trail or scent, pausing briefly to interview a staff member in environmental policy, who happened to be nearby. The interview yielded little information, but it did reveal that they could conduct a second interview with a TCE supplier from facilities at a new location across campus (building 54), which they needed to visit within the next half hour because the informant was leaving for another meeting. They decided to immediately go to building 54 although there was no discussion about what information they hoped to find, or evaluation of the relative value of the information they expected to find there. Along the way they looked at the gradient and one student hypothesized that the concentration was likely to be higher on the other side of the building (the one they hadn’t visited yet). The second interview revealed where TCE is used on campus, and the student holding the Pocket PC summarized the information for the others. While discussing this information, the group took another reading. One student (not holding the Pocket PC) realized that the highest concentration appeared to be surrounding building 3, and suggested that they should lay down some more drills in that area. This idea was dismissed by one of the other students who assured

them that they already knew that the leakage was coming from building 3, and suggested that they go get some interviews to help them interpret their data. One student inquired whether they have a notebook to write down this information in, but none of the students has one. It is worth noting that although they had spent nearly 50% of their time already, the group did not know what units the readings were in (and indeed, one student hypothesized incorrectly that they were a percentage), what levels of TCE were dangerous, how likely the TCE was to spread throughout the environment (including into a nearby river), or what legal repercussions the university might face if the TCE were to leak off of university property. Seeing another interview nearby, they headed in that direction. One student noticed the time, and suggested that they use their last 15 minutes wisely. The Pocket PC changed hands briefly to a different group member, but was quickly given back to the student who has been holding it most of the time as they had trouble tracking down the point that they were headed to. After several minutes of circling the building, they finally accessed the interview which explained the flow of groundwater on campus through an historical anecdote. As the students headed back towards the class they discussed the implications of their findings. One student looked at building 3 (where they think the toxin originated) and then back at the river and declared that by the time the pollution gets to the river the pollution is likely to be highly reduced (although they have no information to back this up). During the debriefing the students said that they don’t believe this is a problem because people are unlikely to drink the groundwater in the area, and the river is too far from the source of the pollution to be a problem. They recommended planting trees to mitigate the problem and monitoring the situation over time. They noted that this solution would cause little alarm in the community, and not destroy the only grassy area on campus.

Findings Learning Through Virtual Investigations A major theme guiding the design of this project was to authentically recreate the practices of environmental engineers. In consultation with MIT environmental engineering faculty we decided that a significant component of this practice is the integration of background ―desktop‖ research (including the type of data found through interviews) with primary field data. This integration is ordinarily difficulty to capture in classroom activities, and consequently students have little experience with this process. In this case, however, it is necessary to combine desktop research on the toxicity levels of TCE, rate of flow, and historical records with primary

data gathered from wells on current water quality. The desktop research can be used to guide the collection of primary data (to hone in on likely sources), as well as the conclusions that one might draw from the data (to determine how far the pollution will flow, and what the consequences might be). In the course of the game play, students were driven almost exclusively by the collection of water quality data from the wells. Our video analysis showed that students’ first reactions were to collect samples either at the game starting location or the site that had already identified measurable levels of contaminant. This choice was seemingly made out of convenience. When students did decide to collect data from interviews, it was because the sites of those interviews were in proximity to sampling that they wanted to conduct, rather than because those people were likely to give them helpful information (interviews were associated with particular buildings that are the home to identified departments). Students often collected 6-10 water samples, taking most of the game time, before they ever determined what the units meant or what toxic levels were. This problem (not knowing toxic levels) was often elucidated in the conversations within groups, but typically dismissed in favor of collecting more data. Sampling Strategies. When collecting water quality samples, the majority of groups used a ―warmer/colder‖ strategy for finding the source of the pollution. They would take two samples and move in the direction of the sample with the higher concentration. This method proved to be largely successful, though it was susceptible to getting stuck on local minima (due to local variability or a smaller secondary spill built into the game), and was very data intensive. Two other strategies that were employed were triangulation, and concentric circles. Triangulation (perhaps suggested by the three simultaneous wells limitation in the game) involved drilling three wells in some relatively small area and then moving in the direction of the highest concentration. The concentric circles strategy was designed to start at the original site of contamination and then move out from there sampling along different radii. Neither of these strategies was more successful in the context of this game, though they might have involved fewer wells, and been less susceptible to local variation. As students began to collect data they would notice after the first 10-15 minutes that they were lacking the information required to interpret the significance of their findings. Many groups had a single person who called for the next step in the investigation to be research that would shed some light on their data, but this opinion was inevitably silenced by other group members who instead went to seek more water sample data. While this could be symptomatic of the ambiguity associated with the informational resources with which they were presented, we believe that this is indicative of a more fundamental failure in their approach to the problem.

The holes in the student desktop research were made more evident when they went to present their assessment and remediation plans to the group. Many of the groups ignored the gaps in their research. For example, one conclusion reported by several groups was that the TCE was unlikely to reach the river because it was ―far away,‖ even though they had not uncovered the information about how fast the TCE was moving or how long it had been there. Other groups made assumptions about the use of groundwater for drinking water, though they had not found the evidence to support these assertions. In some cases students recognized the shortcomings in their information, citing the lack of data on flow rates, or toxic levels, but then proceeded to make recommendations based upon assumptions that were often not correct. Regardless of this information, the proposed solutions were fairly consistent—because this is largely a drinking water problem, and since we don’t drink the groundwater here we should plant trees (which have been found to have a measurable but quite minimal effect on TCE) and monitor the situation. We have classified this solution as the ―political solution‖—on the surface it seems like it should satisfy the parties involved (doesn’t alarm the population, detract from the aesthetics of campus, or call attention to any environmental wrongdoings), but would be largely ineffective against any real problem. In reality this problem has no one solution that could satisfy everyone and address the real environmental and legal concerns (the pollution is likely to wind up in the river, which might upset environmentalists, though it might not have real environmental consequences, but flowing off the property at any level has legal implications). Students seemed unable or unwilling to make the hard tradeoffs and address this solution. And while ―monitoring the situation‖ is a key component, the ways in which this monitoring might inform policy have gone unaddressed.

Learning in Augmented Realities The use of Augmented Reality simulations in an educational context is a relatively recent innovation. Implicit in this design is an underlying (untested) premise that knowledge from the real world context will transfer to the simulation. In the course of this study, we found this assumption to be validated by student actions. In looking for the source of pollution students applied their knowledge of the campus and the departments occupying the different buildings. While they didn’t seek interviews based on this information, they did use it for clues as to the source of the contamination. Known printing presses, metal shops, and other places with large machinery were investigated. Students also demonstrated facility with mapping their data onto the real world context. Most of the groups were observed pointing to sections of the real world and stating the

concentrations at those locations. They would then point to the next location that they wanted to sample and travel there guided by the map on their computers. Throughout the activity we documented many episodes of group decision making and collaboration. Students often cited evidence that they collected from their handhelds in debating the next location that they wanted to visit. Augmented reality simulations have another potential advantage over their purely virtual counterparts. As we observed throughout the course of these activities, groups debated in real time using their voices, gestures and physical locations. While similar representations exist in virtual worlds, they require an initially negotiated standard. Emoticons in chat and hand signals by avatars are two examples of these emergent standards. The more familiar physical dialog was present throughout our student interactions. One common manifestation of this was when individual team members ―voted with their feet‖ in determining the next location to go. While this did not always result in democratic decision making (the person holding the computer seemed to have a larger vote), it did make immediately apparent what people’s opinions were, and provoked critical dialog.

Discussion Learning Through Game Play The formal structures of games (fictitious rules, competitive structures) shaped the activity which unfolded. Students interpreted the game as a mystery game, and made identifying ―the culprit‖ their primary task, while developing an ideal solution became the secondary task. The lack of qualifiers in the final presentations suggests that for most students this task was a closed-ended task, in that there was clearly a ―right‖ answer to which an ideal solution could be found, if only students looked long and hard enough (Jonassen, 1997). We believe that the gaming structure of this activity engaged students in a relatively complex problem-solving task, which required them to think with information, and apply scientific knowledge. This approach is known to help overcome the inert knowledge problem described by Whitehead (1929; See also CGTV, 1993). Adopting the language of gaming (as opposed to simulation) frames Environmental Detectives in a particular cultural lens as well. On the one hand, game play invokes a metaphor of learning through experimentation and inquisitiveness, as philosophers of education as long ago as Plato have used play as a metaphor for understanding learning. Conversely, other play theorists such as Roger Callouis (19**) have emphasized the frivolous nature of play, describing play as "an occasion of pure waste: waste of time, energy, ingenuity, skill, and often of money‖ (p. **).

From either approach, game play is defined as the adoption of arbitrary, fantasy rules (and roles) for the purposes of enjoyment (Sutton-Smith, 1997). We saw both manifestations of ―play‖ operating in these cases and playing a part in the resulting activities and learning that occurred. The game structure of Environmental Detectives engaged players in competitive and collaborative activities where they adopted the roles of environmental scientists. A second theme separating games from simulations are their competitive nature; the fact that games usually have winning and losing conditions, which guide and constrain activity. We envisioned the game taking place within a competitive environment, where the teacher might use a rubric as a basis for evaluating student responses and declaring a winner to the game. In these cases, both the teacher and students initially deliberated at length over the educational and entertainment values of playing collaboratively versus competitively during this unit. Both classes agreed to play the game competitively, with the caveat that groups would share any information on using the hardware and software. Both classes also decided that playing competitively would both be more fun and lead to greater learning, as long as they were allowed to debrief and share information at the end of the exercise. Importantly, both classes also decided that they did not want the teacher to elect a winner. As a result, no groups made ―winning the game‖ a goal, although one group was clearly competitive with other groups, as evinced by their spreading false information to other groups. Play theorists Eric Zimmerman and Katie Salen (in press) emphasize the malleability of game rules, arguing that successful games allow players to change rules in order to meet local constraints. Social factors, such as a community’s tolerance of competitive behavior, play a critical role in shaping how a game is played, and how a game will be adapted. In these cases, we saw that the classroom cultures were open to competitive games, although students wanted the ability of opting in and out of the game. It is worth noting that competitions are common at MI— robotics engineering, environmental engineering, artificial intelligence, and media design competitions are a long-standing, revered traditions at MIT, and are frequently used to support learning in courses. How such games are used in other cultures warrants further study; other cultures may not be so tolerant of games and competition. By design, Environmental Detectives is an open-ended game with no one ―correct‖ answer. There are positive and negative consequences to each decision, and players must design solutions within those constraints. We saw a few groups confused by this ambiguity, as more than one group asked questions which presumed a ―correct‖ response, such as, ―How many wells is enough?‖. Students’ lack of awareness for the incompleteness or insufficiency of their responses similarly reflected this tendency toward binary, right or wrong answers.

Learning through Failure in a game-based environment. By presenting students with an initial challenge to confront, supporting them with tools and resources to solve the problem, and then requiring them to present and defend solutions to problems, Environmental Detectives builds on established traditions of problem-based learning (e.g. Jonassen, 1999). Environmental Detectives deviates from most problem-based learning pedagogies in that students are forced to make decisions between different forms of information which has consequences for how well students do in the game. What sampling strategies students use, what information students decide to pursue, and when students decide to jump from sub-goal to sub-goal can have critical ramifications for student performance. This decision structure is designed to not only be engaging, but to model authentic scientific and engineering practices, including planning research strategies, evaluating the value of data sources, and constructing arguments in debating with team members. These results suggest that the presence of choices alone is not enough to compel students to evaluate the usefulness of information and gaps in their understanding. Repeatedly, students made decisions on what information to access based on convenience rather than through any critical reflection of the demands of the problem. This problem – students not identifying the information required to solve a problem—is a common one in problem-based learning, and we agree with instructional theorists who argue that teachers play a critical role in helping students identify resources when solving problems (e.g. Barrows, et al., 1998, Jonassen, 1999). Elsewhere, we saw students accepting solutions gleaned from other cases (provided within the context of the game) intact, without carefully considering what could be learned from the cases and then using this knowledge to guide the construction of new solutions. A second method for assisting students in identifying resources is through providing scaffolding which might help students identify gaps in their understandings or where they lack the resources for solving problems. A central tension guiding design of Environmental Detectives has been how to balance the game so that students are forced to make meaningful decisions, such as whether to sampling for TCE versus performing desktop research, or investigating the health hazards associated with TCE versus the EPA laws around TCE. Restated, determining how the ―knowledge-seeking‖ process can be scaffolded so that students can access the information they need to solve problems, while preserving the complexity of the real world so that the instructional designer does not ―notice‖ all of the relevant information for students, spelling out what information is useful for problem solving. One way out of this conundrum would be to provide more transparent descriptions of

what information each expert might provide; several of the groups mentioned at least in passing that they needed to learn more about the toxicity of TCE or how it flows through groundwater. We believe that making this information more apparent to students might make accessing such information less opaque, and we plan to reorganize the interview information so that players can easily scan each faculty or staff members’ department affiliation which might suggest what information is available (as well as introduce students to the different fields and subfields of chemistry, environmental engineering, hydrology, and so on). However, consistent with the problem-based learning approach (and good game design), we believe that it is critical to provide distracter information, such Comparative Media Studies faculty who offer information about toxins in popular film, or teacher education faculty who tell players about developing toxin models in StarLogo. On the other hand, this pattern of failing to discern what information is needed to develop an effective solution to the game represents an interesting failure that might be leveraged for future learning. The environmental engineers we interviewed argued that the ability to discern the central research problem at hand and re-evaluate the problem as necessary, including evaluating what information was needed to solve the problem, was a critical skill for novice environmental engineers to develop, and one which they themselves constantly faced doing environmental studies. If one takes seriously the constructivist underpinnings of problem-based learning which, to quote Roger Schank (1994) suggest that failure, perturbation in one’s mental models, is a precursor to learning, then perhaps allowing students to make these mistakes – to make choices and experience their consequences is a good thing. Perhaps one way to think about the pedagogical value of competitive games in education is to consider their role in inducing failure states, in providing a socially acceptable context for trying different strategies, experimenting with ideas, and then revising those ideas. It is here that we take inspiration from the teachers we collaborated with. Perhaps one way to think about the role of strategy games in learning environments is as precursors to conducting full scale investigations. The teachers we worked with saw Environmental Detectives as a useful tool for helping students understand some of the trade-offs in doing larger research projects. Perhaps games can provide one way for overcoming some of the challenges to more open-ended forms of inquiry-based learning, such as a lack of student engagement, or student’s experiencing cognitive overload at the challenges of conducting open-ended inquiry. Games such as Environmental Detectives might provide scaffolding for conducting larger investigations, serving as ―simplified, but authentic‖ conditions for larger, more complex tasks.

Implications Over the last few years, games and simulations have been criticized for their contrived, commoditized nature and contrasted with the social ―authenticity‖ of engaging in communities of practice, either through participation in extended communities of practice, or through establishing classroom-bound communities of practice engaging in authentic inquiry (e.g. Barab & Duffy, 1999). Quoting Lave (1993), Barab and Duffy pit learning environments predicated upon a practice field metaphor against one predicated on Lave and Wenger’s (1991) communities of practice, arguing that (in practice fields) the problems, although authentic in the complexity they bring to the learner, are not authentic in the sense that they are an integral part of the ongoing activity of the society. With the practice field, education is viewed as preparation for some later sets of activities, not as ―meaningful activity in its own right‖ (p.48-49). For Barab and Duffy, the practice field may have some use in constrained contexts, but is clearly less desirable than full blown participation in communities of practice. We believe that distinguishing between practice fields and communities of practice is useful, particularly in respect to what Lave calls the commoditization of knowledge, but to disregard practice fields as ―inauthentic‖ because they use fictional, imaginary worlds in the process of learning is assuming a simplistic notion of authenticity. We agree that when students engage in authentic tasks in order to gain some sort of exchange value, or external reward, rather than to pursue a line of inquiry, something important is lost. However, to equate fantasy, play, and simulation with inauthenticity is also misguided. Simulations, which are fictitious worlds, are now at the heart of many scientific endeavors, and are used to help scientists explore systems which are otherwise difficult, if not impossible to explore (Casti, 1998; Feurzeig & Roberts, 1999; Wolfram, 2002). While this process of learning through imaginary worlds is aided by the computer, learning through imaginary worlds, or play, is a cross-cultural phenomena with historical roots as least old as Plato (Callouis 19**). Consistent with Barab, Squire, and Dueber (2000) we understand authenticity to be a situated phenomenon, referring to the match between learning practices and other social practices. Minimally, authenticity consists of the intersection among task authenticity (tasks used in other cultural practices), process authenticity (processes used by other communities of practice), and individual’s intentions (goals reflected by other communities of practice) within environmental contexts involving tools, resources, and social relationships reflecting those communities in which others participate. Given the proliferation of simulation tools being used in the service of inquiry in the sciences and beyond, we believe that it is critical that educational theorists take the role of such simulation software in learning seriously; indeed, to ignore the role of simulations in

scientific inquiry may mean building less authentic learning environments. This study is but a first step in understanding the situationality of Environmental Detectives specifically, and augmented reality more broadly. In these cases, we saw Environmental Detective players spontaneously using information from their surroundings to solve problems within the augmented reality world, although the nature of this task precluded us from observing how readily players would also use experiences from this game to solve more socially complex problems. In future studies, we would like to see how students perform on similar tasks across a variety of contexts, ranging from virtual realities, to augmented realities, to non-computer-mediated realities.

REFERENCES Barab, S. A., Barnett, M., & Squire, K. (2002). Preparing pre-service teachers: Developing an empirical account of a community of practice. The Journal of the Learning Sciences, 11(4), 489-542. Barab, S., Cherkes-Julkowski, M., Swenson, R., Garrett, S., Shaw, R. (1999) Principles of Self-Organizing Systems: Ecologizing the Learner-Facilitator System. Journal of Learning Sciences, 8 (3/4). Barab, S. A., & Duffy, T. (2000). From practice fields to communities of practice. In D. Jonassen & S. Land (Eds.), Theoretical foundations of learning environments. Hillsdale, NJ: Lawrence Erlbaum Associates. Barab, S. A., Hay, K. E., Barnett, M., & Squire, K. (2001). Constructing virtual worlds: Tracing the historical development of learner practices/understandings. Cognition and Instruction, 19(1), 4794. Barab S.A. & Kirshner D., (in press). Methodologies for capturing learner practices occurring as part of dynamic learning environments. To appear in Journal of the Learning Sciences. Barab, S.A. & Luehmann, A. (in press). Building Sustainable Science Curriculum: Acknowledging and Accommodating Local Adaptation. To appear in Science Education. Barab, S.A., Squire, K.D. & Dueber, W. (2000). A co-evolutionary model for supporting the emergence of authenticity. Educational Technology Research and Development, 48(2), p. 37-62. Barron, B. J. S., Schwartz, D. L., Vye, N. J., Moore, A., Petrosino, A., Zech, L., Bransford, J. D., & The Cognition and Technology Group at Vanderbilt. (1998). Doing with understanding: Lessons from research on problem- and project-based learning. The Journal of the Learning Sciences, 7, 271311. Blumenfeld, P., Fishman, B., Krajcik, J., Marx, R., and Soloway, E. (2000). Creating Usable Innovations in Systemic Reform: Scaling up Technology-Embedded Project-Based Science in Urban Schools, Educational Psychologist, 35 (3), 149-164. Brown, A. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. The Journal of the Learning Sciences, 2(2), 141-178. Brown, J.S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18, 32-42. Bransford, J. D., Franks, J.J., Vye, N.J. & Sherwood, R.D. (1989). New approaches to instruction: Because wisdom can’t be told. In S. Vosniadou and A. Ornony, (Eds). Similarity and analogical reasoning, (pp. 470-497). New York: Cambridge, University Press. Bransford, J.D., Brown, A.L., & Cocking, R.R. (Eds.). (1999). How people learn: Brain, mind, experience, and school. Washington DC: National Academy Press. Casti, J., (1998) Would-be worlds : How simulation is changing the frontiers of science, John Wiley & Sons, New York, NY. Caillois,R. (1979). Man, play, and games. Schocken Books: New York. Cognition and Technology Group at Vanderbilt (CTGV), (1990). Anchored instruction and its relationship to situated cognition. Educational Researcher, 19 (6), 2-10. CTGV. (1993). Anchored instruction and situated cognition revisited. Educational Technology, 33 (3), 5270. Cole, M. (1995). The supra-individual envelope of development: Activity and practice; situation and context. In J. Goodnow, P. Miller, & F. Kessel (Eds.), Cultural practices as contexts for development (pp. 105-117). San Francisco: Jossey-Bass. Colella, V., (2001) Participatory simulations: Building collaborative understanding through immersive dynamic modeling Journal of the Learning Sciences, pp. 471-500. Cobb, P. (2000). Conducting teaching experiments in collaboration with teachers. In A. E. Kelly & R. A. Lesh (Eds.), Handbook of research design in mathematics and science education (pp. 307-333). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Dewey, J. (1938). Context and thought. In R. Bernstein, (Ed.) On experience, nature and freedom. (pp. 88110). New York: Library of Liberal Arts. diSessa, A., (1986). ―Artificial worlds and real experience‖, Instructional Science, pp. 207-227.

Edelson, D. C., Pea, R. D., & Gomez., L. (1996). Constructivism in the collaboratory. In B. G. Wilson (Ed.), Constructivist learning environments: Case studies in instructional design, (pp. 151-164). Englewood Cliffs, NJ: Educational Technology Publications. Elliot, J., Adams, L, & Bruckman, A. (2002) No Magic Bullet: 3D Video Games in Education. Proceedings of ICLS 2002, Seattle, WA, October 2002. Engeström, Y. (1993). Developmental studies of work as a testbench of activity theory: The case of primary care medical practice. In S. Chaiklin & J. Lave (Eds.) Understanding practice: Perspectives on activity and context (pp. 64-103). Cambridge, MA: Cambridge University Press. Engestrom Y. & Cole, M. (1997). Situated cognition in search of an agenda. In D. Kirshner, & J.A. Whitson (Eds). Situated cognition: Social, semiotic, and psychological perspectives, (pp. 301310). Mahwah, NJ: Erlbaum. Feurzeig, W., and N. Roberts (1999), Modeling and simulation in precollege science and mathematics, Springer, New York, NY. Gibson, J.J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin. Glaser, B. G., & Strauss, A. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine. Goodwin, C. (1994) Professional vision. American Anthropologist, 96(3), 606-633. Greeno, J. G. (1998). The situativity of knowing, learning, and research. American Psychologist, 53, 5-26. Guba, E. G., & Lincoln, Y. S. (1983). Epistemological and methodological bases of naturalistic inquiry. In G. F. Madaus, M. S. Scriven., and D. L. Stufflebeam (Eds.), Evaluation models: Viewpoints on educational and human services evaluation (pp. 311-334). Boston, MA: Kluwer Jonassen, D. (1999). Designing Constructivist Learning Environments. Instructional-Design Theories and Models. C. Reigeluth. Mahwah, New Jersey and London, Lawrence Erlbaum. Krajcik, J., Blumenfeld, P., Marx, R., Bass, K., Fredricks, J., & Soloway, E. (1998). Inquiry in projectbased science classrooms. Journal of the Learning Sciences, 7(3&4), 313-351. Lave, J. (1988). Cognition in practice. Cambridge: Cambridge University Press. Lave, J. (1993). Situating learning in communities of practice. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.) Perspectives on socially shared cognition (pp. 17-36). Washington, DC: American Psychological Association. Lave, J. (1997). The culture of acquisition and the practice of understanding. In D. Kirshner, & J.A. Whitson (Eds). Situated cognition: Social, semiotic, and psychological perspectives, (pp. 17-36). Mahwah, NJ: Erlbaum. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York: Cambridge University Press. Leont'ev, A. (1981). Problems in the development of mind. Moscow: Progress Publishers. Nelson, L. M. (1999). Collaborative problem solving. In C. M. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory. Mahwah, NJ: Lawrence Erlbaum Associates. Norman, D. A. (1988). The design of everyday things. New York: Doubleday. Pea, R. (1993). Practices of distributed intelligence and designs for education. In G. Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 47-87). Cambridge, UK: Cambridge University Press. Peirce, C.S. (1868/1992). Questions concerning faculties claimed for man. Journal of Speculative Philosophy 2. Reprinted in N. Houser & C. Kloesel (Eds.) The Essential Peirce (Vol 1, pp. 1127).. Bloomington, IN: Indiana University Press. Reed, E. S. (1991). Cognition as the cooperative appropriation of affordances. Ecological Psychology, 3(2), 135-158. Resnick, L. B. (1987). Learning in school and out. Educational Researcher, 16, 13-20. Roth, W.-M. (1996). Knowledge diffusion in a grade 4-5 classroom during a unit on civil engineering: An analysis of a classroom community in terms of its changing resources and practices. Cognition and Instruction, 14, 179-220. Salzman, C. Loftin, B. & Ash, K. (2000). Using Virtual Reality Technology to Convey Abstract Scientific Concepts, in Learning the Sciences of the 21st Century: Research, Design, and Implementing Advanced Technology Learning Environments. Savery, J., & Duffy, T. (1996). Problem based learning. An instructional model and its constructionist framework. In B. Wilson(Ed.) Constructivist learning environments: Case studies in instructional

design (pp. 135-148). Englewood Cliffs, NJ: Educational Technology Publications. Scardamalia, M., Bereiter, C. & Lamon, M. (1994). The CSIL project : Trying to bring the classroom into World 3. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice, (pp.201-228). Cambridge, MA: Massachusetts Institute ofTechnology. Schank, Roger C. (1994). Goal-Based Scenarios: A Radical Look at Education. Journal of the Learning Sciences, 3(4), 429-453. Schon, D. A. (1987). Educating the reflective practitioner. San Francisco, CA: Jossey-Bass. Silverman, D. (1993). Interpreting qualitative data: Methods for analysing talk, text, and interaction. Newbury Park, CA: SAGE. Smith, J. P., diSessa, A. A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis ofvknowledge in transition. The Journal of the Learning Sciences, 3 (2), 115-163. Soloway, E., Grant, W., Tinker, R., Roschelle, J., Mills, M., Resnick, M., Berg, R., & Eisenberg, M. (1999). Science in the palm of their hands. Communications of the ACM, 42(8), 21-26. Spitulnik, J., Studer, S., Finkel, E., Gustafson, E., Laczko, J., Soloway, E. (1995). The RiverMUD Design Rationale: Scaffolding for Scientific Inquiry Through Modeling, Discourse, and Decision Making in Community-Based Issues. Technical Report, Highly Interactive Computing Research Group, University of Michigan. Squire, K.D. & Johnson, C.J. (2000). Supporting communities of practice with interactive television. Educational Research Technology & Development, 48(1). Squire, K.D., Makinster, J., Barnett, M., Barab, A.L., & Barab, S.A. (2003). Designed Curriculum and Local Culture: Acknowledging the Primacy of Classroom Culture. Science Education. 87:1– 22Stratford, S. J., Krajcik, J., & Soloway, E. (1998). Secondary students' dynamic modeling processes: Analyzing, reasoning about, synthesizing, and testing models of stream ecosystems. Journal of Science Education and Technology, 7(3), 215-234. Sutton-Smith, B. (1997). The ambiguity of play. Cambridge, MA: Harvard University Press. Walkerdine, V. (1997). Redefining the subject in situated cognition theory. In D. Kirshner & J.A. Whitson (Eds). Situated cognition: Social, semiotic, and psychological perspectives, pp. 57-70. Mahwah, NJ: Erlbaum. Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge, MA: Cambridge University Press. Whitehead, A. N. (1929). The Aims of Education. New York: The Free Press. Winograd, T., & Flores, F. (1986). Understanding computers and cognition: A new foundation for design. Reading, MA: Addison-Wesley. Zimmerman, E. & Salen, K. (in press). Game + design: An interactive design handbook. Cambridge, MA: MIT Press.


								
To top