Jeremy Roschelle

Roy Pea

John Brecht

Mark Chung

Tristan De Frondeville

Chris Digiano

Jim Kaput

Sarah Lewis

Judy Li

Charlie Patton

Deborah Tatar

Phil Vahey

Wenming Ye We

A walk on the WILD side How wireless handhelds may change computer-supported collaborative learning"

Keywords: Handhelds, design, wireless networking, collaborative learning architectures, CSCL controversies, WILD (Wireless Internet Learning Devices), shared knowledge, augmentation frameworks, classroom workflow, data mining

Designs for CSCL (Computer-Supported Collabol-ativr Learning) applications usually presume a desktop or laptop computer. Yet future classrooms arc likely to be organized around Wireless Internet Learning Devices (WILD) that resemble graphing calculators, Palm, or Pocket-PC handhelds, connected by short-range wireless networking. WILD learning will have physical affordances that are different from today's computer lab, and different from classrooms with 5 students per computer. These differing affordances may lead to learning activities that deviate significantly from today's images of K-12 CSCL activities. Drawing upon research across a range of recent handheld projects, we suggest application-level affordances around which WILD-based CSCL has begun to organize: (a) augmenting physical space, (b) leveraging topological space, (c) aggregating coherently across all students, (d) conducting the class, and (e) act becomes artifact. We speculate on how CSCL research may consequently evolve towards a focus on kinds of systemic coupling in an augmented activity space.

Introduction

Handheld computers will become an increasingly compelling choice of technology for K-12 classrooms because they will enable a transition from occasional, slrpplemental use to frequent, integral use (Soloway et al.,200 1; [START_REF] Tinker | The whole world in their hands[END_REF]. This transition is dl-iven partly by tlie relatio~~ship between cost and the student-computer ratio. With desktop technology, cost is high, and computer resources must be shared. %day the typical student-computer ratio is 5: 1, with computers in schools most often located in special computer labs rather than the 01-dinary classroom (Cattagni & Ferris, 2001). A teacher must schedule lab use and move the class there (Hecker, 1999). This practice guarantees occasjonal, supplemental computer use at best, a challenge to integrating it with other learning matel-ials and activities in the classroom. Further, this limits the possible overall impact of computing in education: iian instructional resource is used infrequently, it is unlikely to have a large effect. Lal-ge screen size nlay be useful, to be sure, but comes , ~t these sig~.'.?cs::: c o ? -.

By comparison, handheld computers are more af'lbrdablc, making a 1:l student-computer ratio and ready-at-hand computing feasible with their smaller physical size. Tod,iy many math classes either purchase a classroom set of graphing calculators, or require every student to purchase their own unit, enabling frequent, integral use. Some reform-oriented mathematics texts require handheld technology (whereas almost n o widely-sold curricula require desktop computers, because of their limited availability). In the near term, Wireless Internet Learning Devices (WILDs) will likely become available in the same price range as today's Palm devices or advanced graphing calculators, and include short-range wireless networking. WILDS will be at least as powerful as early Macintosh computers, and far more powerful than Apple 11s -allowing a range of powerful learning software. And WILDs will be portable, so students can take them into the field for scientific data gathering (Gay et al., 2001; Soloway et al., 1999; Staudt & Hsi, 1999), to their study hall, on the bus, to a museum [START_REF] Bannasch | The electronic curator: Using a handheld computer at the Exploratorium[END_REF], or anywhere learning happens. Already, companies such as Texas Instruments, Palm, Handspring, Symbol Technologies, Mindsurf, Classroom Connect, and Scholastic are directing attention to creating and supporting WILD classrooms. Evaluation data collected from over 100 K-12 teachers who had 1-2 semesters of experience using 1:1 e-learning in their classrooms after receiving Palm Classroom Teacher Awards (from a pool of 1400 applicants) was overwhelmingly positive (Crawford 8( Vahey, 2002). More specifically: "Handhelds were seen as having positive effects on student learning, on tcaching practices, and on the quality of leal-ning activities. Teachers also stated that handheld technology can make technology more integral to teaching and learning. When asked to indicate theil-degree of agreement or disagrec~nent with statements about handhelds, teachers' responses were as follows: 96.5%

indic'ltcd that they hclicved I);~ndhcld cornputc15 wcl-e eflfcctivc instructional tools for teachers, and 93% stated that tlie use ofhandheld cornputel-s conti-il,utcd positively to the quality ol'the lea~ming activities their students completed. 'I'hc lollowing bcncfits ofhandht.lci technology wet-e cited most often: pol-tahility and ease of ~c e s s , the integl-ation OI computing into a wide variety ol educational activities, promoting autonomous learning and student 01-gdnizstion, pron~otirrg student motivation, promoting student collaboration and communication (using infrared beaming), and supporting inquiry-based instl-uctional activities. Although teachers wet-c very favorable in their evalua- tion of handheld computers for teaching and learning, they did report somc problems, i~icluding the tollowillg: damage to the handheld devices (especially the screen), problems with synchronization of h n n d h ~l ~~ -,~.~~n-~tpl-data wit11 desktop computers, and somc inappropriate use (such as game playing ,~nd of-tdsk beaming)." (Crawfo~d 8( V,lliey, 7002, Executive Sunimary) Given the continuing emph,lsis on col1abo1-ative and communicative processes in subject matter standards such as NC'I'M arid the National Science Education Standards (in the US), many WILD classrooms should become classrooms more characterized by computer-supported collaborative learning. Students will work towal-ds shared understanding in groups. Students will build joint representations of their knowledge. To enhance understanding, students will point to, annotate, and use external repr-esentations in diverse sense-making and discourse practices (Pea & Gomez, 1992). And teachers will have a strong role in managing a learning process that involves many active, communicating learners. Yet, because of the differences in WILD classroon~s vs. computer labs, we conjecture that CSCL applications may have to radically change, and that new research questions will surface. O u r paper is devoted to documenting, reflecting on, and exploring these new directions.

Like conventional computer labs, WILD classrooms should support computational media with cognitively-empowering representations (e.g., simulations, manipulable mathematical notations. modeling tools, diagramming tools). And like recent computer labs, WILD classrooms should support network communication both among local peers and to distant servers. But unlike desktops, WILD classrooms will likely feature relatively small screens. Battery life and heat dissipation issues will prevent intensive use of streaming media o r broadband networks for years [START_REF] Ledbetter | Wireless secrets and lies[END_REF]; although be sure to track developments in ultra wideband wireless networks given February 2002 FCC approvals). And the basic functional characteristics (screen size, processing power, memory, network speed) of handhelds are not rapidly increasing with Moore's Law; improve~uents have been slight over the last 3 years.

Perhaps even more importantly, WILD classroon~s will have affordances not available today that are ripe for new CSCL uses. In particular, CSCL should support peer-to-peer communications, and handhelds provide the affordances to do so. WlLD devices have the capability of directed communication (IK, or infrared beaming) to a specitic person via a physical gesture, instead of selecting a logical name or typing it in. WILD classrooms can thus naturally support peer-to-peer and n~ulticast network topologies, beyond today's predominant client-server computing style. Peer-to-peer communication is naturally supported by beaming, and as Napster makes clear, can have very different collective characteristics and emergent phenomena than client-server communication. Multicast ~~~i l ! I,r supported because radio-based wireless is naturally multicast in a classroon-sized space.

Further, although CSCL research brought to light theories of distributed intelligence [START_REF] Pea | Practices of distributed intelligence and designs for education[END_REF] or distributed cognition [START_REF] Hutchins | Cognition in the wild[END_REF], that knowledge was only visible in two kinds of places in typical CSCL activities: a student's head or a computer display. In a WILD classroom, there may arise more differentiated places for information and knowledge, and highly differentiated "things that make us smart" [START_REF] Norman | Things that ninke us smart: DefendinA izumin rrtili utes in the age of the machitre[END_REF]: devices with different characteristics may proliferate (some larger screens, some with more computational power, some with more colors, or special graphics co-processors) and special purpose information appliances may emerge (e.g., "SmartProbes" that can store data,' Lego MindStormsT" robots, wireless printers driven by IR and Bluetooth beaming). Students are more likely to be choosing appropriate assemblages of devices for their knowledge work than in conventional desktop-based CSCL, highlighting a growing need for the development and deployment of meta-tool knowledge. In this paper, we consider how these changing affordances may change CSCL applications.

Our article begins by surveying several early WILD applications, in order to abstract some application-level affordances of WILD (as compared to the physicallevel capabilities we discussed above). We then suggest some of the differences these application-level affordances may bring to CSCL, and highlight how WILD is likely to create a new application type, along the lines of augmented activity spaces. Beyond thinking about possible application types, we may speculate about the fault lines that might organize future CSCL research.

A look at WlLD in the wild

Although classroom research using handheld computers has been going on for years, and has spawned some large research grants recently, there are n o formal surveys of WILD applications. We forego assembling a complete survey here, instead describing a handful of WILD application types involved in one or more projects at SRI, or that have been described at past CSCL meetings. The SRI projects include CILT -the Center for Innovative Learning Technologies (http://cilt.org, a distributed center with broad-based participation from hundreds of organizations in collaborative projects on themes including "Ubiquitous Computing"), the Pa'.:! Ed!!cnricv Pioneers program (awarding competitive grants of free handheld computers to every student and teacher in over one hundred classrooms), SiriiCnIc (a mathematics project that has investigated handheld learning for 4 yean), and a U.S. Department of Education grant that developed a handheld assessment tool. We only include application types that: (a) have been used by multiple researcherldevelopers in building WILD prototypes, and (b) have some early evidence that the proto types yield interesting classroom exyeriences.

We will consider the systems in the following list:

1. ClnssTnlkis a networked classroom communication system in which any of five question types (multiple choice, numeric, short and long text, algebraic expressions) can be provided by a teacher to students, so that when their answers are submitted, a histogram of their aggregate work is displayed to the students and the teacher so as to guide subsequent classroom discoum about student learning and difficulties with specific aspects of the subject materials (Dufresne et al., 1996; Abrahamson et al., 2000; Mazur, 1997). 2. ImageMap is an assessment feedback system for supporting media-rich learning conversations that we are developing at SRI International. An image (e.g., graph, map, photo) is distributed to each student with a handheld networked device, a question is asked about the representation, and each student annotates the image with a response. A server receives these responses from the pool of students, aggregates their responses by superimposing their annotations in some manner on the image that was distributed, and projects them on a public display, allowing students and teachers to see the distribution pattern of different answers. 3. Probeware describes the use of probes and sensors connected to computers (whether handheld or desktop) to collect and display real-time measurements of environmental parameters such as temperature, light, motion, force, so~uid and clcctrical power [START_REF] Tinker | Portable tecl~nologies: Science learning in context[END_REF]). l'hornton (1997) demonstrated that high school students' intuitive ideas about motion, velocity, acceleration, and force become more accurate when using probeware than using any other instructional strategy, including lectures, problems, or traditional labs. 4. Mobile computing has been shown to enhance field study using digital imagery (in educational studies involving botanical species identification: Gay, Reiger, & Bennington, 2001). 5. Participntory Sittrlrlntiorrs is a phrase increasingly used to describe a paradigm in which small wearable computer 'badges' or handheld conlputers are used to create life-size simu1af;n--rtiv;+ies in which participants can represent conceptual entities in a complex system so as to simulate, for example, the spread of viruses, or cars in traffic (e.g., Colella et al., 1998; Wilensky & Stroup, 1999). After experiencing a simulation, participants work together to analyze data, create hypotheses, and conduct experiments to infer underlying rules for their sinlulation. 6. Building on the work of the SimCalc project on the "Hubcalc" concept of connecting many handheld devices to the teacher's computer (Wilensky & Stroup, 2000), Texas Instruments developed a wireless classrooni communication system that connects handheld graphing calculators so that programmed tasks can be sent within a classroom to calculators for students to work on. [START_REF] Wilensky | Networked Gridlock: Students Enacting Complex Dynamic Phenomena with the HubNet Architecture[END_REF] developed such a task, where students each control a traffic light on a projected traffic grid and the class as a whole has the goal of setting up rules for smooth traffic flow. Additionally, a NSF-funded project is investigating classroom wireless networks of handheld computing versions of SimCalc environments for learning the mathematics of change and variation (e.g., Kaput & Hegedus, 2002). 7. In one CILT project, the Exploratorium is exploring use of a wireless network and handheld computers to provide information and scaffolding for museum visitors as they virtually explore an outdoor setting. Visitors walk through the landscape with a handheld networked device, linked to a wealth of information and media related to their direct experience of the ecosystem. The online information is navigated through a visual representation of the trails and of the wetlands at large; at the same time, sensors in the environment read the movements of the visitor, enabling the delivery of information specific to that location (http://www.exploratorium.edu/ lagoon; for related work, see [START_REF] Bannasch | The electronic curator: Using a handheld computer at the Exploratorium[END_REF][START_REF] Exploratorium | Electronic Guidebook Forum: lsslres and Questions about Wireless Handheld Coniputers in Eshibitiorl Spaces[END_REF].

In addition to this list, there are many functional WlLD uses in classrooms that are not particularly collaborative: organizer, attendance, and student record keeping. Here we maintain the emphasis on inquiry processes, social constructivist analyses, and distributed cognition designs that are characteristic of CSCL (Koschrnann, 1996).

Analysis of WILD application-level affordances

In this section, we will generalize across the list of WlLD applications above, and describe application-level affordances that sec: :

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istic of this emerging technology.

I . A u g t r ~n t i r g physicrll space wit11 ii~formntiotr excknnges

We find that virtually all the WILD applications above either augment or amplify an existing physical space with information exchanges [START_REF] Engelbart | Augmenting human intellect: A conceptual frainework (Summary Report, AFOSR-3233)[END_REF]; the space the students are engaged in during their activity includes the devices, but is not limited to the space within the screen. Participatory Simulation activities illustrate this robustly; the badges or devices overlay information exchanges on the physical movements of the students, and the information and students' memory of their movements are the focus of inquiry. Probeware and the museum scenarios share this characteristic, but it is less prominent in the HubCalc, NetCalc and ClassTalk scenarios, although the activity space is still very much a physical classroom space (the "moves" enacted by the teacher and students are, significantly, moves in the classroom discourse space, which is augmented by information exchanges). In contrast, archetypal CSCL most often concentrates attention on spaces that are wholly contained within the bounds of the computer screen.

This potential power of augmentation may be understood by analogy to microworlds. Piaget, the intellectual spirit behind Papert's concept of microworlds, theorized that facility with abstract representations, which are more advanced than concrete representations, arrives later developmentally. Developers of microworlds invert this theory with the design principle that transforming abstract ideas into a manipulative, exploratory concrete form makes the abstraction more learnable. But microworlds only took the abstractions as far back as concretely realized sign systems (even if they initially began with a physical robotic "turtle" in the early implementations of Logo). Participatory Simulations and Probeware reconnect abstractions with embodied, physical, spatial explorations that precede concrete sign systems. This may make the learners' experience of abstract concepts yet more visceral and meaningful [START_REF] Colella | Participatory Simulations: Using computational objects to learn about dynamic systems[END_REF][START_REF] Colella | Participatory Simulations: Building Collaborative Understanding through Immersive Dynamic Modeling[END_REF].

2. Leveraging topological space, of two distinct kinds [START_REF] Lemke | Typological and Topological Meaning in Diagnostic Discourse[END_REF] makes a compelling distinction between typological and topological representations, suggesting the interplay of language-based, taxonomic, categorical representations ("typological") and spatially based, visual, continuously varying representations ( " t n y l o + ~-~" ' .

Lemke argues that much ofthe history of mathematics revolves around the fruitful interplay of these representations.

The WILD applications noticeably leverage topological space, capturing information based on spatial proximity and preserving for reflection that which is simultaneously topological and typological. For example, the ImageMap assessment represents degrees of student understanding through a direct spatial mapping of individual contributions to an aggregate representation. Even the more rudimentary ClassTalk-in the first instance a multiple-choice/ typological system -emphasizes topological representations by presenting: (a) results as an easily interpreted histogram, rather than as tables of numeric data, and (b) students with stimuli that are choices among multiple visual representations. Likewise, Participatory Simulations exchange information based on interstudent proximity in the virus role-play to examine the dynamics of disease transmission. SimCalc representations are editable graphs that are topological in nature, and in Probeware, the placement of a probe in a data source (a spatial act) results primarily in a graph (a spatial representation). The museum scenarios focus on image capture and proximity to an exhibit (or outdoor landscape element) as key drivers of the information exchange. While we have highlighted 2-D representations in the ImageMap assessment characterizations, we can foresee 3-D and 4-D image-based assessment activities as well. An example of a 4-D image assessment task item (time is the fourth dimension), would be a digital video record of a classroom teaching interaction that requires a group of pre-service teachers to each highlight, with graphical and text annotations, specific problems with the instructional strategies used by the video recorded teacher at different moments in time. Aggregate representations of the pre-service teachers' responses could bring to light different perceptions of the issues that the video recorded teacher faced in his or her instruction, and help guide a reflective learning discussion among the faculty member and pre-service teachers.

This emphasis on computer use to bring more topological representations into the classroom continues an overall trend to balance topological (e.g., graphs) and typological (e.g., algebra) representations that has been an important part of past CSCL research. But beyond that, WILD classroon~s have new affordances that make topological representations even more powerful, and typological representations less so. The stylus used with handheld computers as a pointing and inscriptional device makes it especially easy to correlate user control with spatial representations, even more so than with a mouse. Further, directional beaming and probe placement connects information exchanges to simple physical gestures, whereas most conventional CSCL exchanges must use icons or labels to represent logical destinrtic~r -n d -0urces of information flows. Conversely, it is intriguing that Palm OS devices, the trendsetter in user interfaces for handhelds, have dramatically simplified their design vis-i-vis desktop computers in part by simplifying typological representations from hierarchies to flat categorical lists: on a Palm OS handheld, one cannot organize folders of folders of folders of files; only a single level of categorization is allowed. And although a portable keyboard makes writing easier, Palm OS devices are not good for reading or writing large amounts of text.

We make an important distinction between two kinds of topological representations that we designate as "geospatial" and "semiospatial." Geospatial representations (geo = "of the world") are defined by formally specifiable mapping functions from measurable spatial parameters of the physical world (distance and direction, as in terms of height, depth, width) and their representational system counterparts (i.e., inscriptions: such as 2D and 3D maps, drawings, pictures). In contrast, semiospatial representations are those in which the spatial attributes of the topological representation are not mappable to spatial attributes of the physical world (except to those of the inscription itself). Semiospatial representations include Cartesian and other graphs, concept maps, flowcharts, and non-geo-gridded information visualizations generally. More technically, semiospatial representations are those for which, if one were to ask a geospatial question about aspects of a specific representation-such as "*How many meters away is the concept 'President' from 'Vice President' in an organizational chart for the U. S. government?" -one would be committing what Gilbert [START_REF] Ryle | The concept of mind[END_REF] would have called a "category mistake," from which various logical fallacies and conceptual conundrums may follow. Semiospatial representations are useful for supporting reasoning, argumentation, and deictic functions that are important for establishing co-reference and attentional alignment in collaborative learning.

The following scenario illustrates the power of topological features of semiospatial representations for learning. A teacher creates a diagram on a whiteboard, captures it with a digital camera, and then distributes it to the students' handheld computers in the classroom. These handhelds allow pointing with a stylus to spaces on that diagram so as to answer her question: "Which link in this concept map did you find most difficult?" An instructional discourse then ensues when the class and teacher see the aggregated results of their link selections depicted on the computer-projected display of the diagram with data superimposed. The semiospatial representation provided by this technological augmentation of the physical whiteboard space, in the diagram's depiction on each student's handheld display, provides the common spatial framework for L ~L .

Lemke's typologicalltopological distinction and our geospatial/semiospatial distinction can be viewed as part of an overall educational technology interest in understanding the cognitive value and educational use of multiple representations [START_REF] Kaput | Technology and mathematics education[END_REF][START_REF] Kozma | The use of multiple, linked representations to facilitate science understanding[END_REF][START_REF] Shafrir | Representational Competence[END_REF]. For handhelds, such multiple representations are likely to be distributed across multiple devices. In NetCalc classrooms, we observed students aligning multiple devices so they could compare multiple representations. In the Datagotchi scenarios developed as a CILT seed project (http://www.cilt.org/images/DataGotchi.pdf), we suggested that students would naturally line up their WILD handhelds to form larger spaces along which representations could be compared (in the manner of [START_REF] Rekimoto | A multiple device approach for supporting whiteboard-based interactions[END_REF].

Aggregating coherently across all students participating individually

Another interesting characteristic of three of the WILD applications is that they aggregate information generated by all the individual students in the classroom. This is most salient in the ClassTalk and ImageMap applications, where each student contributes an answer, and all answers are rapidly aggregated into a single representation. In planned extensions to the ImageMap, we take this strategy further so that an exploration can occur simultaneously with all students participating. The idea is that an unknown shape (perhaps a phase plot of a chaotic motion) can be generated by having many students each exploring different portions of the parameter space. As the plot fills in with different contributions, students can start to see regions that haven't been explored, and ones where something interesting might be happening. This intermediate representation can then direct their continued exploration, as they see what they are building together [START_REF] Pea | Seeing what we build together: Distributed multimedia learning environments for transformative communications[END_REF]. Aggregation across everyone also features prominently in Participatory Simulations. Many NetCalc/HubCalc scenarios involve students contributing individual mathematical objects to an overall aggregate representation that includes the whole class. Not only are all the students' responses aggregated, but they are also aggregated in a coherent representation that can be read and understood as a whole fairly easily. They are thus akin at a within-classroom level to the aggregate scientific visualizations in student-scientist partnership projects such as GLOBE, Global Lab, and KidsNet, in which students from disparate sites collect local data defined by scientific protocols that are then aggregated at a remote server and reflected back for interpretative discussions at local sites [START_REF] Cohen | Internet linksfor science education[END_REF]. In contrast, in archetypal CSCL it is far more comnlon for only 2 or 3 stuuents LO contribute to a shared representation (e.g., Single Display Groupware). Or in cases with large numbers of contributors (e.g., Knowledge Forum), the aggregation emerges slowly and asynchronously and may not produce a cohesively readable overall representation.

Aggregating coherently across all students is particularly important because it enables quick formative assessments that can allow the teacher to "take the pulse" of learning progress for the classroom as a whole. Further, because all students have individual devices, the teacher can ensure that all students are participating individually. And because every student has a role in the aggregate representation, they may take a more active role in discussions; they are literally represented in the information structure that supports the instructional discourse, rather than outside of it as an information consumer. By contrast, in conventional CSCL, with multiple students crowded around one machine, freeloading is a common phenomenon and the teacher must visit each group of students to track progress.

Conducting classroom performances

It has become fairly common to describe the positive changes in the teacher's role brought about by CSCL as a move from "sage-on-the-stage" (teachercentered instruction) to "guide-by-the-side" (teacher as coach, or guide to small group or individual learners working with educational technologies), an arrangement in which more attention can be given to understanding students' thinking and reasoning than with lecture-centric instruction. The move to "guide-by-theside," however, is at least partially an artifact of desktop technology; there is literally nowhere else for the teacher to go when 2 to 4 students are crowded around a single monitor. In a WILD classroom, this physical constraint does not apply, and it is not at all clear from our examples that the teacher will only be a "guide-by-the-side," as more interesting and powerful roles are possible. What then will be the apt metaphor for the role of the teacher in a WILD classroon~?

The WILD applications above have in common a teacher role much more like the "conductor-of-performances" for an orchestra: students in a WILD classroom are contributing to an overall performance. In the ImageMap application (and especially the extended version described above), they generate an overall aggregate representation, with a coherent visual gestalt. In Participatory Simulations, they participate in a simulation run (like an emergentlychoreographed performance). In SimCalc/NetCalc, they contribute to an overall animation. For all three cases, students contribute to a joint performdllce, ~e r u d ~i y and with input technology. The teacher attends primarily to group performance, not to each individual student. Moreover, the teacher, like the conductor, has responsibility for choosing and sequencing the material to be performed (the curricular activities), interpreting the performance, and guiding it toward its desired forms. As in rehearsal, the conductor might direct groups of students to practice something alone, or in small groups. During performance, the teacher will work to ensure that all parts are heard, that everyone gives their best performance-directing attention towards the students who need the most encouragement while keeping the overall performance moving forward.

WILD technology provides a radical shift because, unlike personal desktop computers, it creates the communication and computational conditions that make collective performance with representations both possible and meaningful in the aggregate. In some ways, such collective performances share key elements of the sage-on-the-stage, but are more dialogic by design. Full group participation contexts will be featured more fully, and teacher-led discussion around the contributions of an individual or group will become more prominent. But unlike sage-on-the-stage, the teacher need not bear primary responsibility for filling in the turns of the representational and conversational space. WILD technology will readily facilitate contributions from students and groups that can create transformative learning conversations as the norm [START_REF] Pea | Seeing what we build together: Distributed multimedia learning environments for transformative communications[END_REF]Polman & Pea, in press), rather than those of information transmission. Moreover, like the conductor, the WILD paradigm puts the teacher naturally in a position to notice whether and how much each participant is contributing, and thus can help the teacher work on having all the students continuously working towards the classroom performance.

Act becomes artqact ications

The final application affordance we draw attention to is that WILD appl' have the potential to instrument the learning space to collect summaries of messaging patterns and messaging content over longer timespans and over multiple sets of classroom participants to enable multi-level analyses of patterns of interactions and outcomes. Instant messaging (also called "texting" or SMS) is hugely popular among teens in countries where SMS is universally available on cell phones. We expect that messaging of text, representations, and data will become much more frequent in WILD learning spaces, and that the overall patterns of messaging, as well as message contents might be productively analyzed. Of our sample applications, this potential is most clear in the Exploratorium/museum examples; by giving individual visitors devices for interacting with exhibits, interesting use histories can be collected across a large set of visitors. Each visitor's exhibit interaction becomes a captured artifact; the database of interactions can be data-mined, analyzed, and reflected upon. (Earlier prototypes and policy issues raised by such "history-enriched digital objects" are outlined in Hill & Hollan, 1994). A teacher will be able to request an aggregate data set on what her students did with a particular exhibit. The class could then reflect back in the classroom on different phenomena they noticed in the exhibit. Researchers and designers may reflect on these results (with appropriate permissions concerning privacy of data), looking at the history of when the exhibit was used, how it was used, and what different classes of visitors did with it.

This possibility to mine the data generated in the "act becomes artifact" cycle is nascent in the other sample WILD applications. But it will become more prominent as classrooms become "persistently WILD." Since classrooms will spend much more time with personal, ready-at-hand WILD applications than they currently do with computers in labs, far more of the students' interactions will be captured on devices (and servers that aggregate the information). Further, the classroom communications networks can be instrumented to track information exchanges, so that patterns of exchanges can be examined. All electronically-mediated or "e-interactions" could be tagged with values for a broad range of parameters, including facts like time-stamp, user identity, institutional demographics, and response characteristics, but also user profile characteristics explicitly defined or tacitly inferred. The value of those "etransactions" can be mined as to their properties in context and concomitant results. Once the WILD conversational acts are captured and indexed in the flow of networked message transactions, the teacher and learners themselves may reflect on the patterns of their interactions.

Finally, the actual "workflows" required by a CSCL activity, such as a jigsaw classroom, can be directly enacted on the devices, so that the topology of the network and devices matches the conceptual topology of problem-solving roles and knowledge exchanges (thus an artifact, the curriculum, becomes more directly enactable).

These "act becomes artifact" possibilities will create significantly new CSCL research opportunities for applying computational data-mining methodologies (such as those used in bio-informatics, [START_REF] Witten | Data Mining[END_REF], to unearth patterns of relationships between instructional transactions and learning outcomes. With conventional textbook curricula, researchers make a distinction between the bought curriculum (textbook), the teacher's planned curriculum, the taught curriculum (what is enacted in the classroom), and the learned curriculum. It is now very hard to get statistical data on more than the bought curricula, because of the difficulty of tracking what actually happens in the classroom; it is even harder to track what students learn in a fine-grained way. If WILD applications make CSCL activities more directly enactable in an instrumented networked classroom, it will become much more possible to track the taught curriculum. Further, if ClassTalWImageMap formative assessment techniques are easy to give as quick, take-the-pulse quizzes, information will be generated about the learned curriculum. Mining the correlations among the bought, planned, taught, and learned curriculum could create a very powerful research process for curriculum improvement. Yet these prospects have "big brother" like overtones of continuous surveillance. Much nuanced work will be necessary on privacy and security policies and safeguards so mining of the actbecomes-artifact cycle is devoted to services that help learners.

Augmented activity spaces emerge

We have suggested five WILD application affordances already illustrated by early handheld CSCL applications: (1) augmenting physical space; (2) leveraging topological space, of two distinct kinds; (3) aggregating coherently across all students' individual contributions; (4) conducting classroom performances; and (5) "act becomes artifact." Looking for the larger pattern in these directions, we see WILD-based CSCL leading to considerably different CSCL application types than those of the desktop: those more grounded in physical space, about spatial relationships, simultaneously engaging whole classrooms, and encouraging a "conductor" metaphor for teaching more than one of "guide-on-the-side." Overall WILD-based CSCL seems headed towards augmented activity spaces.

These are early impressions, of course, and as WILD applications develop, new directions may become evident. In any event, the major point of our argument holds -the differing physical capabilities of personal, pah-sized computers and either wireless local-area networks or mobile ad-hoc networks create differing application-level affordances, which creates quite different potentials for CSCL. (For more on mobile ad-hoc networks, see http://www.ietf.org/ html.charters/manet-charter.htm1.) Moreover, given how compelling handhelds are likely to be in the next few years, compared to bulky, expensive, complex desktop computers, we can expect that these differing application affordances will become very significant for the majority of innovators exploring K-12 learning situations. Today's archetypal CSCL applications include: Distance Learning: participation in a shared, possibly immersive, virtual space that mimics some characteristics of real learning spaces, e.g., a virtual campus and offices for teacher professional development organizations and participants using MOO technology (TAPPED IN: Schank et a].,] 999). Single Display Group Ware: Side by side use of a shared, large display by a group of 2-4 students and (intermittently) a teacher (e.g., Dynagrams [START_REF] Pea | Augmenting the discourse of learning with computer-based learning environments[END_REF][START_REF] Stewart | When two Hands are better than one: Enhancing collaboration using single display Groupware[END_REF]. Knowledge Spaces: contribution to a shared conceptual space that organizes individual knowledge elements, such as OISE's CSILE [START_REF] Scardamalia | Computer support for knowledge-building communities[END_REF]. Messaging: writing notes or messages to a partner or discussion forum (e.g., [START_REF] Honey | The mathematics learning forum online: Using telecommunications as a tool for reflective practice[END_REF].

Distance learning will still be a significant issue for universities seeking to broaden their audience to students who cannot readily come to class. But for the largely local and classroom-based K-12 audiences, "virtual spaces" inside tiny palmtop screens will not be compelling compared to the augmented physical spaces they will inhabit. Distance learning will still be interesting in the "act becomes artifact" sense, emphasizing comparative analyses across data from different sites, but not in the "communicating with a distant partner" sense.

However, assuming these devices spread popular instant messaging capabilities, and that these capabilities are active on the devices when students are on the bus, in the cafi, or at home, a new kind of "distance learning" may emerge. After they leave class, teams of students may be able to coordinate ongoing groupwork more closely: they may engage in coordinating schedules, sending each other updated information, asking spontaneous questions of each other, all from various locations in their neighborhood. A common story from European countries is of groups of teenagers talking about messages coming in on cell phones as they sit in a cafk together. New and interesting patterns of CSCL may emerge where "groupwork" engages additional outsiders as a schoolbased group member is messaged while sitting among a non-school-based group. Thus, we speculate that analyses of messaging patterns, uses, and practices (presently a smallish specialty within CSCL) may grow in interest and importance. There are also intriguing prospects in this context for the WILD educational applications ofCOINs (community of interest networks), network services used in the business world to bring together individuals or organizations with common interests, concerns and/or values.

The CSCL thread that studies shared knowledge generated around shared screens (e.g., [START_REF] Dillenbourg | Does a shared screen makea shared solution[END_REF] will change with WILD. Large shared screens will be less common than small personal screens, though a few large, public displays (such as in the ClassTalk application) will likely be very important. [START_REF] Suthers | Towards a systematic study of representational guidance for collaborative learning discourse[END_REF] highlights the problem of following the "same" conceptual object as it moves around different displays. This problem will, we expect, become more prominent: how will students track "their" contribution as it gets beamed around to different displays, with differing representational characteristics, and amid derivative works? Further, among CSCL issues, it may be that the maintenance of shared attention will be more problematic with smaller screens, while the problems of negotiating control of a single mouse may be less problematic. In general, CSCL issues concerning how shared knowledge arises in a classroom with multiple representational devices with different technical characteristics and different user capabilities are likely to be rich.

Finally, we believe the creation of "knowledge spaces" within and across classrooms will have a very distinctive flavor with WILD classrooms. WILD lends itself more to creating knowledge spaces through peer-to-peer and multicast "synchronization" of contributions to the same semantic category than it does to client-server "construction" of contributions in complex, integrated, server-based systems. Frankly, we do not yet know about what peer to peer knowledge sharing systems for CSCL will be like, but chances are they will be more ad hoc, more diverse, more fragmentary, and more decentralized than today's client-server knowledge spaces. Creating appropriate synchronization capabilities among handhelds for classrooms (which we at SRI term "ClassSyncn in contrast to the PalmOS "Hotsync": see Brecht, Chung, & [START_REF] Brecht | CML -The Classsync Modeling Language[END_REF], such that knowledge spaces thrive, will be an interesting research area.

Speculations o n new research directions

In closing, we envision research directions for CSCL in a future WILD age. [START_REF] Koschmann | Paradigm shifts and instructional technology: An introduction[END_REF] notes that educational technologies have evolved through the paradigms of CAI, ITS, Logo-as-Latin, and CSCL. The paradigms can be organized as two dialectic pairs of forces. Early debates focused on the relation-~l t ) of student to computer: computer-controlling-student (CAI) vs. studentcontrolling-computer (Logo). This split was recast as the choice of conlputer as tutor (CAI), tutee (Logo), or tool [START_REF] Taylor | Introduction[END_REF]. Later, the debate split on the role of cognitive representations in educational technology (Lajoie & Derry, 1993) with an ITS camp emphasizing information-processing-model-based interventions to trace student cognition and compare it to normative models, another emphasizing computer-based models and their representations as a semiotic intervention mediating CSCL discourse among students and teachers, emphasizing contributions of socio-cultural theories of learning (e.g., Vygotsky, Leont'ev, and Rogoff).

We find that the control (tutor, tutee, tool) and representational issues (modeling the learner vs. mediating learner conversations) are insufficiently rich to organize the interesting R&D debates. We speculate that an interesting debate will form around the kinds of system cotlplings [START_REF] Morrison | New actors, new connections: The role of local information infrastructures in school reform[END_REF] among the information in different distributed devices, and critical theory discourse around power relationships in schooling contexts [START_REF] Apple | Computers in schools: Salvation or social disease?[END_REF][START_REF] Segal | The American ideology of technological progress: Historical perspectives[END_REF]. Overly tight coupling, where every information exchange among personal devices is centrally controllable and tracked, may be too close to Orwellian scenarios. Overly loose coupling, where each Palm is an information island, will not lead to interesting shared knowledge spaces and activity artifacts. The kinds of coupling needed may also diversify with different pedagogical strategies and activity designs. Some CSCL researchers have been turning to Activity Theory as fertile ground for design theory (e.g., Gifford & Enyedy, 1999), an approach that has attracted attention for CHI design generally (Bodker, 1991;[START_REF] Nardi | Context nnd Consciousness: Activity Theory and Hurnan-Cot~~puter Irlternctiorr[END_REF]. In an activity theoretic perspective, activity occurs within the framework of an objective and a community of other users, in which rules and roles affect participants' behaviors, and in which the outcome can become another activity or artifact. While not necessarily committing to the different aspects ofthe social theory that guides such work, we find it useful for articulating different kinds of systemic coupling that may become important for CSCL. Activity Theory is a methodological framework with a core representation being the diagram displayed below (Figure 1, adapted from [START_REF] Nardi | Context nnd Consciousness: Activity Theory and Hurnan-Cot~~puter Irlternctiorr[END_REF].

Others

Figure 1. Activity Theory (adapted from [START_REF] Nardi | Context nnd Consciousness: Activity Theory and Hurnan-Cot~~puter Irlternctiorr[END_REF] The tutor, tutee, tool debate, as well as the representation debate, have largely focused on the topmost agent-tool-objective relationship of the diagram.

Tutor: Computer is the agent, student problem-solving behavior is the objective (goal), model tracing is the tool Tutee: Student is the agent, a computer program written by the student is the objective, microworlds are the tools Tool: Student is the agent, computer is the semiotic tool, shared knowledge is the object With WILD, other parts of the framework become important system couplings. For example, "rules" and "roles" become important categories of coupling in the distributed system, especially rules that protect privacy, but also privilegelike rules about "roles" that define capabilities one is enabled to have with one's A walk on the WILD side 163 device in specific situations, such as rules about who can make or take what kind of contribution to or from a knowledge synchronization system. This generative nesting is fertile for inventing new pedagogical activities in WILD settings; the coupling of the output of one activity to the next sequential activity, or within a hierarchical framework of activity becomes interesting. Further, "division of labor" becomes an interesting category of coupling, as students may choose to divide up multiple representations among multiple devices, to provide a larger overall screen space. Thus rules and roles interact.

In the past, debates focused on the control issue (tutor, tool, or tutee) or the representation issue (model tracing inventions vs. semiotic inventions). Whereas these clashes may continue in educational technology now, as far as WILD classrooms go, only the tool and semiotic perspectives make a good fit. We see little evidence that students want to be "tutored" by their personal devices, and while they may tweak parameters in simulations, or do constructionist activities with them, it is unlikely that Logo-as-Latin will be the primary paradigm, with students spending most of their time WILD programming. Moreover, while there will be many interesting uses of intelligent modeling in the data mining1 act-becomes-artifact sense, the low power of palmtops makes embedded intelligent model tracing unlikely. WILD is a much better fit for semiotic intervention with new forms of modeling and representation.

Going beyond these historical clashes over control and uses of representation, WILD will differ from traditional CSCL applications by creating a more distributed systems peer-to-peer network topology. The kinds of coupling and regulation of those couplings in such a system should be fertile ground for future innovation and controversy. Finally, Lemke's distinction between topological/typological representational systems will find new purchase in WILD activities, and there is much to explore in the prospects of geospatial and semiospatial representational systems for augmenting the physical spaces in which learning, teaching, and communication more broadly occur.

Notes

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Authors' addresses

About the alithors

Dr. Jeremy Roschelle has an extensive background in educational software design, math and science education, collaborative learning, and video analysis methodology. At MIT, he designed and implemented the graphics subsystem for Boxer, a successor to the Logo programming language. In his Berkeley Ph.D, he analyzed cognitive learning processes by which students develop mental models of velocity and acceleration vectors, and authored a simulation called "The Envisioning Machine" that utilized dynamic graphics and multiple representation. In his work at the Institute for Research on Learning, he investigated the conversational processes which enabled collaborating learners to build shared understandings of scientific concepts. Dr. Roschelle continues to serve as a co-PI for SimCalc, leading the design of simulations that den~ocratize access to the mathematics of change. Recently, he has focused on interoperable, reusable comp;,:~rnts for n x t h learning; design of digital libraries for mathematical applets; and networked, handheld learning devices.


Roy D Pea
email: roypea@stanford.edu

J C R Licklider

Robert Taylor

Barry Fishman

Douglas Gordin

Sam Kwon

Steven Mcgee

Kevin O'neill

Joseph Polman

Learning Science through Collaborative Visualization over the Internet 1

refers to development of scientific knowledge that is mediated by scientific visualization tools in a collaborative learning context.

Introduction

How will learning take place as we stride into the third millennium? I believe we should emphasize new models for learning that go beyond the antiquated conceptions of education found in most teacher-centered, formally organized classrooms. Reforms seeking to improve education would productively focus on understanding and supporting learning in ways that pay close attention to the skills of social interaction and participation in new activities that currently constitute lifelong learning in working and living communities. In contrast to learning-before-doing-the model of most educational settings-I would urge attention to learning-in-doing, a model where learners are increasingly involved in the authentic practices of communities through learning conversations and activities involving expert practitioners, educators and peers. Learning-in-doing requires interactions between groups that traditionally have been separated by the institutional boundaries of such institutions as school, work, university, and home. Scientific and business workgroup practices using Internet and highbandwidth services recognize that not all partners necessary to an interaction can be colocated-and education needs to exploit the same insight.

A convergence of socio-technical developments has led to early successes and enthusiasm about broad-scale potentials for improving learning, education and schooling in these ways. These developments coalesce in the emergence of "virtual learning environments" or "online learning communities." Virtual learning environments may be constituted within or across classrooms or campuses, within or between businesses (or homes), or involving participants in heterogeneous settings, such as school-home-community, or school-workplace-university. By collapsing spatio-temporal barriers through the construction of virtual learning communities, we may enable greater intimacy and authenticity in the learning process. Such technologies can be used to facilitate the return of learning models that existed with considerable success prior to formal schooling, such as apprenticeship, long-term mentoring, and collaborative groups that learn through work on projects. Expertise that is now geographically dispersed and isolated will become increasingly interconnected to the benefit of lifelong learning. Virtual museums, the emphasis of this workshop, offer yet another context for the consideration of virtual learning environment design and research.

The primary converging trends leading to virtual learning communities have been:

(1) the movement toward more socially-situated conceptions of learning, toward viewing intelligence as a distributed achievement rather than as a property of individual minds; (2) the advent and rapid growth of Internet use in support of both informal learning and more formal learning communities; and (3) rethinking appropriate roles for the teacher in effective learning environments. Together, these trends may well have synergistic effects far greater than any of them could individually for deepening learner understanding and rendering learning more relevant for enabling the learners' participation in cultural practices. Participation in cultural practices is, after all, the kind of "transfer" outcome from one's involvement in learning activities that we ultimately seek. Acquisition of knowledge per se is subsidiary to this aim.

Information technologies play multiple roles in these socio-technical developments in serving: (1) As a meta-representational substrate-in which authors create media-rich documents using symbol systems such as language, diagrams, video, audio, mathematical notations, and scientific or data visualizations, and make conceptual linkages between documents or document components (as in hypermedia) in ways that were awkward or impossible in noncomputational systems like print [START_REF] Pea | Distributed multimedia learning environments: Why and how? Interactive Learning Environments[END_REF].

(2) As a communication channel for establishing highly interactive conversations among individuals distributed across space and time [START_REF] Pea | Seeing what we build together: Distributed multimedia learning environments for transformative communications[END_REF]. These communications in "media spaces" may transcend in some respects what is possible in face-to-face encounters (sometimes characterized as 'beyond being there'), e.g., when the groupware in support of them allows for joint activities such as computer-assisted design in the production of a common artifact.

(3) As interface to individual, group, and cultural memories-archives of information, knowledge, and representations of past activities that can be accessed, drawn upon, and extended as needed when new problems arise, or when reflection leads to new insights. (4) To establish spaces and places in which individuals come together in real-time (synchronously) or at different times (asynchronously) to collectively engage in some activity together (e.g., [START_REF]Designing virtual communities in the service of learning[END_REF][START_REF] Harrison | Re-place-ing space: The roles of place and space in collaborative systems[END_REF]. These social activities are mediated by informational representations of space (e.g., a graphical city comprised of buildings incorporating rooms in which individuals throughout the globe may convene through their computers to have meetings, discuss issues, learn, play, and socialize). The persistence of persons, documents, and rooms for meeting in cyberspace is being exploited in multi-user virtual environments, or "virtual worlds." [START_REF] Cerf | National Collaboratories: Applying information technology for scientific research[END_REF] As cognitive tools for augmenting human performance in complex tasks and for learning 2 . Some theorists of mind view the concept of "distributed intelligence" (DI) as crucial for conceptualizing learning and education (e.g., [START_REF] Cole | A cultural-historical approach to distributed cognition[END_REF][START_REF] Pea | Practices of distributed intelligence and designs for education[END_REF][START_REF] Perkins | Person-plus: A distributed view of thinking and learning[END_REF]. In this perspective, the main sense of DI emerges from the image of people-in-action, and in their activity, we see the configuring of distributed intelligence. Activity is enabled by intelligence, but that intelligence is distributed across people, environments, and situations, rather than being viewed as a resident possession of the individual embodied mind. Intelligence is accomplished rather than possessed; the accomplishments of human activity routinely engage not only an individual's memories but also the intelligence embodied in the design of artifacts and in the social resources often available in situations. As I noted in my chapter on distributed intelligence and education: "There are both social and material dimensions of this distribution. The social distribution of intelligence comes from its construction in activities such as the guided participation in joint action common to parent-child interaction or apprenticeship, or through people's collaborative efforts to achieve shared aims. The material distribution of intelligence originates in the situated invention of uses of aspects of the environment or the exploitation of the affordances of designed artifacts, either of which may contribute to supporting the achievement of an activity's purpose" (Pea, 32, p. 50). 2 Early and influential expressions of this perspective were provided by Licklider [START_REF] Licklider | Man-computer symbiosis[END_REF], and by Engelbart [START_REF] Engelbart | A conceptual framework for augmenting man's intellect[END_REF] in his seminal work at SRI International. These efforts lead eventually to the "personal computer" [START_REF] Kay | Microelectronics and the personal computer[END_REF]. See Rheingold [START_REF] Rheingold | Tools for thought[END_REF] for a fine history of computers as "tools for thought"; see Pea [START_REF] Pea | Beyond amplification: Using computers to reorganize human mental functioning[END_REF][START_REF] Pea | Integrating human and computer intelligence[END_REF] for socio-historical and developmental considerations.

With the explosive growth of the Internet, now connecting hundreds of millions of people around the globe, the relevance of distributed intelligence to the design of interactive learning environments has become much clearer than it was in 1993. And its implications have become all the more problematic, for those who think of designing learning activities and assessing learning outcomes in education as tool-free, individual cognition. Throughout the world, virtual learning communities are being established and growing in shape to fit the ecological niches found necessary to their members' needs. They may be as small as a few people scattered over the net in chat rooms devoted to discussing a mutual reading or a prospective stock investment. Or they may be as large as thousands of individuals coupled together in a virtual fieldtrip, exploring some part of the globe through teleconferencing video connections or updated datasets and commentaries that together establish a knowledge-building community about a learning topic such as global warming [START_REF] Gordin | Student conference on global warming: A collaborative network-supported ecologically hierarchic geosciences curriculum[END_REF]. Moreover, they are vast as the 50 million E-Bay buyers, the largest online global marketplace.

In this paper, I will describe the CoVis project that exemplifies the application of distributed intelligence to problems of designing learning environments. I co-directed the Learning through Collaborative Visualization (CoVis) Project, with my colleague Professor Louis Gomez of Northwestern University from 1992-1997, and have been involved in collaboration and consulting capacity after that era with its newer developments, especially with Professor Daniel Edelson on WorldWatcher. CoVis provides a broad range of lessons for how distributed intelligence may be effectively supported through the uses of learner-centered collaboration technologies and scientific visualization tools.

Virtual learning communities

First, I should say more about the concept of "virtual learning community." Virtual learning communities (VLCs) are first distinguished by their "virtuality"-face-to-face teaching instructions or informal learning activities are actual, not virtual. What makes the learning communities "virtual" is that they are mediated over computers and using computer networks. The applications involved in the communications among participants in VLCs may be as wideranging as electronic mail, newsgroups, chat rooms, listservs, remote screen-sharing collaborative work applications, and multi-user virtual environments.

How about the concept of "learning"? For a socio-technical arrangement to constitute a "virtual learning community," its membership should explicitly express "learning" as an objective of activity participation over the net. Otherwise, any telecommunications-involving activity would be a VLC, since many of us would argue that most any activity in which one engages may correlate with learning of some kind, even if only tacitly.

Finally-and most problematically-is the notion of "community." What makes one? Certainly each of us belongs to many communities, is peripheral to others, and does not belong to yet many others. Yet, we cannot be too thrilled that the concept is difficult for sociologists, anthropologists, and other social scientists concerned with social phenomena. The core sense of a community, for our purposes, is a collective of participants with significant persistence whose members align around some common activities. Communities do things, of virtually infinite variety-they may learn, advance knowledge, collect things, provide help to one another, read books, do crossword puzzles, design jets, watch sporting events together. In addition, typically, such communities are comprised of participants who tend to have access to encounters with one another. Information technologies change the ways in which such communities can be formed and sustained in their activities [START_REF]Designing virtual communities in the service of learning[END_REF][START_REF] Kim | Community building on the web[END_REF]. Let us now consider the design of CoVis as virtual learning community.

The CoVis Project: Learning through Collaborative Visualization

How can the perspective of distributed intelligence be used to guide the design of virtual learning communities for science? How can we use the different affordances of information technologies to support the material and social distribution of intelligence that will better enable the accomplishments of science learning?

In 1992, with NSF funding 1 and support from industry partners, my colleague Louis Gomez and I created the Learning through Collaborative Visualization (CoVis) Project [START_REF] Pea | Distributed multi-media learning environments: The collaborative visualization project[END_REF]. We had a vision for science educational reform through distributed science learning -taking cognitive, social, technological, and scientific breakthroughs into account-which we sought to implement and then empirically study as a community appropriates and evolves its uses. Our development of the CoVis learning environment and its components was guided by a studentquestion-centered, collaboration-focused pedagogy, and our commitment that classroom science learning should more closely resemble the open-ended, inquiry-based approach of science practice. As we put it in our January 1992 NSF proposal: "We believe that science learning environments should look and act more like the collaborative, connected work environments of scientists. To this end, teachers and students need ways to reduce the complexity of getting access to resources that are inaccessible locally. These resources include human expertise in the form of other teachers, scientists and graduate students in business, industry, research settings, and other learners. They also include tools, instrumentation, hands-on materials and labs, museum exhibits, and computing and telecommunications infrastructures...

[We] aim: To examine how geographically dispersed teachers, students, and collaborators can integrally and readily use advanced information technologies to facilitate the types of collaboration and communication demanded by project-enhanced science learning. We argue that applications of advanced technologies provide American educators with critical levers for promoting cognitive apprenticeships in science learning. We aim to build the next generation of infrastructure for new forms of science learning and teaching, and create a national model for the kinds of distributed multimedia science learning environments supportable with the future NREN [National Research and Education Network, later called the NII, or National Information Infrastructure].... We argue that these new [High Performance Computing and Communications] technologies can provide the backbone for the transitioning process from didactic science teaching to cognitive apprenticeships in project-enhanced science learning.... Students need to learn and do science in context of real problems and with sophisticated tools."

After its first five years of invention, empirical studies of use patterns, and design iterations, CoVis came to provide a testbed "collaboratory" [START_REF] Cerf | National Collaboratories: Applying information technology for scientific research[END_REF][START_REF]Towards a National Collaboratory: Report of an Invitational Workshop at the Rockefeller University[END_REF] for learning science by doing science over the Internet-that directly worked with over 100 teachers and thousands of students in 54 middle and high schools in 11 states, with thousands of other teachers using CoVis resources. CoVis schools were connected with high-speed Internet access (commonly T-1) that we used as a critical leveraging technology to develop and evolve through use a wide array of new resources for learning. We provided teachers and students with a software suite of learnercentered tools for doing project-based collaborative learning. Student projects often involved pursuing inquiries using scientific visualization of datasets in the atmospheric sciences and geosciences, and use of a full set of Internet communication tools including e-mail, Usenet news, Gopher, Web browsers, and desktop video-conferencing, that enabled new kinds of learning relationships. Participating classrooms typically had six or more desktop computers, and students had individual net accounts. Our ambitious aim was integral use of high performance computing and communications tools for project-enhanced science learning.

Our assessment of learning and teaching needs led us to project-enhanced science (39) as a fundamental pedagogy for achieving deeper learner understanding and distributed intelligence among the science learning community. This approach to science education was based upon several contemporary research strands in the cognitive sciences of learning [START_REF] Bransford | How people learn: Brain, mind, experience and school[END_REF]. Chief among these is the idea that, long before and outside of formal education, people have learned through participation in "communities of practice" (e.g., [START_REF] Lave | Situated learning: Legitimate peripheral participation[END_REF][START_REF] Wenger | Communities of practice: Learning, meaning, and identity[END_REF]. In contrast to the common "course delivery" models of instruction and distance learning, the advantage of such communities is that learning is situated with respect to community-based goals and activities in which knowledge is developed and used [START_REF] Brown | Situated cognition and the culture of learning[END_REF][START_REF] Pea | Augmenting the discourse of learning with computer-based learning environments[END_REF][START_REF] Songer | Exploring learning opportunities in coordinated network-enhanced classrooms: A case study of Kids as Global Scientists[END_REF]. In the classroom, this benefit may take the form of "cognitive apprenticeship" (8), with students guided, both by their teachers and by remote mentors, to think about science in many of the fundamental ways that scientists do. In designing CoVis learning environments, we acknowledged the large gaps between students and scientists in motivation, purposes, knowledge, and physical distance. The point of science education is not to take the "little scientist" that Piaget said was in every child and create an actual scientist. Heeding these gaps, we sought to design settings in which students could engage as "legitimate peripheral participants" in "communities of practice" for science.

In the community of science practice, the computational tools used-such as scientific visualization and modeling environments-as well as the data collected for investigations are critical to the scientists' abilities to formulate, think about, and work on their problems. In designing the CoVis testbed, we sought to take advantage of this reliance by providing students with learning-appropriate ways to access the same real-time weather data and globally gridded archival earth sciences and environmental data that the scientists study, using tools that we modeled on those used by scientists [START_REF] Edelson | Addressing the challenges of inquiry-based learning through technology and curriculum design[END_REF][START_REF] Gordin | The Greenhouse effect visualizer: A tool for the science classroom[END_REF][START_REF] Gordin | The Climate Visualizer: Sense-making through scientific visualization[END_REF][START_REF] Hall | An Online Hybrid Multimedia Environment for K-12 Education in the Atmospheric Sciences[END_REF]. Scaffolding access to these tools and the scientists and other professionals that use them is a central design technique we used to "seed" a sense of legitimate peripheral participation for students in the CoVis learning testbed. There are other recent efforts to bridge the science education-scientific practice "gap," including the GLOBE Project, Global Lab, GREEN, Earthwatch, Kids as Global Scientists, Hands-On Universe, and BioKids, which have developed related models, of what have been called "student-scientist" partnerships [START_REF] Cohen | Internet links for science education[END_REF][START_REF] Songer | Exploring learning opportunities in coordinated network-enhanced classrooms: A case study of Kids as Global Scientists[END_REF].

A crucial resource that made broad scale CoVis participation in our testbed work out was the ongoing nature of professional development for teachers, supported in the mid-90's by our CoVis Geosciences Web Server (http://www.covis.nwu.edu/), one of the first educational web projects, with a focus on ironing out the details of effective engagement for learners in projectoriented pedagogy. This focus made clear how important "fingertip effects" are for teachers' appropriation of innovations such as CoVis, and as I will describe, we created "model" learning activities for interschool project collaborations in which students' learning can be guided. I will highlight some of the technical features and social affordances of designs that made more fingertip-accessible such new classroom activities as collaborative knowledge inquiry, telementoring, and expressive and interpretive uses of scientific visualizations as a representational vehicle for sense making in science.

CoVis has taken a design-intensive and iterative re-design approach to developing the learning and teaching software, curriculum activities, and participating groups (schools, education researchers, content experts and telementors, science educators and science education institutions) that became part of the "CoVis testbed" [START_REF] Edelson | Addressing the challenges of inquiry-based learning through technology and curriculum design[END_REF][START_REF] Gomez | The CoVis Project: Building a large scale science education testbed[END_REF][START_REF] Pea | Science education as a driver of cyberspace technology development[END_REF]. It is challenging to briefly describe this process of mutual influence among researchers, technology specialists, and educators in our work, but overall, we shifted from building new tools and resources with participatory design advice from educators, to learning more about their situations of technology use so that we could co-evolve activity structures in which sustained use of telecommunications in support of project-based science learning would become commonplace.

Because much of the effort in establishing a distributed science learning "collaboratory" has required technology development and integration, it is not surprising that much emphasis has been on the "material" aspects of distributed intelligence, as embodied in the affordances of the properties of learner-centered scientific visualization software (e.g., [START_REF] Edelson | Addressing the challenges of inquiry-based learning through technology and curriculum design[END_REF][START_REF] Gordin | Prospects for scientific visualization as an educational technology[END_REF][START_REF] Gordin | The Climate Visualizer: Sense-making through scientific visualization[END_REF], and groupware [START_REF] Edelson | Constructivism in the collaboratory[END_REF][START_REF] Edelson | The Collaboratory Notebook[END_REF], datasets, and other media designed for learner and teacher use. The following table provides a few illustrations for how design of material aspects of the CoVis distributed intelligence (DI) systems in which learners and others now participate has fostered, and in many cases, enabled, the kinds of activities central to our vision of distributed science learning.

Material aspects of DI used in design

Social aspects of DI that are enabled

(1) Time-shifting communications (e.g., email, listservs, newsgroups, web site annotations).

Facilitates broad access to distributed group inquiry;

Enables collaboration across global time zones.

(2) Space-collapsing communications.

Facilitates broad access to distributed group inquiry;

Enables virtual field trips to remote places.

(3) "Semantically typed" hypermedia links in our CoVis Collaboratory Notebook (e.g., Question, Conjecture, Evidence For, Evidence Against).

More readily-achieved structured scientific inquiry;

Simplifies tracking learners' questioning and project inquiry processes that are central to constructivist pedagogy, and navigating the information products of a group's knowledge-building project.

(4) Archival memory for communication records.

Creates 'living' database of knowledge building community;

Builds persistent and re-usable database of scientists and other mentors for distributed learning communities.

(5) "Scaffolding" front-ends to scientific visualization tools (e.g. geographic context map overlay, semantic units, and semantic constraints on operators for manipulating visualizations).

By starting with qualitative inquiries, enables broad learner access to complex science topics and systems;

By making scientists' tacit knowledge explicit in the tool interface, makes it possible for novice learners to pursue their open-ended research questions (e.g. weather prediction; causal patterns affiliated with global warming).

(6) CoVis telementoring database (e.g., forms for mentors to "profile expertise" and for teachers to search for it; automatic message handling for project participants, mentors, teachers)

Facilitates remote mentor participation and teacher identification of appropriate mentors.

In our initial work in developing distributed intelligent systems for science learning, we thus centered on creating tools and resources for learners. Since 1995, a great deal of our attention and effort shifted to the "social" aspects of distributed intelligence that are required to launch and sustain these efforts to reform science education toward a learner-centered, project-based pedagogy. Driven by teacher needs and a massive scale-up from 6 to over 100 teachers in geographically diverse sites from 1993 to 1995, we focused on creating activity structures that contextualized the uses of tools and resources by "seeding" the CoVis GeoSciences Web Server (http://www.covis.nwu.edu/geosciences/) with distributed science learning activities, providing models for teachers to appropriate and adapt to their local circumstances [START_REF] Gomez | The CoVis Project: Building a large scale science education testbed[END_REF]. We called our sets of activity structures around a common topic "CIAs" or CoVis Interschool Activities (http://www.covis.nwu.edu/ geosciences/activities). They included multi-week resources on the topics of global climate change, water quality, land use management, weather prediction, and soil science. The World Wide Web was the primary medium through which these activities were shared with teachers, many of whom helped design them in face-to-face summer workshops and refine them in the context of their ongoing teaching. CoVis teachers using these activities formed communities of interest through listservs, to guide and support one another as they sought to interpret and extend the CoVis vision in new ways.

These predictably scheduled interschool activities help teachers think concretely about different aspects of project science, educational telecommunications, and collaboration, because each involve opportunities for telementoring, student collaborative learning, uses of the many Internet communication tools we have described, and such CoVis web resources as scientific visualization environments, datasets, collaborative support, and sample assessment rubrics. These activities provide a curriculum project "shell" or activity structure within which teachers can create specific projects that can be done across classrooms or within a classroom. Our central design challenge has been creating activities so as to provide enough structure so they may be feasibly conducted in a classroom context with limited time and workable classroom procedures, while also fostering enough freedom in the intellectual environment so that learners' questions may be developed as novel project directions [START_REF] Polman | Designing project-based science: Connecting learners through guided inquiry[END_REF].

Characterizing our aims and achievements in the design and implementation of scientific visualization environments for middle and high school learning in the CoVis Project and beyond helps make concrete what this work has been like.

The WorldWatcher Global Visualization Environment for Science Education 3

Scientific visualization has dramatically changed scientists' practices in recent years by "using vision to think," exploiting human visual pattern perception for complex investigations in large datasets (e.g., [START_REF] Brodie | Scientific visualization[END_REF][START_REF] Cerf | National Collaboratories: Applying information technology for scientific research[END_REF][START_REF] Nielson | Scientific visualization: Overviews, methodologies and techniques[END_REF][START_REF] Kim | Community building on the web[END_REF]. Scientific visualization provides an image rendered through high-speed computer graphics that is based on a numerical dataset that describes some quantity in the world (e.g., global temperatures). Our goal in developing scientific visualization environments for pre-college education was to investigate the socio-technical conditions under which scientific visualization, integrated into inquiry-based learning activities, could enable students of diverse abilities (not only the most motivated and talented) to develop an understanding of complex scientific phenomena such as climate, weather and global climate change. We say "socio-technical" conditions because key components of the effective use of learning technologies include software functions that are readily learnable and usable-but also teacher-guided activities that are part of the social surround of technology use. 3 For the WorldWatcher Project initiated by Daniel Edelson and Roy Pea at Northwestern University as a development of the CoVis Project (and also supported by NSF Grant #9453715), we thank Co-PI Louis Gomez, lead programmers Douglas Gordin, Joey Gray, Brian Clark and Eric Russell, and the contributions of many others, including Eddy Ameen, Tajuana Bates, Jonathan Berkowitz, Matthew Brandyberry, Matthew Brown, Kylene Chinsio, Elaine Coleman, Christopher DiGiano, Jason Gandhi, Duane Griffin, Eric Hanson, Ben Johnson, Steve Juh, Ryan Kowalczyk, Danielle Lessovitz, Ray Liu, Adam Murphy, Kathleen Schwille, Michael Smith, Michael Taber, Adam Tarnoff, Maya Wallace, Viktoria Wang, and Eshanthika Wijesinha. WorldWatcher research was supported by NSF grants #9453715 and #972068, by a SMETE Postdoctoral Fellowship (#9714534), and by Sun Microsystems, ESRI, and Precision Farming Enterprises.

Our early investigations in CoVis revealed the need for specific supportive structures in a scientific visualization software architecture that can provide what learners need [START_REF] Gordin | The Greenhouse effect visualizer: A tool for the science classroom[END_REF][START_REF] Gordin | Prospects for scientific visualization as an educational technology[END_REF][START_REF] Gordin | The Climate Visualizer: Sense-making through scientific visualization[END_REF]. Unfortunately, the tools and techniques scientists use presuppose significant prior knowledge in the canons of scientific inquiry, data exploration and systematic inquiry, and in strategies for search, analysis, and transformation of data using the capabilities of scientific visualization software programs. Figure 1 exemplifies a scientific visualization environment developed for scientists, not science learners. WorldWatcher is a scientific visualization environment designed primarily for supporting student investigations of two-dimensional, gridded scientific data, and based on the ClimateWatcher software we first released in April 1996 [START_REF] Edelson | Addressing the challenges of inquiry-based learning through technology and curriculum design[END_REF]. WorldWatcher built upon ClimateWatcher's aim to provide an accessible and supportive environment for students to explore, interpret, and analyze scientific data about the transfer of energy through the earthatmosphere system and climate change in ways akin to scientists' inquiry activities. Data is distributed with WorldWatcher in data libraries that support educational activities centered around specific datasets, as used in interpretive, analytic and expressive visualization activities (http://www.worldwatcher.nwu.edu/).

Users of WorldWatcher can now create dynamic color visualizations of many different data types beyond those initial topics (from NASA and other public domain sources), and can import their own geo-gridded, as well as point datasets (using a standard spreadsheet format). WorldWatcher datasets for global climate change (insolation, albedo, absorbed/reflected solar energy, surface temperature, greenhouse effect/increase, outgoing long-wave radiation, net energy balance) are complemented by human and physical geography data to allow students to examine the causes and implications of climate change. Global climate data on precipitation and relative humidity are provided, as are physical geography data on elevation/bathymetry, soil type, dominant vegetation and ground cover, and plant energy absorption (FPAR). Students can also access global information on population magnitude and density, carbon emissions, and national boundaries.

Student access to the collections of WorldWatcher datasets is supported in two ways: (1) by schematic diagrams that graphically depict relationships among variables-such as The Energy Balance in Figure 2 -to foster students' conceptual development and which can be clicked WorldWatcher enables learners to examine and interpret datasets created by the scientific community and to create their own data using built-in arithmetic operations and climate models. WorldWatcher offers display features of visualization environments designed for scientific researchers, such as depicting 2-D global data in the form of false-color maps, but to provide geographical context for learners, these maps are displayed with latitude-longitude markings and an optional continent outline overlay (Figure 3). When a user interacts with the WorldWatcher visualization, a continually updating readout tracks the user's mouse as it moves over an image and displays current latitude, longitude, country or state/province, and data values. Users tailor visualizations by modifying color-scheme, mapping of colors to numerical values, spatial resolution, and magnification. WorldWatcher provides statistical summaries for user-selected regions4 or whole maps.

Beyond these statistical summaries, WorldWatcher offers unary and binary mathematical operations for mathematical data analysis. Within an image, users can add, subtract, multiply, or divide all the values in a region or an entire image by a constant. They can normalize values in an image, and use the blackbody equation to convert energy values to temperature and temperature values to energy. WorldWatcher binary operators-enabling users to apply a function at each location in two images-are addition, subtraction, multiplication, division, maximum, minimum, and correlation, with the result displayed as a new visualization. The functions of the WorldWatcher visualization environment allow students to view these data in the form of color maps at a variety of spatial and temporal resolutions, and to animate change over time. The activities are supported by a multimedia database of background and explanatory materials focusing on science topics and scientific visualization techniques.

WorldWatcher offers a simple on-line notebook feature that supports text, diagrams, multimedia, and clickable links to open specific datasets. Teachers are using the notebook to design and disseminate activities, and students are using it to create projects and record their cumulative progress in their projects, because they can annotate their visualizations In today's uses of WorldWatcher advanced by LeTUS, the Center for Learning Technologies in Urban Schools (http://www.letus.org/), there are both middle school and high school curricula available, developed through a research center partnership, led by Northwestern University and involving the University of Michigan and 100 public schools throughout Chicago and Detroit. Scientific visualization is integrated into inquiry-oriented class work with hands-on labs, teamwork, and class discussions to develop learner understanding of both the scientific and social issues affiliated with changes in the environment. For middle school, The Global Warming Project is an eight-week unit centered on scientific factors contributing to the controversial global warming debate. The unit closes with student investigations considering scientific evidence and social concerns as they appraise potential solutions to issues raised by the global warming debate. Looking At The Environment (LATE) is a year long, inquiry-based, visually intensive environmental science curriculum for high school centered on three key issues: [START_REF]Designing virtual communities in the service of learning[END_REF] The relationship between population growth and resource availability; (2) Electricity generation and meeting the demand for energy; and (3) Managing water resources for agricultural use and human consumption.

Conclusion

In the CoVis Project, we had several key objectives:

(1) To demonstrate the feasibility, opportunities, and challenges of establishing a distributed multimedia learning environment-the "CoVis testbed"-in which high school students could learn science by doing science over the Internet, using real scientific data and scientific visualization environments, as well as groupware and desktop videoconferencing tools for collaborative learning and virtual visits to labs, museums and other schools;

(2) To create a collaborative partnership in the development of the CoVis testbed of university researchers in the sciences of learning and learning environment design with a science education museum, a university scientific research group (UIUC/NCSA), multiple public schools, and industry partners;

(3) To investigate in detail how to make a powerfully general framework for scientific visualization in learning and teaching that could adapt scientists' tools, create software tools and curriculum to exploit that framework, and study how teachers and learners could come to use it productively in their learning and teaching activities.

When we began these efforts, the vision of establishing a high-end learning collaboratory for students to learn science by doing science over the Internet, with university-scientist level computing and communications resources, and engaging scientist telementors and science education museums in "virtual visits" was considered improbable by many, and exceptionally costly. Today, we see many of the features of our CoVis vision for science education integrated in the day-to-day learning activities of urban classrooms, among the most challenging settings for educational reform using technologies. While these achievements have been gratifying, we have only begun to explore the transformative roles for advanced computing and communications in supporting learning and teaching that taps the distributed intelligence available, so that anyone, anytime, anywhere, and at any pace, can advance their lifelong learning hopes and dreams.

Figure 1 :

 1 Figure 1: A visualization of surface temperature data for the Northern Hemisphere displayed by Transform, a powerful, general-purpose visualization environment widely used by scientific researchers (circa 1992)

Figure 2 :

 2 Figure 2: The Earth Energy Balance diagram user interface to WorldWatcher datasets through directly to datasets for visualization, or by structured online notebook activities created by the teacher (see below).WorldWatcher enables learners to examine and interpret datasets created by the scientific community and to create their own data using built-in arithmetic operations and climate models. WorldWatcher offers display features of visualization environments designed for scientific researchers, such as depicting 2-D global data in the form of false-color maps, but to provide geographical context for learners, these maps are displayed with latitude-longitude markings and an optional continent outline overlay (Figure3). When a user interacts with the WorldWatcher visualization, a continually updating readout tracks the user's mouse as it moves over an image and displays current latitude, longitude, country or state/province, and data values. Users tailor visualizations by modifying color-scheme, mapping of colors to numerical values, spatial resolution, and magnification. WorldWatcher provides statistical summaries for user-selected regions 4 or whole maps.Beyond these statistical summaries, WorldWatcher offers unary and binary mathematical operations for mathematical data analysis. Within an image, users can add, subtract, multiply, or divide all the values in a region or an entire image by a constant. They can normalize values in an image, and use the blackbody equation to convert energy values to temperature and temperature values to energy. WorldWatcher binary operators-enabling users to apply a function at each location in two images-are addition, subtraction, multiplication, division, maximum, minimum, and correlation, with the result displayed as a new visualization.

Figure 3 :

 3 Figure 3: A WorldWatcher visualization window with key to what is representedFor example, examining the difference between global surface temperature values in January and July (all three visualizations included as Figure4), students can observe the stability of ocean temperature and equatorial lands, in contrast to non-equatorial land masses, and the polar regions especially.

Figure 4 :

 4 Figure 4: Three WorldWatcher visualization windows: Surface temperature for Jan-94, July-94 and the difference between Jan-94 and July-94

Regions can be selected using rectangular and irregular region selection tools, as well as by specifying geographic areas by name (e.g., South America in Figure3), or data values by range (e.g., all areas with temperatures between 60 and 75 degrees F.).

Funded by the National Science Foundation as an advanced networking testbed, our partnership of Northwestern University, Bellcore, Ameritech, the Exploratorium Science Museum in San Francisco, and the University of The project developments were transitioned in 1997 as a The CoVis Project has been funded by National Science Foundation Grants #9253462 and #9454729, Illinois Board of Higher Education Eisenhower grants, and industrial partners Ameritech and Bellcore. We are grateful for hardware and/or software contributions by Aldus, Apple Computer, Farallon Computing, Sony Corporation, Spyglass, and Sun Microsystems. Other supports since the early CoVis era were grants for WorldWatcher (#9720687), the Living Curriculum Project (#9720423) and the extensive curriculum development and urban teacher professional development activities of the LeTUS Center (#9720383).

Nobel Symposium (2002LeTUS Center for Learning Technologies in Urban Schools based at Northwestern and University Chicago and Detroit public school systems how to support urban school teachers in using project-based inquiry as their core approach to teaching earth and environmental sciences at the middle and high school level. Project, I will highlight our design research methodologies for making the powerful features of professional scientific visualization tools usable to students and teachers in WorldWatcher.