WORKSHOP GOALS

Including Stanford faculty and leaders in the field, a design workshop was held on the prospective educational needs of environmental sensor networks that could be used for learning and educational purposes at the pre-college and college levels. The aim was to survey and advance the collective of work going on using sensors and probes in science education at multiple educational levels, in universities, not-for-profit research organizations and in industry. The workshop set the stage for establishing next steps toward defining projects for collaborative work under the Media X Network arrangements with Stanford faculty and in other co-development projects to be defined with Stanford faculty and partners in other organizations. But the report is also open for broad distribution, in hope that other innovators and researchers may be inspired by the visions expressed in these pages.

There is considerable interest in considering the types of sensors described by Omron as possible directions for this effort in sensor networks, including cameras and vision sensing, seismic, NO 2 , CO 2 , pollen count, water quality, suspended particles, habitat monitoring, microclimates, and contaminant flow. Our workshop included brainstorming and scenario development concerning best practices in the field of using sensors and probes in science education. We also considered other sensors than light, touch and temperature that could be used in a programmable sensors/smart bricks kind of approach like that developed by Resnick and Martin (MIT) and Eisenberg (U. Colorado).

The brainstorm and educational scenarios were designed to shed light on: * meeting significant learning needs (important national and state learning standards in K-12), not simply "supplemental activities"; * being tremendously engaging in tapping issues and concerns that learners at these age ranges care about, and where sensor networks could serve important public concerns (e.g., biodiversity, habitat preservation, safety from pollutants in the home or school, water quality, air quality, ozone depletion); * novel uses of sensors or probes in education or have dramatic prospects for reductions in cost, usability, and other factors; * ideas for promising NSF or other grants to study their use in testbeds of schools and assess teacher needs and learning outcomes.

How the workshop relates to a larger program of research

Following this workshop, we would aim to identify as a group the most productive directions for continued Stanford-Omron collaborations in terms of prototype system development and "testbed" work with learners at various levels and in partnership with schools and other institutions. We can foresee substantial concept, scenario, prototype and product development for the sensors and sensor networks viewed as most productive for the science educational applications.

PART 1. Highlights from the morning brainstorm

The challenge of curriculum and standards. A major challenge for the integration of sensing networks into education is the tremendous pressure from the federal government for testing and assessment. Hence, to truly exploit the potential of sensing networks in education, we are faced with the need to build enormous infrastructure for evaluation as programs are being built. Programs without such standards will not be adopted. How can one prove that sensing networks actually produce measurable learning?

One approach to the issue is to ask how we can drive standards by showing that kids can do amazing things with a new kind of curriculum in which sensing networks play an important role. This would redefine the problem to one of assessment, not standards.

The role of informal learning

Another approach to dealing with the seemingly insurmountable problem of standards and the formal curriculum is to develop programs of informal learning. For example, develop toys, games, museum exhibits, and activities for kids in which sensors and networks play a role.

New uses of sensors for simulating visceral experiences

An exciting educational application for sensing technology allows kids to have new kinds of visceral experiences. For example, spherical lenses could be used to simulate what it would be like to have 360-degree panoramic eyes--to see the totality of a room or vista. They can also manipulate space and time scales; for example, example, being able to shift perspectives from being very big to very small or to experience phenomena that change very slowly or quickly.

How to help teachers understand the educational potential of sensors?

There are science standards in elementary school, but a tremendous lack of trained science teachers. How can sensors help provide them with a positive experience in teaching science? Right now many teachers do not think of science as measuring anything. When we think of affecting change in the system, it's important to know whom we are trying to change. The belief is that, when given the right technologies and curriculum, kids can do great things with science.

Networking sensor communities and power issues

An example of a project in progress today is CENS, an NSF Science & Technology Center based at UCLA. CENS is developing embedded networked sensing systems and applying this technology to critical scientific and social applications. The project aims to understand how these large-scale, distributed systems, composed of smart sensors and actuators embedded in the physical world, will eventually infuse the entire world at a physical -versus virtual -level. CENS systems will form a critical infrastructure resource for society -they will monitor and collect information on such diverse subjects as plankton colonies, endangered species, soil and air contaminants, medical patients, buildings, bridges and other man-made structures. Across this wide range of applications, embedded networked sensing systems promise to reveal previously unobservable phenomena. http://cens.ucla.edu/ Technical issues raised by the project include the problem of power supply, i.e., how to let sensors last in the wilderness as well as how to enable communication among the participants gathering the data.

The potential of sensor networks for pre-cognitive science It could be that the most important use of sensing technology is not to teach young kids formal scientific concepts theories but precognitive schemas. This would entail using sensing technology to help kids understand in a visceral way some of the patterns that occur in the world. An example would be the notion that temperature can vary over the room or over your body. Another example would be dropping a ball and getting a visually sense of force and acceleration. A student doesn't get this kind of visceral understanding from just "being in the world" because these are undifferentiated experiences. Educational sensing networks would allow a more targeted experience of the underlying concepts.

Sensing networks and informal learning

How can we create informal programs to harness sensors and networks? For example, how can kids use sensor data to improve the local environment? These types of activities do not have to be lodged in the formal curriculum.

Sensing plus control

We should also be thinking about the technologies for controlling sensing. A simple example is the ability to control lights or a thermostat through a handclap. Other examples in the real world include Napa Valley vineyards that detect cold or hot spots and turn on fans to maintain stable temperatures. The question is how to get teachers to use this stuff in their science teaching. See NASA VINTAGE project at: http://www.vestra.com/nasavintage/background.htm

The question is how to get teachers to use this stuff in their science teaching.

Sensing network, scientific communities and ethics

How about neuro-imaging -little thinking caps that can map activity of your brain during meetings? This kind of technology gets us thinking about new forms of democracy and citizenship. For example, in a world where your sense data are shared, there is the question of common intellectual property, an intellectual commons. At the limit we can ask, Are your physiological states even owned by you? There are new ethical problems raised by the possibility of recording and sharing sense data obtained from group meetings.

Portfolios as a "wedge"

Portfolios hold great promise as a mechanism for integrating sensing technologies in the school system. The basic idea is that an individual student would have a personal digital portfolio -in the form of some kind of electronic database -that would contain a record of projects, courses, grades, etc. A portfolio could "travel" with the student from class to class and over his/her academic career. Also, a student's portfolio can be part of a community portfolio or even a world portfolio that aggregates data from the individual portfolios. A critical feature of portfolios that make use of sensing data is that it contains not just results and data, but evidence of the student's reasoning process. Why did a student obtain a particular result? Or do a specific kind of measurement? Are there scientific sketches or drawings? A big opportunity is to map the reasoning to curriculum standards and assessment.

Tools for extracting meta data

The portfolio vision cannot happen if there are no tools for processing the data that are contained in portfolios. And it is not sufficient to have an all-encompassing metadata scheme for portfolios: the artifacts are too idiosyncratic as are the points of view and the values of all involved. The development of looks that extract metadata from unstructured data and text would be key in developing a national portfolio system.

The importance of arguments

A centrally important idea is that curricula and standards are a means to an end. And that end should be to test the ability of a student to persuade somebody of something. In other words to make a scientific argument or to perform other actions using evidence as warrants for supporting beliefs, in science or social science or in policy. This ability undergirds professional societies, etc. This should be reflected in Educational Testing Service exams.

Hard versus soft sensing

Hard sensing often refers to sensors that touch something in the environment, e.g., sensing heat, light, moisture, etc. Soft sensors use algorithms to detect objects in the environment, e.g., video sensors that could be trained to track particular kinds of animals in a preserve.

Empathetic sensing and the curriculum challenge

There may be a systemic challenge with the use of sensors for "empathetic" sensing. How can we relate these experiences to a set of goals for general education, not just science? There is also a filtering process that needs to occur with teams establishing a finite set of goals or standards; we need a common vocabulary to talk about sensing in order to establish common goals.

Sensing multiplexers and combiners

On the technology side, one key thing that is missing is a decentralized control of sources of sense data. This involves multiplexed networks that can integrate sense data. Other aspects that are missing are what one might call sensor combiners, which detect signals and perform operations such as AND and OR. There are also integrator sensors. A few of these little combiners and how they combine naturally could move logic out into the edges of the sensing networks themselves. For example, you could build sensors for light and sound, but not sensing both light and sound. Also, you could imagine little surface patches consisting of sensors which could combine in a natural spatial way -for example patching together pieces of an image together using algorithms -to form a large sensor integrating information from different image sources.

PART 2. Making SENS Scenarios

Below are the scenarios developed during the afternoon breakout sessions.

Johnny Health Monitor

This scenario addresses the problem of how sensing devices could be used in school and informal settings to help control obesity in the country, not only for people with an obesity problem but for healthy people as well. The system could monitor blood pressure, glucose levels, and so on. It presents a blue-sky vision and does not focus on the technical details.

In this scenario, Johnny wears a health monitor that keeps a long-term record of his physical activity as well as certain properties of the environment in which he lives. In a school setting, lots of kids would be wearing these monitors. They would be Game Boy-like devices that would provide feedback. Each device would also upload the wearer's personal data to a classroom database so that individuals can compare themselves to their peers over time. One could also design specific activities where a student would measure his physiological states for a particular event (e.g., a race).

The health monitor could also be used in emergency situations to alert the school nurse or appropriate medical people that a child needs help. The system could also use kids' individual differences in physical exercise as a way to teach reasoning about data. For example, do sensors reveal differences in physiological responses for boys and girls under different conditions? Other uses include being able to quickly visualize the aggregate data to help kids think about the impact of what they eat. Finally, although there are mobile sensing devices that perform similar functions today (e.g., diabetes monitoring which control an insulin pump), they are not integrated with a network.

Smart Utensils

Related to the first scenario, this idea examines the role of embedded sensors to help kids understand nutrition and be aware of the consequences of their eating habits. For example, how could the sensors be used in the school cafeteria? A smart spoon could help someone identify how many calories are contained in what is about to be eaten: how much fat, how many calories due to fat, and so on. If the food is too hot, the utensil could change color to generate a "don't burn your mouth!" signal. These kinds of utensils could be culturally embracing (e.g., chopsticks). You could also have a do not eat utensil which, when inserted into food, could alert a kid to contents to which they are allergic (e.g., peanuts, coconuts, lactose intolerance). The idea is to bring nutrition and health to the forefront of consciousness by building utensils with embedded sensors.

Comparing Distributed Communities

In this scenario three or more schools are working on common problems or games involving the community as a whole. Take the example of feeling "tired." Using a device like the Johnny Health Monitor described earlier, kids in different communities could be instrumented so that different properties could be sensed such as metabolic rate, respiration, etc. In addition to the quantitative sense data, the kids would have a means of recording qualitative or subjective data. For example, they would be able to press a button whenever they feel tired. Another possibility would to keep an electronic diary. The latter could be almost a blog that is a time-stamped self-report on how the kid is feeling. At the end of each collection period, say every one or two weeks, the data would be uploaded so that the aggregate data could be collated and visualized. This opens up some interesting possibilities. For example, a screen could appear which compares the kid's subjective assessment with the quantitative data. "Here are the times you said you were tired, but the data say otherwise." The kids could also see visualizations of distributions of tiredness data within and between schools. This could trigger discussions across the network of the sources underlying the differences. The next phase would be coming to some consensual decisions about what to do about the tiredness issue. More nap days? More breaks? After the intervention, the students could then see if anything significant had changed over time in either the qualitative or quantitative data? Do they feel less tired as a group or individuals? Are we getting more tired as school this year as compared with last year? The system could afford inquiry for other psychological dimensions, e.g., anxiousness, loneliness.

Ecological Profile Kit

This scenario involves instrumenting communities with 20 or so sensor types that would measure air quality, water quality, traffic patterns, noise pollution levels, etc. Each school site would be responsible for creating an ecological profile for a 5-mile radius around the school based on the data gathered by the 20 different sensors. For example, a traffic monitoring system could be set up to count how many cars pass by a certain point every hour.

Once an ecological profile is obtained for a school, it can then be shared with other schools and used to provoke a discussion about similarities and differences.

One of the most powerful learning opportunities afforded by this technology would be the ability to compare data across communities and get feedback. This could also be used to track the effects of intervention. For example, if a new housing development is created, how would this affect the ecological profile overtime?

Two Days in the Life of Anna

This scenario would take place five years from now. The first thing to know about Anna is that she is equipped with a notebook tablet size device called a handlet. Handlets have some interesting properties. For example, it not only has a camera, but the camera can record different colors of the light spectrum -red, green and blue wavelengths. The handlet also has a GPS system in it, so Anna knows exactly where she is.

Anna gets out of bed, and her handlet lets her know what time it is. And since it is part of a sensing network, the handlet can also tell her how cold it is or even how humid it is outside.

Anna is also part of a school group doing a project on energy use and transportation. She in the biking group so she really does want to know if it's going to rain later on in the day.

While Anna rides to school, her bike is collecting data on how much power she is exerting while pedaling and also using time to calculate her energy usage. As she pedals to school she has a cool bracelet version of the handlet which shows a map with the locations of her other friends on their bikes. When she gets to school she meet ups with her bike team to share their thoughts about the morning ride. Other teams are the car team and the bus team. Each team is gathering data that is all being saved in a portfolio system so that all the students can see and share each other's data.

Toward the end of the school day, Anna is not feeling well. She has the one-day flu. On her ride home, her heart rate is fast and she feels more tired than usual. When she gets home, she is out of breath and wants to talk to her mom, who is still at work. She can do this using her handlet that has voice and video over IP. Anna tells her that she is not up to doing any homework. Her mom tells her to stick her hand into the "doctor tester." The doctor tester uses a drop of blood to measure a number of parameters about what is going on inside her body. It also does an imaging spectral analysis of her pallor. Her mom can also see the results and determines that indeed Anna has a virus and that there's nothing to do about it but rest. But mom also insists that Anna does her homework.

Later on, Anna decides to check in on the Beluga whale project that she is a part of. The project is tracking a pod of baby whales and getting data about their physical and migratory states. She does some research on Beluga whales on the web and puts copies of relevant research in the appropriate place in her portfolio. Interestingly, this information isn't just stored locally but centrally so there is always a copy in case something happens to the handlet. This also enables her to share her research with other project members. Later that night she detects that there might be a problem with some baby whales. They used to come to the surface more often. The mother appears to be pushing the baby to the surface. She and her friends have an online discussion about the fact that both Anna and the baby whale felt sick on the some day.

Anna goes to bed and her handlet keeps track of her sleep cycle, detecting intervals of REM and possibly bad dreams. She will be able to use this data to compare how her healthy sleep cycle compares with this particular night when she is not feeling well.

Anna feels better the next morning. Today her bike group is presenting their power usage data to the rest of the class. She shares her physiological data over the days the bike group has been keeping track of its energy expenditures. Looking at her charts, Anna's teammates notice that her energy output level was much lower on the day when she was sick. The group then drills down to get more detail on what was wrong. They look at Anna's actual metabolic data. They see that her pulse, temperature, breathing were higher but her power usage was a lot less and it took a lot longer to get home. The teacher's role is to provoke a culture of inquiry in the science class and she uses this instance to challenge the class to come up with some reasons that would explain this data. The students then embark on a project where they want to systematically experiment with the relationship between power usage and respiration rate, temperature and so on. The goal is to come up with general indicators of how healthy someone is. To probe this further, they need a larger pool of data. So they communicate and begin to convince other classes around the country to share their data so they can get a larger sample size for their study.

Sensor Augmented Costume

This is an idea for younger kids. Imagine a Halloween costume of a dog with sensors that allow you to see the world with the kinds of colors that a dog could see. Or with lenses that would allow you see as though you were looking through a compound eye.

It builds on the existing culture of kids liking to dressing up, but harnesses sensing technology to give kids a -sense of what it might feel like to be a particular kind of animal.

Artificial Nose

Imagine being able to recognize or sensemore in the world than you are currently able. It could also be thought of as a kind of category recognizer. Suppose you are walking along and see something you don't recognize for example a plant or a bird. You could take sensory snapshot of the thing that might involve images or even the object's chemical composition. You can go home and upload it to your desktop machine and get information about the particular kind of object you saw. But more importantly, you could use the desktop machine to make a custom sensor for the particular object. That is, ask the machine to "make me a sensor" for this plant or this bird or this sound. Maybe there's a kind of flash memory stick that can be inserted into the desktop machine that is a programmable sensor. Now you have a sensor that is for this category of object.

Suzie walks up the hill and sees a new plant. "What's this?" She takes a picture with her digital camera and images it.

Later in the day Suzie customizes the sensor to be a "black mustard" sensor by plugging in her analyzer stick --a device that is able to take a chemical "fingerprint" of the plant. She uploads her local data map on black mustard to the regional analysis and mapping center. She is connected via GPS while wandering around. Later Suzie's local data is integrated with a national data center and map on black mustard. She compares these.

You can collect or trade category sensors or give them away. Now as you walk along in your environment and you see the object, you can place the sensor beside it and down load the information to the desktop device -this is another instance of using the category to learn about objects that you are curious about. Once you are connected to the web there's all kinds of related information that could be gleaned -movies, facts and so on. There are a lot of ways the idea could play out including being able to compare the location or other properties of your particular object against information in larger databases generated by communities of data gatherers. This could play into kids' notions of scrapbooks. You could imagine having a scrapbook so that as you page through the scrapbook of actual leaves, the scrapbook might also contain a little chip that attaches to your sensor device that would provide more information about it.

In general, the world is filled with things that you'd like to recognize. What sensors can do is expand your cognitive repertoire of things that you would like to know more about. Once you have programmed your custom sensor, it could be posted on the web so that others could download it and create a community of informal researchers interested in say, invasive plants of a particular domain or region. This would enable you could then get a map showing the distributed locations of a particularly noxious or invasive plant right now. You could also get a sense of a plant's local effects by capturing the stuff around it and map its effects on its environment overtime. For example, what are the other locations in a particular region that have this same fingerprint? You could also see temporal and visual overlays making visible how one plant prepares the way for the next one that moves into the region. With augmented reality glasses, you might even see a projection of how the local region you are looking at might change as you see the invasive plants marching across the hill or meadow. If there was a true sensing network, millions of people with their own sensors could build up a rich database of knowledge about the environment. A key idea here is how your own personal activities can be seamlessly connected to broader communities of social interest A rough schematic of how this might work is to have a micro analytical array of antibodies with an electronic substrate that draws air through it and detects those places where molecules stuck to the antibodies. Then you could see how antibodies stuck to the electrical substrate and see changes in the responsiveness of the particular parts of the array. This would create a kind of fingerprint -an organic chemical sensor. Finally, you could think of this as a kind of deja vu sensor packaging the sound, smell and sight of a particular object that could be recognized and reexperienced. A group at Cornell is working on this right now -their project has 60 thousand birders gathering and collecting information on the migration patterns of various species of birds. This demonstrates that one can build virtual communities of people with common interest by making each of them sensors for particular kinds of object. These scenarios create a future vision. But it is impossible to know when the ubiquitous sensing networks will exist, with the appropriate computation, to make these scenarios a reality.

Interconnected Cyber-beings

A vision emerges of networks of cyber-being; humans augmented with super sensing powers and the ability to consolidate this data via metadata schemes.

Technical issues in making ubiquitous networks a reality

The reality of having ubiquitous networks enabling the integration of millions of sensing nodes is very far away. There remain many technical challenges. For example, it is easier to integrate information from sensing nodes that are fixed on buildings than from sensors on mobile objects.

Markets for sensing networks in education?

Is there a market for SENS networks in education? Rich in long term potential --especially the customizability of personalizable sensing devices --it is still an open issue as to how to develop such networks and products serving real needs and business models that are sustainable. Especially difficult is the challenge of gathering empirical evidence that such networks would afford real learning.

On the other hand, the compelling, engaging notion of a handlet sensing device may not be that far away.

There may be opportunities for making money through developing and selling pieces of the process that might drive innovation.

There are specific sensors out there for various properties, but the idea of programmable recognizer may be powerful, do-able and marketable. The idea could be a sweet spot for whole generation of sensors. Another important idea is having techniques for filtering for kinds of data someone could be interested in.

Public health as a fertile area for development

Public health is also a domain where cost reduction and improvements could drive sensing network innovation. On the psychological side, if you knew what you were doing to yourself (e.g., overeating) would it change your behavior? For example, sensor vision which would show you visually the effects of smoking in a mirror.

Anna's handlet could provide immediate feedback to parents or doctors about emerging patterns that might affect her health. But would a doctor really allow this?

Designing new kinds of musical instruments Maybe slightly less controversial yet marketable applications are tools for helping kids design new kinds of musical instruments that might respond to properties like one's body position or breathing rate.

Competition may also be used to promote sensing technologies. In Japan, there are individuals (not kids) already competing with each other over networks that sense data captured by individual pedometers.

Understanding patterns of data overtime

A powerful idea is to link the individual to patterns of data changing overtime. This requires linking actions and outcomes for learning. One possibility is to use reminders and prompts as predictors as to what might happen if behavior continues in a certain direction.

How to exploit data from a classroom as a unit? How could sensors be used to measure flow of the class as a whole throughout some interval of time? Could the flow experience (in which skill level and task challenge are matched so well that one develops new abilities in a "flow" with the environment) be optimized using sensors? Perhaps timestamped portfolios are part of answer; metadata captured in portfolio activity overtime would reveal relationships among many kinds of data. The goal would be harnessing new analytical techniques to figure out what are the relevant relationships and attributes in looking at a million portfolios overtime. This approach diverges from traditional scientific method of hypothesis testing.

Existing sensing projects tracking flow

There are existing sets of data that could be exploited for learning. For example, Los Angeles transportation engineers are using cell phone data to map traffic patterns. They are also sensing bridge traffic. But kids have no access to the data gathered in these projects. How can we make these available to a kid for specific purposes? What are the access rights and issues?

Hybrid models of learning and assessment How to make these visions happen pragmatically within the school systems over the next five years remains a daunting challenge. But there are interesting possibilities. For example, one step may be to develop a hybrid model of active investigation --scaffolding multiple-choice items with active investigation based on some activity a student has just carried out. This would insure not only the ability to do active investigation, but also the ability to perform well on standardized tests. Data analysis tools in portfolios could still be a key enabler. The challenge is how to link traditional items in an assessment to what kids are actually doing. Think of a future on-line SAT that coordinates assessment with active investigation. One would still need evidence that these activities are working in the assessment scheme. How do we get beyond the constrained linear multiple-choice model to encourage critical thinking made possible by open-ended assessment? It could be that we should just proceed to innovate and that standards will catch up.

(Prentice-Hall), a textbook that supports college-level courses based on mobile robot design projects. Dr. Martin also cofounded Gleason Research with his wife Wanda Gleason, a robotics company that consults on educational projects. He is a founding engineer for Ipsil, Inc. a privately held start-up company in Cambridge, MA. http://www.cs.uml.edu/~fredm / Michael Mills, Ph.D. Stanford Center for Innovations in Learning Michael Mills is cognitive scientist with 17 years experience in interface, product design and user studies. He has a track record of innovation and accomplishment in real-world interface development, product design and teaching. While principal scientist at Apple Computer, he was instrumental in the development of QuickTime and QuickTimeVR. At IDEO product development, he was lead interaction designer for 3COM's Audrey information appliance. As tenured professor at NYU he developed The Active Eye interactive softwarefor teaching visual perception and has taught courses on computational media, computer graphics and research methods. He holds several interface design patents in digital video and has authored many articles on interface design. http://www.stanford.edu/~mmills Charlie Patton, Ph.D. SRI International, Center for Technology in Learning Early in his career, Charlie Patton was struck with a compelling vision of how handheld devices could be designed to radically democratize access to the concepts of mathematics. In 1982, he took this vision to Hewlett-Packard Co., home of the first scientific calculator. Since that time, at HP, with Texas Instruments, through NSF grants, and now, at SRI, he has been fully engaged in researching, fostering, and inventing the future of handhelds in education, including the first symbolic handhelds, the HP-28C and successors, that changed forever the ground rules for the teaching of calculus and algebra. Dr. Patton has authored 4 books, numerous articles, and currently holds 13 final and pending patents in handheld software systems, wireless networking, and digital rights management, with several more in preparation. At CTL, Dr. Patton is helping build a technology bridge from research to practice, while fostering the uptake of learning science insights in a number of SRI's technology programs.

Tom Prudhomme, Ph.D. UIUC/NCSA, Division Director, Cybercommunities Grid computing technologies offer professional and research communities a revolutionary new mode of operation. The Cybercommunities Division of NCSA is leading the way by building and transforming these communities of practice and by applying and extending the Grid infrastructure. Any technology is hampered from the start if its implementation ignores the human side of the human/technology interaction-not only on the individual level, but also within the dynamics of larger groups. By forming close partnerships with selected communities, Cybercommunities ensures they are served by an innovative and effective Grid-based environment, developed and implemented through an understanding not only of the newest technology, but also of the human processes related to collaboration, learning, and knowledge sharing. Tom directs this NCSA Division and the NEESgrid Project (http://www.neesgrid.org/) , a major grid computing initiative funded by the National Science Foundation, which is linking earthquake researchers across the U.S. with leading-edge computing resources and research equipment, allowing collaborative teams to plan, perform, and publish their experiments. (3) The role technology can play in mediating science learning and teaching in classrooms, especially in supporting meaningful scientific inquiry; and (4) Understanding how innovative designs for education can help us develop better theories of learning and better educational practice. He is collaboratively pursuing these issues using CENS's embedded networked ("in-situ") sensing technology for habitat monitoring and seismic sensing over the next several years, so that grade 7-12 students can, using Inquiry Modules, remotely observe complex systems in nature, by pursuing their questions through creation of new experiments that direct networked sensors to collect data in a way they have designed. Each Inquiry Module is developed by focused, collaborative teams including domain experts, grade-level teachers, educational researchers with experience developing and studying inquiry-based learning environments, and information scientists experienced in integrating complex data and interactive tools into curricula.

Roy

Janet Fouts

The Exploratorium

  Pea, D. Phil., Oxon Stanford Center for Innovations in Learning Roy Pea is Professor of Education and the Learning Sciences at Stanford University, Co-Director of the Stanford Center for Innovations in Learning, and Director of a new PhD Program in Learning Sciences and Technology Design. Since 1981, Dr. Pea has been active in exploring, defining, and researching new issues in how information technologies can fundamentally support and advance learning and teaching, with particular focus on topics in science, mathematics, technology education. Particular areas of interest are computer-supported collaborative and on-line community learning, uses of digital video for learning research and teacher education, scientific visualization, and pervasive learning with wireless handheld computers. He has published over 110 chapters and articles on cognition, education, and learning technologies, and was co-author of the 2000 National Academy Press volume, How People Learn. With NAE President Bill Wulf, he has been chairing a joint National Academy of Sciences/National Academy of Engineering Committee on Improving Learning with Information Technology. Before coming to Stanford, Dr. Pea was director of the Center for Technology in Learning at SRI International. He also founded the Learning Sciences Ph.D. program at Northwestern University, and served as dean of the School of Education and Social Policy. http://www.stanford.edu/~roypea/ Bill Sandoval, Ph.D. UCLA Graduate School of Education and Information Studies Center for Embedded Network Sensing Dr. Sandoval's research examines the following areas and how they relate to one other: (1) Children's ideas about how science is done and what scientific knowledge is, and how these ideas influence their efforts to learn science; (2) Teachers' ideas about how science is done and what scientific knowledge is, and how these ideas influence their science teaching;

  http://www.gseis.ucla.edu/faculty/sandoval/ Nancy Songer, Ph.D. University of Michigan Nancy Songer is an Associate Professor of Science Education and Educational Technology at the University of Michigan. Her current research focuses on in-depth investigations of the educational potential and realities of innovative technologies for reform-based science education. Recent awards include being named a 1995 Presidential Faculty Fellow by the National Science Foundation and the White House, and the 1995 Early Career Research Award by the National Association of Research in Science Teaching (NARST). Dr. Songer currently directs the Kids as Global Scientists project (www.onesky.umich.edu), a WWW-based curricular/software program currently implemented in 90 world-distributed middle school locations. http://www.soe.umich.edu/faculty/songer / Lori Takeuchi Stanford University Lori Takeuchi is a second-year doctoral student in the Learning Sciences and Technology Design (LSTD) program at Stanford. Lori has been involved in the WILD research projects and is currently the Program Advisor for Stanford's Learning, Design, & Technology (LDT) Masters Program. Before coming to Stanford, Lori produced middle-school science software for companies including BBN, Logal Software, and Riverdeep Interactive Learning. She received a master's degree in Technology in Education from Harvard, before which she worked in instructional television at Thirteen/WNET in New York.

PART 3. Making SENS --Emergent Ideas

Here is a summary of key ideas that emerged from a synthesis of the morning Making SENS matrix exercises and the afternoon scenario sketches.

Integrating personal with networked communities

A key idea cutting across all the scenarios is the importance of not only instrumenting peoples' personal sensing possibilities, but also how to integrate their personal experiences with centralized, and possibly massive databases of containing information from other individuals. A nascent example is the Cornell project exploiting the data gathered by 60 thousand "birders".

WORKSHOP PARTICIPANTS

Stephen Bannasch, Ph.D.

The Concord Consortium

Stephen Bannasch, Director of Technology for the Concord Consortium, manages technical planning and development. He pioneered the technology used in educational applications of computer interfaces to laboratory experiments (microcomputer-based labs or MBL). Dr. Bannasch is currently working on the Data and Models project developing new physical and computer models for helping kids understand heat and temperature. He also works with the Exploratorium in San Francisco developing handheld computers and wireless communication to allow visitors to explore exhibits in greater depth. http://www.concord.org/~sbannasch/

Corey Brady

Texas Instruments Corey Brady is an educator and a developer of education materials that use Web and network technologies effectively. As an instructional designer and programmer, and later as chief education officer and chief operating officer, Mr. Brady helped to conceive, create, and manage the online mathematics curriculum products of Boxer Learning, Inc. He is now with Texas Instruments, as manager of product strategy for the TI-Navigator networked classroom solution.

Michael

Kimihiko Iwamura

Omron Advanced Systems Mr. Iwamura has been leading OAS since 1996 as general manager and president. He has extensive business relationships among major corporations and financial institutions in Japan, as well as startup companies and venture capital firms in Silicon Valley. Mr. Iwamura is a fund management and cross-border business development professional, with over 12 years of experience in both investing with venture capital firms, and arranging strategic alliances in Silicon Valley. Previously, he was with Fuji Xerox for more than 15 years as a strategic planning manager. Mr. Iwamura holds a B.S. in physics from International Christian University in Tokyo.

Eileen


Shelley Goldman

Roy Pea

Heidy Maldonado

Lee Martin

Toby White

Functioning in the Wireless Classroom

Code It! fosters mathematics learning environments where pre-algebra students use handheld technologies to confidently and enjoyably explore and learn about functions. The resources we developed-server-based and handheld software and paper-based student and teacher texts-were packaged as a 20-session unit on code making and breaking and designed to boost students' understanding of mathematical functions and their facility with the multiple representations of tables, graphs and symbols. We field tested the wireless system with two teachers and 120 students during summer school, and conducted studies on the features and function of the technology as a learning and teaching resource. We report on project development and research, focusing on lessons learned about the strengths and difficulties of wireless, handheld technology in the mathematic classroom.

Need

This is a particularly important time to develop and research technologies for math learning. Across the US, there is a push to raise lukewarm achievement results on standardized tests and international comparisons. Teachers are being asked to teach more content while raising the level of achievement, but are left unprepared. The Code It! materials-technology and real world based-were designed to address this challenge, capitalizing on developing technology for stimulating high performance while providing a satisfying, successful learning experience on the topic of functions.

Technology should be providing tools to support the activities of mathematics classrooms. Gathering data, looking for data patterns, developing algorithms, thinking spatially, working with multiple representations, and WILD Team@Stdnford members contributed to the C I I ~L ~ 111 work reported herein. Thanks to Sarah Walter, Gloria Miller, Mike Mills, John Murray, Nicolai Scheele and Wolfgang Effelsberg. Special thanks to our partnering teachers and their students, the Stanford Teacher Education Program and the Stanford student teachers, and the Santa Clara School District. This project was supported by Stanford Center for Innovation in Learning (SCIL) and the Wallenberg Global Learning Network. We also thankfully acknowledge an HP mobile wireless computing equipment grant to SCIL.

complex calculations are all greatly facilitated by computers, yet many problems have accompanied classroom computer use: prohibitive cost, use models that do not mesh with classroom structure. and a learning curve for teachers that has been too high for too little gain. Even when researchers developed compelling exemplars [START_REF]Middle-school Mathematics through Application Project Final Report to the NSF[END_REF][START_REF] Roschelle | Changing how and what children learn in school with collaborative cognitive technologies[END_REF], changes in organizational, social, and pedagogical practices with computers produced high hurdles [START_REF] Goldman | Technology in the Mathematics Classroom: Guidelines from the Field[END_REF][START_REF] Means | The Connected School: Technology and Learning in High School[END_REF]. The combination of increased access, increasing teacher knowledge, decreasing technology costs, and new portable devices is creating a synergy and new possibilities for mitigating problems.

Early studies of handheld technology use are modest yet suggestive of how they might best be applied [ 5 , [START_REF] Curtis | Handheld Use in K-12: A Descriptive Account[END_REF][START_REF] Luchini | Supporting learning in context: Extending learner-centered design to the development of handheld educational software[END_REF][START_REF] Vahey | Palm Education Pioneers Program: Final Evaluation Report[END_REF][START_REF] Roschelle | To unlock the learning value of wireless mobile devices, understand coupling[END_REF], 101 These are promising trends, and to tap the potential in Code I t ! , we developed technology for enhancing students' math learning by providing representations that can be easily manipulated, and by creating a context for learning formalisms [I 1,121. Our project addresses three main research areas:

1.1. Enhanced mathematics learning. Do t h e materials and wireless tools enhance student learning of crucial algebra concepts and skills? What progress do students make in reasoning with representations of patterns and functions? In communicating mathematically? Are there any increases in productivity with this approach? Is engagement with math high? Do the materials boost student achievement? 1.2. Productive tool use. How well do wireless handhelds or desktop con~puters work as a tools for math learning? What are the resources and obstacles to use of the handhelds, network infrastructure, and collaborative activity structures for learning mathematics? 1.3. Enhanced teaching. Through partnerships with teachers we learn about conditions for success and needs for further development. Will teachers benefit from unit teaching tools and access to server-based group and individual portfolios and data? What are the critical issues in teacher learning and appropriation of this technology?

We anticipate that the results in each of these research areas will influence the design of a generation of math tools by indicating ways that server-based communication, data tracking and storage capabilities can enhance the teacher's role.

Our Response and Approach

The project was structured around three strands of activity: materials development, work with teachers, and field test research in the classroom. Each is discussed in turn.

Materials development.

We created a technology-integrated 20-session curriculum unit on linear and quadratic functions, with a code making and breaking theme. The unit was developed to bring alive in classroom activity the functionality of the Code It! application with pre-algebra content. The unit places algebra learning in a real-world context-making and breaking secret codes [13]. Students can challenge others to break their codes to determine message content. The materials are structured so that students must examine properties of functions in general, learn to distinguish among "families" of functions (e.g., linear vs. quadratic) as well as learn properties of each family (effects of changes in constants to graph, for example). Connections among symbols, tables and graphs are emphasized.

Code It! was inspired by a curriculum unit, Codes, Inc, developed by the Middle-school Mathematics through Applications Project (MMAP), which won recognitions as a standards-based curriculum as Computerworld Smithsonian Awards finalist, and as a promising middle school mathematics curriculum as judged by two independent US Department of Education expert panels. Evaluation showed teachers found the MMAP codes unit helpful for transitioning to great use of technology and more applications-based curriculum materials [START_REF] Lichtenstein | Final Evaluation Report: Middle-school Mathematics through Applications Project[END_REF]. Teachers saw real values for the unit with its multiple representations of functions to aid learners' development of rigorous algebra skills.

2.1.1

The change from desktop to WILD. The advantages of the wireless version of the software over the desktop version are obvious. With hand-helds and web technologies, students can create and exchange codes easily, and records of their work and communicative transactions can be captured on the server. It is this promise of engaging technology that promotes increased interactions with mathematics and embedded assessment activities that we hope to promote. In the desktop software developed by MMAP, students could create substitution codes software, view graphs, frequency tables, and patterns in codes, and receive verification of successful decoding based on the expressions used for coding. Previously, exchanging the codes was based on cumbersome e-mail, now supplanted by wireless communications.

Code It! software is built around an in-room wireless network which allows students to work on codes together in groups and to share codes between groups. Each PDA is connected wirelessly to a teacher's station located at the front of the room. The teacher's station runs administrative software that allows the teacher to control and monitor groups in a variety of ways. The teacher can monitor which students are logged into the server, place students into groups, create and distribute practice problems, and open an observer window to display a group's current state. The observer window is particularly important for teachers because the small display size of the handhelds makes it more difficult to monitor group work in a traditional "over the shoulder" manner. In addition, the server logs each group's activity, allowing for a researcher or teacher to reconstruct the precise ways in which students were using the software and solving the math problems. The server also acts as a repository for text and codes created by the teacher and students, providing an easy way to share codes among groups and to distribute practice problems.

Image 1 : Function table view of a code solution.

'lain Text: Each PDA runs Code It! software that allows students to create plaintext, to make and break codes, and to upload and download text and codes from the server. When making or breaking codes, the student's screen displays the encoding function (or current guess) and the graphs and tables that represent the function and the coded text.'

In the current implementation of the s o h a r e , these various representations are displayed on a single long screen that must be scrolled up-and-down to bring each representation into view. While this is not ideal, our field-test focus was on ensuring that the software was robust, and the design as it was generally These representations include a graph of the function, a function table, and word and letter frequency tables. These representations were selected in order to foster particular kinds of learning, rather than to create an ideal coding tool. All of the PDAs within any given group are bound together via the server. A change made by one student propagates to the other students in his or her group, so the PDAs within a group always display the same function and text. Students were encouraged, by the assignment of group roles, to have each group member focus on a different representation. One student can look at the graph, another at the function table, and a third at the letter frequency table, for example. The goal is to foster communication among group members and increase the diversity of information available to the group for the coding or decoding task. When a group has successfully created or broken a code, they submit their solution to the teacher station.

Instrumentation. Beyond its

-5 use for channeling the work of collaborative groups to be insynch, the server stores all messages being worked on by the different groups. It also provides administrative functions so that the teacher may define groups and assign students to groups. It allows both teacher and students to write and edit plain text messages with a normal keyboard, not just the restricted keyboard of the PDA. The server also logs communications to and from the PDAs to a file functioned well. This interface issue will be corrected in future versions.

that can later be used to recreate the PDA display for any student at any particular time.

Work with teachers.

The professional development component turned out to be as important as the materials and software in ensuring that students get a well structured, engaging and deep mathematical learning experience through Code It!

Teachers "choreograph" the performances of classroom tasks and activities to enable students to learn mathematical ideas and language, and to develop mathematical practices. Teachers are also faced with supplementing their adopted texts to meet students' needs for more in-depth coverage and differing approaches to the most important concepts. They also experiment to increase comprehensive coverage, an emphasis on thinking, problem solving and communication. We know that teachers must do a great deal of work to arrange productive participation structures in their classrooms, and we need to learn about the places that they can find for incorporating wireless technology under daily classroom conditions. This is a complex job, and teachers need time to learn and develop both teaching sensitivities and practices with technology. Their professional development is crucial.

Professional development included three days of training and planning with the teacher partners as well as just-in-time, in-class support. The workshop included: Time for being introduced to the technology including PDA use and the teacher station.

Early access to review of the curriculum unit, and discussion and revision of each activity. Practical planning advice and session-by-session planning time for implementing technology in the curriculum. This time included management of technology-related issues such as battery life, the class activity schedule and student roles. Just-in-time daily consultations on math and technology while implementing the unit. Our research questions are aimed at uncovering the strategies that teachers use to orchestrate the use of handhelds, identifying the obstacles and affordances they interact with in using Code-It! materials and technologies.

Project questions and methods.

Our research goal was to test the usefulness of the materials and to generate knowledge about the ways in which they were effective. We collected several data streams, including observational data, videotapes of teachers and the four-member student groups used in the classrooms, problem-solving and informational interviews with student dyads, and pre-and post-testing of mathematics knowledge. These methods, of observing and videotaping classroom field tests of materials and feeding back results into the design, enable the development team to define areas for revision based on the use of the materials under real classroom conditions [IS, [START_REF] Cobb | Design Experiments in Educational Research[END_REF]171. The cycle of development and field test supports our learning about how teachers and students receive materials and how best to iteratively improve the design for achieving the teaching and learning goals.

It is part of our method to track topics and issues as they emerge in field tests and teacher workshops, and to then interview, videotape, and interrogate. We look first for obvious patterns of structure such as who is involved in planning and holding activities, how the space is set up and how and when people move around in it, what the flow of activities is from start to finish, what materials and resources are used, how and when teachers and students talk, what they talk about, and how they interpret their experiences after the fact. After searching for some of these basic structures, or because of "noticeable" events, other areas for analysis emerge. These qualitative methods are complimented by the development and use of problem-solving tasks and pre-and post-tests with students and interviews and surveys of teachers.

The research process is characterized by an interrelationship among field-testing, development, and evaluation. For instance, prospective materials emerge from user tests and focus groups, get written, and get put back into the field test, during which time those materials are evaluated and revised. Finally, materials are put to the test under controlled conditions in a series of summative, clinical studies.

We partnered with the Stanford Teacher Education Program and the Santa Clara, CA Schools to use Code It! with all of the district middle school students in their summer school program for our 20-session field test with two teachers over a five-week period. Each teacher had 60 students split between two consecutive 100 minute class sessions: one teacher used handheld iPaq computers (PDAs); the other teacher used Compaq Tablet PCs. Our analysis concentrates on the two PDA classrooms.

Our WILD team developed software and a unit of activities in consultation with the teachers, pre-and posttested students with mathematics vocabulary items, short answer problems based on unit concepts and representations, and released pre-algebra and algebra items from the California High School Exit Exam. We collected video on 120 hours of classroom time, using two cameras in the PDA classroom each day. This enabled us to "follow" the activities and progress of four groups of students in those classes. We also interviewed twelve pairs of students (N=24) from those focus groups. For students in our focus groups, we have pre-and post-tests, an interview where they were asked to problem solve and break a code, and a video record of their activities and progress through the unit. In addition, the server provided a record of process steps for each group's attempts to make and break codes, and when they successfully completed each task (or not).

Image 3: Student group working on Code It!

Lessons Learned

Analysis is preliminary, yet we have some encouraging results on how our learning technology design functioned. and the ways in which teaching and learning were affected. Whereas we have outlined the project with three categories of interest, our results reinforce the synergistic nature and interactions of the technology, the teaching and the learning. We do not report on them as if they were mutually exclusive because we observed them in the flow of classroom activities where their affordances and constraints melded into resulting situations and events. So we present here observations where the constellation came together in mutually reinforcing ways or in negative ways. We are able to offer some next steps that we have identified to improve the Code It! experience and some of those steps point to specifics of students' math learning, how teacher can make the most optimal use of the materials, and how the design of the Code It! technology itself might change.

Students and math.

Two aspects of student work dominated our attention during and after the field test. The first was meeting our goals of getting students to interact with, discuss, and use the mathematics. The second was the establishment of the social arrangements needed to support working with the tools to meet the math engagement goals.

Students' interaction with mathematics.

To date, we have analyzed results of the pre-and post-tests in the PDA class with our targeted student groups and the results were very promising (N=45). The mean increase from pre-to post tests was eight percentage points; in four of six focus groups, students made significant gains, in some cases raising their scores by 15.30%. The PDA students showed significant gains on test items relating to evaluating exponents and the graphs of functions. On one graphical item, 44% of students answered correctly on the post-test, as compared to only 13% on the pre-test. What is exciting about these trends is that these results were for students in grades 6-8 who were placed in heterogeneous groups regardless of their previous school math course achievement. Many students in the class had done poorly in sixth or seventh grade math, while some students had completed the first year of algebra. These algebra students were encouraged to help others, and the pre-and post-test results showed that these interactions may have had an impact on achievement.

3.1.2.

Focusing on the Curriculum. Turning the attentions of students in the Code It! classroom to the mathematics took a great deal of social engineering on the part of the teachers and the development team. The team had to mitigate the effects of the widely heterogeneous groups and the constraints that the Code It! tool entered into the equation. We treated these as both challenges and opportunities. During the teacher preparation workshop, we discussed these constraints and decided to introduce rotating social roles inside the groups in order to make sure all students had a chance to work with all aspects, tools and representations during the problem solving process. Once the session began and students started learning how to code and decode using the PDAs, a more anarchistic atmosphere emerged in the classroom. The teacher moved ahead with the activities with a mention of the roles, yet students forged ahead without an obvious group structures.

We saw "stylus wars" break out among students who were vying for control of the group PDAs and communication with the server. Heated discussions arose and the chaos and discontent led us to work with the teaching staff to design social roles that would foster group collaboration, rather than competition. Linking the process roles of (I) recorder, (2) presenter, (3) publisher and (4) equipment manager to the different code representations in the software, and rotating these roles avoided much of the inter-group conflict, but also contributed to more engagement on the part of more students in code analysis and breaking activities. In addition to group roles, the teacher and summer interns instituted and enforced a rule stating that "all students in a group must be able to explain the strategies and routes to solutions in order to receive credit." That meant that if the more skilled and experienced students in the group solved a code before others, the entire group had to review the steps taken, the strategies used, the solution, and be able to demonstrate it on their PDAs. This increased team cooperation, resulted in a practice of questions and explanations across group members. Teams spent time working together to make sure they could each explain their problem solving if asked informally by a teacher or if asked to present to the entire class.

Due to the social engagement rules, we saw more consistency of performances on the part of the students over time. Early on we observed a full range of performances, from students engaging in some extremely rich and iterative collaborative problem-solving processes, to students relying on simple guessing strategies, to students avoiding work altogether. After instituting the social rules for "student roles" and the "explanation rule for credit," new patterns emerged, and we observed multiple instances of the students who took problemsolving approaches talking with those who usually guessed or seemed uninvolved. The social engineering improved group relations and engaged students more deeply with the software and the mathematics under study.

Image 4: Teacher's view (server screen).

Teachers.

Findings about teachers and their work fall into three categories: (1) teacher training and teacher knowledge; (2) teachers' appropriation of the technology: and, (3) teachers' "workarounds" for the technology's shortcomings.

3.2.1.

Teacher training a n d knowledge. Even though teachers participated in three days of teacher professional development that included explorations of the unit and the technology and individually practiced using the teacher station and the PDAs, the situations that developed when twenty-five to thirty students were using the system in class outstripped their novice knowledge. While the teachers were clear on the basic operations of the PDAs, they were less experienced in working with the interactions that students would practice and the ways the software could be used to pose or solve particular coding problems. As the days of the field test went by all three of these teacher practice areas improved. We did see examples of how continuing work with the teachers on an ad-hoc basis addressed some of the teachers' lack of prior experience. During the first two weeks of the unit, the teachers found themselves grappling with understanding the potential of the technology to help with the students' problem solving processes. In one of several meetings between a math educator on our staff and the teachers, the strategies for working to decode were explored. It was agreed at the meeting that our staff member would pair with one of the teachers so they could demonstrate together a systematic way to use Code It! to crack more difficult codes. Once this happened there was an increase in student productivity in code breaking. We realize in analyzing this incident that we were unable to have predicted the teacher's need for scaffolding in order to use Code It! most effectively with the students. Next steps will include more classroom problem simulations for the teachers to work with that can be fashioned from this year's field test. This will help teachers to figure out how the tools can best be used for dealing with different kinds of problem solving affiliated with the unit.

Teachers appropriated the technology.

The teachers actually did a great deal of work to make the technology useful for meeting the curricular goals. Code It! presented them with a set of challenges and complexities to manage and, in some instances, overcome. We saw the teachers appropriate the technology into their teaching practices. One teacher found a way to use the blue and white circles that showed the active status of every student in each group on the teacher station screen as a classroom management tool, keeping track even from across the room of which students were on-line and which groups were most active. This enabled him to troubleshoot for students who were having log-on issues as well as to visit groups that were lagging behind in the decoding problems. The same teacher used the teacher station to develop activities and code breaking problems when he saw the need for more scaffolding between simple and more complex code breaking problems. One student teacher in the classroom also developed problems and entered them into the system through the teacher's station. This gave teachers the opportunity to assess students' work and needs, to create hand-tailored activities and problems, and to organize them so they were available to the students. Both teachers used projection equipment so they and students could discuss with visual aids the various strategies for code breaking as well as demonstrate how the different Code It! tools could be used. This demonstrated how the system was robust in use and how teachers made it compatible with their teaching practices.

Teachers worked around technology

shortcomings. Some constraints in the technology caused rifts in the smooth flow of classroom activity and the teachers found themselves compensating when needed. The constraints were many and ranged from overload during whole class log-on, to the lengthy scrolling screen with multiple representations of data, the fact that some of the representations of coded data were more useful to students than others (e.g., as the system could only handle short codes they could not benefit much from word frequency analysis data), to the fact that the system could log only group work. The teachers created short codes when they provided supplementary activities, shared strategies for working with all of the representations, reminding students of how to find them, and established and enforced a series of roles and expectations about the sharing of code breaking solutions to mitigate the fact that but one PDA in each group could communicate with the server at a time (and return new results in a graph of the function entered, for example) as Code It! tracked only group activity.

Together, these findings about teacher work indicate that, although the technology introduced all kinds of management of instructional activities, the teachers were able to demonstrate a steep leaming curve, demonstrate an ability to strategize, plan and respond to student and technological difficulties, and to capitalize on the tools to facilitate math learning.

Technology design and performance.

Three features of Code It! were shown to need reworking during the classroom test: (1) how mathematical representations and tools are organized; (2) the choice to base the system on group activities and interaction over individual acts; and, (3) the design and usability of record keeping functions-potentially so promising with wireless technology. Each is discussed in turn with examples to illustrate why we decided to reconsider the design. Our original goal was to produce a first, yet functional and robust version of Code It! in a short development period to have it ready for the summer 2003 classroom test. In essence, we had less than twelve weeks for development. This decision was made because we were particularly interested in partnering with the teacher education program in their summer teaching academy and we were eager to provide a summer school mathematics curriculum that featured wireless computing. To us, this was both a service and an experiment. That time line resulted in several design compromises that raised issues when the system reached the classroom,

The organization of mathematical representations.

The ways in which students came to interact with the multiple tools and representations for understanding and finding code keys revealed side effects of our choices. The first representational issue arose from the fact that we designed the application with one, lengthy scrolling screen for different representations instead of using a multiple windows approach. While the scroll function was easy for students to learn and use, it gave prominence to the first representation showing on the screen (the graph). with other representations absent from view without scrolling. We anticipated that giving prominence to the graph would signal a hierarchy to students, and it did. We constructed the curriculum materials and a process for introducing the other tools and representations (a function table, letter' frequency and word frequency tables). The group process and daily rotation of assigned roles for students attaching them to each of the representations was a compromise between the constraints of the system and the ways students could best benefit from the representations available. Although the system of each student having a portion of the data displayed for interpretation on hislher PDA made for group collaborative learning, it meant that on any day a student had the opportunity to relate to only one view of the data. We were able to deduce that the multiple representation capabilities of the software is a strength and real resource for mathematical work and learning. A question that now remains for investigation is how structured or unstructured students' access to those representations should be.

This is significant because we also discovered that representations were unequal in their familiarity and usefulness. The graphical tool was extremely powerful because the data window automatically fits to the values of the coded text. This enabled students to do a kind of curve-fitting that usually got them close to a correct code. Many of the students could determine the exponent graphically, and often the lead coefficient as well by adjusting the graph. The function table was just as powerful, though far fewer students learned how to use it well (at least partly because of a programming error that we corrected for halfway through the course). The frequency table was two tools in one, because it had an inverse function table embedded in it. That table was confusing to most students, and even those who had already taken first-year algebra operated with misconceptions when they interpreted it. The word frequency table was used even less due to the fact that our coded text passages were short due to the software slowing down with long passages. Students rarely used it, except to guess which numbers might be representing the single-letter words "I" and "a". These differences in representational placement, form and usefulness had consequences-the students assigned to the graph and the function table had the most interaction with the decoding activity.

It is not clear from our field test that our work on representations was completed to satisfaction. We came away from the field test questioning whether the multiple representations provided an appropriate level of scaffolding for the students' understanding. Some observed groups quickly found simple strategies that led them to crack codes without improving their dominion of functions. For example, some students consistently and repeatedly used the graph of a function as predictors of the range and domain of the encoding function, methodically changing values in the function until the axis labels came within the range of the traditional alphabet. Others consistently sought out single, two, and three-letter words, as well as mappings for the most common letter in the English language ("e") as a path for breaking codes. We have discussed incorporating a prediction capability to the software, which could alert the teacher or team itself when a group is in danger of depending solely on a particular pattern of code breaking.

The choice for group over individual.

Our design decision was to bias the function of the system to support groups over individual students. This was a choice we made early on in order to be able to experiment with the capabilities of the teacher station data collection. This decision proved to be extremely constraining in the classroom, and needed many socially engineered workarounds. Students wanted to play and explore when they had PDAs in their hands, but were unable to do so when it wasn't their day to communicate for their group. They had trouble orienting to these rules, and often competed for control of the stylus and PDA that would communicate with the server. Many cursor wars broke out in groups. When the wars were resolved in favor of strict, daily roles, some students became alienated. They were discouraged, and in many instances bored. This led them to discover other features of the PDAs such as appointment books, animated messages, and games such as Solitaire. Our desire to have the group at work and record all group transactions resulted in some students tuning out when they had a less-than-critical role assigned on a day. Schools also rely on individual accountability, and the system was unable to account for individual problem solving activity. As a workaround, we sought to set up each student as hislher own group, but the system could not handle 30 groups at a time.

Rethinking record keeping functions. When we designed

Code It! we were pleased to experiment with the kinds of information and data that the server could provide to the teacher. The server did keep track of a great deal of information about group problem solving. When a group would solve for a code, the server would congratulate them and create a record of the solution. Unfortunately. these records were not immediately recoverable by the teachers and more work will have to be completed to develop useable data on student work. We discovered that students also needed access to records of their transactions and solutions. It became a practice for students from groups to share the ways they approached a n d broke codes. Even though groups kept notebook records o f their solution strategies, students found it difficult to present their strategies after the fact.

W e are hoping that in the next version o f the software w e can support easy to implement access to records of problem solving by the students. W e seek to address the ideal balance between scaffolding for collaboration and individual exploration o f the encoded functions, within the C o d e It! software environment, s o a s to lessen opportunities for disruptive stylus-wars. Our upcoming iteration will incorporate a flexible grouping mechanism, while maintaining both the appeal o f collaboration and software robustness that made this first experience with C o d e It! successful. W e are also considering ways to decouple the individual PDAs from the system. This would allow for students to explore and problem-solve individually as well as in groups, and as well as provide opportunities for "anytime, anywhere computing", home and family connections.

Conclusion

W e had a successful field test o f the Code It! wireless application under extremely constrained, yet real, classroom conditions. Now that w e have an idea o f how students and teachers actually put such software to use, w e can develop a next version based on these interactions. This will result in a more aesthetically pleasing and useful application that fits the interaction, content, a n d a s s e s s m e n t d e m a n d s o f t h e m i d d l e mathematics ciassroom.

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