Module 1: Science, Scientific Inquiry and Project-based ... · through Collaborative Geographic Information Systems [ESIC-GIS] Module 1: Science, Scientific Inquiry and Project-based

© 2003 University of Kansas Extending Scientific Inquiry 1 - 1 through Collaborative Geographic Information Systems [ESIC-GIS]

Module 1: Science, Scientific Inquiry and Project-based Learning

This module will show the value of incorporating project-based learning with scientific inquiry as well as provide a concrete example from which to learn and introduce the importance of GIS. The concept of project-based learning has substantial scientific backing to legitimize its practice and usefulness in the classroom. Not only can project-based learning be incorporated into science classes, but the social sciences can also benefit. Through the process of learning the importance and application of project-based learning, teachers can help students move from being information consumers to becoming information producers and information decision makers.



Chapter 1: Engaging in Research

Teachers who learn science didactically and abstractly cannot be expected to teach children constructively and concretely.

Many researchers have found that classroom teachers who have never conducted scientific investigations and research as a part of their preparation, are unlikely to model these investigative behaviors for their students. Science teachers should have ongoing, significant, and substantial involvement in field or laboratory work, including active inquiry research that goes beyond traditional validation activities. Teacher's scientific research projects should require all aspects of scientific inquiry, including formulation of research questions, development of procedures, implementation, collection and processing of data, and the reporting and defense of results.

With this in mind, we are beginning this online course by engaging in an ongoing collaborative research project. Actively participating in this research is intended to give all of us common experience from which we can reflect on the process and nature of science. As you work through these lessons, you become a part of a collaborative research community. This community represents over 1100 schools including thousands of teachers and students. Researchers, 4000 citizen scientists, and community mentors who are actively engaged in scientific inquiry are also members of this extensive community.

Additional Reading: Before you begin, you will need a good understanding of latitude and longitude especially in relation to your GPS unit. Refer to Latitude and Longitude and the GPS Unit (Appendix A). It will help you to avoid common mistakes and gain a broader understanding of the use and format of latitude and longitude in relation to geographic information systems (GIS).

The Pathfinder Science research project is located at http://pathfinderscience.net

Research indicates that lichens and the tardigrades living on them can be used to monitor and assess

atmospheric levels of sulfur dioxide, SO2. This is a research area of Pathfinder Science.

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Activity: If you have not been involved with the PathFinder Science program (formerly KanCRN), you will need to register (http://pathfinderscience.net/teachers/registration/). The system is set up so that when you register we know where the data from research projects is coming from geographically- notice on the registration that it automatically finds that latitude and longitude of your school. Once you have finished the registration, go find your school on the Map of Participating Schools (http://pathfinderscience.net/maps/schools).

Activity: Begin the research project by looking at the research focus. http://pathfinderscience.net/so2/cexres_foc.cfm

Activity: Background information is located at http://pathfinderscience.net/so2/cbackground.cfm. There is a lot of good information and links from this page.

The protocol for doing the lichen survey is http://pathfinderscience.net/so2/cproto1.cfm.

The protocol online calls for 10 trees within 1 km of your school. For this course we need to alter the sampling protocol just a bit. The class will need for you to sample 30 trees that are geographically dispersed across your city or school attendance area. You do not need to collect bark nor determine bark pH (unless you want to). You also do not need to complete the tardigrade sampling part of this protocol. The data for lichen coverage on 30 trees will be required for later mapping analysis throughout the course. There are several ways for you to organize getting this data; therefore, it is absolutely essential that you collect the tree species and latitude and longitude for each tree you sample. You are welcome to do all thirty trees yourself; however, it would be great if you had students help collect the data. This can be done during class time or after school. It can be done by individual students or by teams of students. I find that teams of two work well for counting and recording. You could even send the protocol and counting grids home with students. Each student and their family could do one tree in their yard at home as family homework!

Collecting latitude and longitude can be handled in one of several ways. You can use a Global Positioning Satellite (GPS) unit to locate each tree; however, it is very important that the GPS receiver be setup correctly to collect this data. Please read the information on GPS use in classrooms located in the library. This project has a limited number of GPS units on hand here and we would be glad to loan you one for a week if that would facilitate your work. Please email, Dr. Steve Case at [email protected] with your request.

You can follow the protocol on the website using Topographic maps of your area to determine Lat/Long or, if your trees are near a street address (within 100 meters), you may use the TeleAtlas Latitude/Longitude Finder (http://www.geocode.com) (you'll need to register).

Once you have collected and recorded the data for all thirty trees, it can be submitted at http://pathfinderscience.net/so2/cdata_sub.cfm

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On the left side of the research project page http://pathfinderscience.net/so2/, there are a series of links under the title, Creating the Context. We are working our way through these links from top to bottom. Continue working down through these links, reading the material and submitting the information requested until you have completed all parts of the Creating the Context.

You are following a "road map" as you work on the lichen research. The following diagram is that "road map", otherwise known as a science research process guide. The research process model makes use of a modified and extended Vee-diagram (Novak and Gowin, 1984). The Vee-diagram below creates the scaffolding for organizing and discussing the process of scientific inquiry.

Research shows that most people believe that all scientific investigations adhere to an identical set and sequence of steps known as the scientific method (McComas, 1996). The Vee-diagram is a deliberate spatial exception to this perception of research being a very linear process. The modified Vee is flexible enough to span a wide variety of projects and allow for the representation of the research process across different science disciplines. It offers guidance without being overly prescriptive and helps teachers facilitate student work by providing a specific process structure, a road map, on which to base class discussion, reflection, and/or the explicit teaching of science process. The Vee-diagram helps students visually understand where they are in the process and how to proceed.

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Creating a Context for Scientific Inquiry

The Creating the Context Vee-diagram (the left of the two Vee-diagrams) reflects the view that good research questions emerges from a rich context of understanding. Creating a context for research questions is at the heart of our research activity on lichen. Scientists from every age and education level find it difficult to ask good research questions—questions that go to the heart of what we want to know. The activities and experiences organized around the Creating the Context Vee-diagram are a set of experiences that bring a much deeper level of understanding of the content and processes involved in the research area. The activities of Creating the Context include engaging background information, standardized methods of measurement, active experience in gathering and analyzing data, discussion about what is known and not known on a specific research area, and work with data to look for broad general patterns.

Asking a good research questions drives scientific research in this process model. In this process model, asking a research question happens in the middle! It is time to proceed on with the research. The next step is to explore the Guided Research Vee-diagram on the right, the Research Vee, represents the process driven by a very specific research question. Most students, and frankly most of us, have not engaged in scientific research. The task of creating a good research project, starting with a well framed question, seems too large and quite daunting.

Online, the Research Vee diagram becomes a Guided Research Area to provided for additional support to an authentic, meaningful research project. This structured inquiry allows researchers to engage in the research process as a tutorial, without the need to develop the entire process on their own. This can only be a support. Students must ask and seek answers to their own research questions. The Guided Research Vee begins by developing a research question: http://pathfinderscience.net/so2/gquest.cfm. Explore this page. Three quarters of the way down the page you are asked to submit your own research questions. When you were working on the Creating the Context activities you probably had interesting research questions emerge. This is the place to share them!

Activity: Each of you should submit at least one potential area of research (question) in this area.

Once you have contributed an interesting question, proceed down the links under Guided Research. The next link is Background Info. This page offers suggestions of where to go to find additional information, and it offers a place to share new information. Each of you should find new information relevant to the guided research question. This information should be contributed through this page. http://pathfinderscience.net/so2/gbackground.cfm

As you complete the activities of each of the links under the guided research, you should contribute the information you find. The process of the Research Vee Diagram is being driven by a research question. We "Created a Context" for research, and now we are now generating possible answers to our questions about the natural world.

The next step is Data Analysis. To begin Analysis, go to http://pathfinderscience.net/so2/gdata_analysis.cfm. This site contains information on geographic information systems (GIS). A GIS is a computer-based

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tool for mapping and analyzing data and their spatial relationships on the surface of the earth. The data that we have collected as a part of this project is well-suited to GIS technology because it has a critical geographic dimension. GIS integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps. These abilities distinguish GIS from other types of analysis.

Map making and geographic analysis are not new, but a GIS performs these tasks better and faster than we can by the old manual methods. And, before GIS technology, only a few people had the skills necessary to use geographic information to help with decision making and problem solving. A GIS stores information about the world as a collection of thematic layers that can be linked together by geography. This simple but extremely powerful and versatile tool can be applied to many real-world problems from tracking delivery vehicles, to recording details of planning applications, to modeling global atmospheric circulation.

Activity: Once you have contributed data, view the results of your study in a map-based format. Don't worry. We will take a closer look at your data with maps a bit later on in the course. (http://pathfinderscience.net/maps/lichen)

Once you have worked your way through all the links and completed all the element of the Guided Research, contribute your conclusions about your research. Share what you found out and make sure to answer the research question in your conclusion. I am sure that our group can have an interesting discussion of the conclusions we each reach.

Activity: Ask a question about one of the other class member's conclusion posted at http://pathfinderscience.net/so2/gconclusion.cfm.

University of Kansas Center for Science Education http://gis.kuscied.org



Chapter 2: The Nature of Science

Additional Reading: It would be beneficial to reference chapters 1 - 3 of: Carr, Joseph J. (1992). The Art of Science: A Practical Guide to Experiments, Observations, and Handling Data. Eagle Rock, VA: High Text.

Let's step out of our research activity and reflect on the process and connect it to instruction. Change is one of the most consistent factors of modern society. What the coming decades hold is difficult to imagine and impossible to predict. Today's students face a world that grows increasingly complex. Their futures will likely include dramatic changes that influence their schooling as well as the careers they pursue.

Educating our students has no higher purpose than providing a solid foundation for this future. Participating in quality programs will prepare students to lead personally fulfilling and socially responsible lives that encourage independent thinking and informed decision making.

Science education plays an integral role in preparing and providing students with the essential knowledge and skills necessary for scientific literacy to construct this solid foundation. "However, most Americans are not science literate" (Rutherford and Ahlgren 1990). The Third International Mathematics and Science Study (NCES 1996) bears this out. These studies indicate that the achievement levels of American tenth graders taking science from the 1970s through the 1990s were equal only to those of developing countries. Findings from the Third International Mathematics and Science Study-Repeat reinforce that little progress has been made toward the goals of scientific literacy (NCES 2000).

Why Is Science Important to All Students?

• First, science is important to students because an understanding of science addresses a fundamental human trait-curiosity: the need or desire to know. Science gives students a way to interpret what they see in the natural world; it provides a means for understanding that world and provides a vehicle for incorporating that understanding into their everyday lives. The study of science satisfies and validates students' curiosities and provides the answers to many questions.

Science is a quest or journey that expands the realm of students' knowledge in numerous ways. Some of these ways include presenting a system of active investigations, providing opportunities for students to use their senses, engaging students in observations, evoking a sense of wonder, and prompting more questions and further investigation.

Through investigations, questions are asked, and models or ideas are developed and tested. These models or ideas are accepted or refuted based on their abilities to predict the behavior of natural or human events. Science develops hypotheses that provide a context for understanding and predicting the behavior of nature. These hypotheses are based on facts

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collected through observation and experimentation. They are not wild guesses or unsubstantiated ideas; they are explanations of how nature works. Hypotheses change when observations contradict them.

Many other fields of study are based on the principles of observation, rational argumentation, and logic, but they do not include principles of prediction, experimentation, and validation of prediction. Science gives students the capacity to deepen their understandings of the natural world, thereby helping to bridge the gap between the realms of believing and knowing. Without the ability to know and confidently predict the behavior of nature, students would be left with only belief and superstition. An argument based on verifiable evidence is different from one based on metaphysics or other ways of knowing. Science allows students to know something with an acceptable level of certainty.

• Second, science is important to students because an understanding of science leads to scientific literacy, a prerequisite for functioning in modern society, and prepares students for their futures.

Science teachers use inquiry to accomplish three related educational purposes:

• to provide students with opportunities to think independently in order to obtain knowledge for themselves,

• to help students see for themselves how knowledge is formulated by collecting, organizing, and manipulating data,

• and to use inquiry to promote students' higher-order thinking skills such as analysis, synthesis, and evaluation.

The traditional K-12 science classroom has consisted of the teaching of well-established facts with little or no resemblance to the question-centered, collaborative practice of real scientists or to the science education outlined in the national standards. The inspiration for this question-centered, pursuit of real science comes from many sources. These include Piaget's metaphor of the child as scientist, Dewey's (1938) call for students to engage in projects sparked by their interest, and Bruner's (1963) challenge that an intellectually honest form of any subject can be taught to a child of any age. In an effort for students to do science, we have to understand what science does. To achieve these goals, science education must be re-oriented so that inquiry is not just an important new topic; it must become the "basic and controlling principle in the ultimate organization of science education."

In The Common Sense of Science, by Jacob Bronowski says, "In many scientific problems, the difficulty is to state the question rightly.... Science begins with the belief that the world is orderly: or better, that it can be made orderly by human arrangement" (1978, p 58). "Nevertheless, the idea of cause and effect has taken powerful hold on our minds.... This has become our natural way of looking at all problems" (1978, p 59). Using scientific inquiry allows teachers to introduce critical thinking skills. Lewis Thomas says, "I believe that the worst thing that has happened to science education is that the great fun has gone out of it.... Very few see science as the high adventure it really is, the wildest of all explorations ever taken by human beings, the chance to catch close views of things never seen before, the shrewdest maneuver for discovering how the world works" (1981). Using scientific inquiry allows the teachers and students to develop a better understanding of the nature of science, but even more importantly, it develops areas that allow teachers to be facilitators of learning for their students. "The development of a problem-centered approach to learning allows [teachers] to operate as a mediator, guide, provocateur, friend and co-learner with their students. Problem-centered

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learning allows many experiences and prior knowledge to come into play as students develop new constructs" (1981).

Using scientific inquiry as an organizing structure allows for the analysis of the learning potential of any situation. The instructor's role would be to help students find those situations that had the greatest potential for constructivist-based student learning. Science content is most meaningful when it is learned through the perspectives and methods of inquiry. The opportunity of using these tools will be using constructivism as a referent to integrate the knowledge of science, learning, and pedagogy to their classroom.

The National Research Council - Teaching Science as Research - Scientific Inquiry Engaging in the process of science, scientific inquiry is one way to construct a model of personal understanding of the natural world. Each of us tests that understanding against a perceived material reality. Since different and frequently faulty models of the world can be employed, securing accurate and meaningful knowledge can be difficult. Scientific research knowledge derives its value from the meaningful contributions it makes to our understanding of the material world. The process of science and the standards of evidence that have developed, result in testable, valid and useful knowledge.

Developing students' understanding of the scientific inquiry (the nature of science) is an objective of all high-quality science instruction. Recent efforts to reform science education in the United States have strongly emphasized this understanding as an essential component of general scientific literacy (AAAS, 1993, NRC, 1996). More specifically, the National Science Education Standards say that "science as inquiry is basic to science education and a controlling principle in the ultimate organization and selection of students' activities. The standards on inquiry highlight the ability to conduct inquiry as the primary way to develop understanding about the nature of science.

"Students at all grade levels and in every domain of science should have the opportunity to use scientific inquiry and develop the ability to think and act in ways associated with inquiry, including asking questions, planning and conducting investigations, using appropriate tools and techniques to gather data, thinking critically and logically about relationships between evidence and explanations, constructing and analyzing alternative explanations, and communicating scientific arguments."

The Inquiry Standard provides the following definition of scientific literacy: "Scientific literacy means that a person can ask, find, or determine answers to questions derived from curiosity about everyday experiences. It means that a person has the ability to describe, explain, and predict natural phenomena. Scientific literacy entails being able to read with understanding articles about science in the popular press and to engage in social conversation about the validity of the conclusions. Scientific literacy implies that a person can identify scientific issues underlying national and local decisions and express positions that are scientifically and technologically informed. A literate citizen should evaluate the quality of scientific information on the basis of its source and the methods used to generate it. Scientific literacy also implies the capacity to pose and evaluate arguments based on evidence and to apply conclusions from such arguments appropriately" (NRC 1996, 22). From this definition of scientific literacy, there are recognized attributes that describe scientifically literate people:

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• They use scientific knowledge, problem-solving skills, and informed attitudes in making responsible everyday decisions.

• They understand how society influences science, as well as how science influences society.

• They understand that society limits science through the allocation of resources.

• They recognize both the potential and limitations of science in advancing human and ecological welfare.

• They have a broad understanding of the conceptual schemes of scientific knowledge and are able to apply the understanding in various situations.

• They have an intellectual interest in science. • They are curious about the natural world. • They understand that new scientific knowledge results from inquiry that uses

prior knowledge. • They distinguish between scientific knowledge and personal opinion. • They recognize the origins of scientific knowledge and understand that

scientific knowledge is subject to change. • They have a richer, more exciting view of the world.

How Do Students Learn Science?

The following diagram illustrates a view of learning and of the learner in this environment of scientific inquiry.

Each individual learner is a part of a larger learning community. This learning community is engaged in authentic contexts and work using authentic tools. Authentic assessments provide information back to the learner about their individual

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progress toward their learning objectives. This learning community is supported by scaffolding, metacognitive reflection, technology and information resources, and collaboration. Scaffolding provides a model for guiding student inquiry and thinking. Metacognitive reflection and discussion helps learners frame and communicate their thinking. This collaboration extends beyond the immediate learning community and may involve partners in data collection or other aspects of actually doing the project. To achieve scientific literacy for ourselves and our students, it is important to consider findings in the research on how students learn.

Yager (2000) summarized Simpson's 1963 article on the nature of science as consisting of four facets:

1. Wondering about and questioning events and objects in the natural world 2. Offering explanations about the objects and events encountered 3. Designing experiments as a means of collecting evidence to see if

explanations have validity 4. Communicating the evidence collected to others with the hope that they will

agree with the explanation and accept the evidence provided.

Simpson's four facets of science still hold true today and account for much of how students should learn science. Learning in science is not just about reading textbooks or memorizing facts. Learning in science is about having the opportunity to rely on one's own knowledge to construct new scientific understandings and concepts. Learning in science is about being able to rely on prior knowledge and transferring knowledge gained to new situations. This clearly means that students should participate actively in the acquisition of scientific knowledge.

One area of research examining how students learn in science is called expert/novice studies. Although these studies are usually conducted with adults, Gobbo and Chi (1986) have examined the research with children who were categorized as experts or novices, based on knowledge. Their research has identified several important differences between the knowledge of experts and the knowledge of novices. First, as might be expected, experts have substantially more and deeper factual knowledge than do novices. More importantly, this knowledge exists in a conceptual frame. This means that experts think contextually, whereas novices tend to think by relying on surface and isolated facts. Second, experts group their knowledge more effectively than novices, thus, making it easier to retrieve when needed to solve domain problems, which experts can do better than do novices (Gobbo and Chi 1986).

Lowery (2000) agreed with Gobbo and Chi's findings when he applied expert/novice research to the science classroom. He stated that the typical student begins to learn science at the novice level and then, through the learning process, can become an expert learner on the science being taught. Lowery proposes three formats for learning science: hands-on, pictorial/ representational, and symbolic/narrative. Hands-on learning, says Lowery, allows students to use as many senses as possible. Pictorial/representational learning, sometimes referred to as passive learning, restricts the number of senses used by students, according to Lowery. He considers symbolic/narrative learning to be abstract and difficult for students because they typically can use only the sense of sight during the learning process. Science classroom activities where students are actively exploring science (hands-on science) provide opportunities for students to become experts on the science being studied. Pictorial/representational classroom activities include using videos or simulations to represent science for the students, which may or may not allow students to become experts in the knowledge being presented. Examples of a symbolic/narrative science activity is having students read science textbooks and

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memorize science content for a particular test or classroom event. This type of activity provides little or no opportunity for students to develop expertise in the science being studied. All three types of learning have a place in the science classroom, according to Lowery. However, he cautions that using only one type of learning may not give students the opportunity to become expert learners in the subject being studied.

Another area of research that examines how students learn is called learning and transfer. Research in this area found that students learned best when they had opportunities to experience and then transfer knowledge to familiar situations, because all new learning involves transfer. As a result of the idea of learning and transfer, attempts to make schooling more relevant to the subsequent workplace have also guided the use of case-based learning in business schools, law schools, and schools that teach educational leadership (Hallinger and Williams NRC 1999).

Learning and transfer research has direct implications for science education. Several critical factors must be considered: (1) Students learn best when motivated to spend the time needed to learn the complex subject matter found within science and to solve problems they find interesting. (2) The type of initial learning that engages students is a major factor in determining the development of expertise and transfer of the knowledge. (3) Students must be given opportunities to create and use the knowledge in a new situation. (4) Students must be given the time to learn for understanding and the opportunity to transfer this learning (NRC 1999).

Included in research on how students learn science is the work of Piaget. Piaget's research led to the constructivist philosophy prevalent in science education today. According to this philosophy, "learners bring their personal experiences into the classroom, and these experiences have a tremendous impact on students' views of how the world works" (Schulte 1996, 25). This means that knowledge exists within students and that they must be able to construct their own meanings and understandings when learning science concepts. The context for learning science is important for promoting and transferring knowledge. If students are to learn science this way, their metacognitions must be taken into account; they must be allowed to develop problem-solving tools for classroom learning, and, ultimately, they must have opportunities to transfer their understandings and skills to everyday environments.

Additional research on learners and learning includes that of Gardner's (1983) on multiple intelligences, which first appeared in his book Frames of Mind. Through his research, Gardner has hypothesized that human potential encompasses eight intelligence types: spatial, musical, kinesthetic, interpersonal, intrapersonal, verbal, mathematical, and naturalist. Information on how to apply Gardner's work in the classroom can be found in Multiple Intelligences in the Classroom by Thomas Armstrong.

Summative research on how students learn science is presented in the key findings from How People Learn: Bridging Research and Practice (NRC 1999).

Students come to the classroom with preconceptions about how the world works. If their initial understanding is not engaged, they may fail to grasp the new concepts and information that are taught, or they may learn them for purposes of a test but revert to their preconceptions outside the classroom (NRC 1999b, 10). The understandings that students bring to the classroom are based on experiences that are built from the moment a child is born and added to each day. Building from these personal experiences is an important aspect of learning. If these experiences are not related to the concept being studied, the student will learn the concept for a short

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period of time but not retain the concept (Ausebel, 1960). This is often the case when students do well on short unit tests but score poorly on culminating course exams. Often these personal experiences lead to misconceptions about science. Those student-held misconceptions must be revealed to the teacher. By understanding the students' misconceptions, the teacher can provide instruction to replace the students' incorrect understandings with correct understandings. If the teacher allows the students to draw from their experiences to reveal misconceptions, they can replace the misconceptions with the correct scientific understandings that will likely be retained. Making Sense of Secondary Science: Research into Children's Ideas is a publication dedicated solely to revealing students' misconceptions from international research. A Private Universe video and accompanying teacher workshop guide provide discussion topics for teachers of science on misconceptions that can develop in the science classroom. To develop competence in an area of inquiry, students must: (a) have deep foundations of factual knowledge; (b) understand facts and ideas in the context of a conceptual framework; and (c) organize knowledge in ways that facilitate retrieval and application (NRC 1999b, 12). The concept of students developing the ability to inquire within science emerges from research that encompasses the findings from expert and novice studies. Expert learners have the ability to draw from a richly structured information base, and they are able to make sense of facts and place them into a larger context. On the other hand, novice learners cannot complete this transfer of knowledge, and facts remain disconnected.

A "metacognitive" approach to instruction can help students learn to take control of their own learning by defining learning goals and monitoring their progress in achieving them (NRC 1999b, 13). "Metacognitive" activities are ones in which students are allowed to verbalize their thinking as they work, monitor their own understanding carefully, and make note of when additional information is required for understanding. Typically, metacognition takes the form of an internal conversation. For example, if the students know what they are attempting to learn or do, they can consciously assess whether learning is happening and in what way. It is often important and useful for the students to talk through this process. These "think aloud" protocols can also provide the teacher with valuable information about what and how the student is learning. This is the function of scaffolding (support) structures like the Vee diagram science process model. These kinds of structures allow thinking aloud as well as a common reference point for communication.

Students create and retain new knowledge when teachers design learning experiences in a manner that is consistent with how children naturally learn science. Through the acquisition of new knowledge, students can continually modify existing knowledge throughout their life. As more learning occurs, more concepts are modified and the learning (cognition of the learner) becomes more and more inclusive. As students move from grade to grade, science concepts become more highly organized and complex. Students are then able to think more abstractly and independently and formulate solutions to complex problems. Thus, students acquire the capacity to understand science and become scientifically literate citizens.

Thinking Question: What do you feel are the critical issues for classroom science teachers to understand about how their student learn and understand science?

Conclusion and Implications for Teachers Recent research shows that classrooms throughout a K-12 science program must be connected to a broader community (NRC 1999). This community includes homes, businesses, the geographic area a student is most familiar with, and the culture or cultures of that broader community. This community-centered approach to

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learning acknowledges that children learn from one another and that students bring knowledge from the broader community into the classroom.

Classrooms need to value the search for understanding in science. Learning happens in classrooms where the search for understanding is valued and students and teachers have the freedom to make mistakes in order to learn. In contrast, in a classroom where the teacher tolerates no mistakes or incorrect answers, a student's willingness to ask questions is hindered. Students fear asking questions when they do not understand the material, limiting their search for scientific understanding. However, when the teacher uses statements or questions that encourage students to develop new ideas and pose new questions, engagement and the search for a deeper understanding soon follow. Meaningful learning occurs in classrooms where students can explore and test the validity of their statements or beliefs and, at the same time, challenge misconceptions that they might have. Through experiencing these types of activities, students are more likely to truly learn essential science content and concepts.

Knowledge is a conceptual model through which the individual makes sense of the world (Sternberg, 1985). Constructivism emerged from the realization that pre-existing knowledge influences the way new knowledge is added to the individual's conceptual model, modifying its subsequent meaning (Stahl, 1991). Educators increasingly understand that private knowledge--the true conceptual framework of the individual--may differ considerably from the public knowledge of science.

Content knowledge consists of the concepts and relationships constructed through professional investigations in the natural sciences and the processes of scientific investigation. Therefore, the goals of formal education have shifted from the relatively straightforward process of transmitting information to the more complex task of facilitating development of a meaningful conceptual framework (Brophy, 1992). Because young children have less extensive personal models than adults, integration of new knowledge is generally improved when learning is concrete. As children mature, they develop a greater ability to operate in the abstract. However, there is considerable evidence to indicate that concrete learning is present well into the high school years and possibly into adulthood (Renner, Grant and Sutherland, 1978).

The need to relate new knowledge to familiar, and even personal, referents seems inherent in meaningful and creative learning. In science teaching, both at the K-12 and university levels, instructors rely heavily upon the abstract teaching methods of lecture and textbook readings supplemented by verification activities and laboratory demonstrations (Boyer, 1987; Dunkin and Barnes, 1986; Smith and Anderson, 1984). As a result, many students, at all levels, learn science superficially. Stepans et al. (1986) found that although older students can use more science terms than younger students, they may decline in their understanding of fundamental concepts. It appears that new knowledge, if poorly integrated, may actually be counterproductive.

A second major problem in many courses taught traditionally is their emphasis on rapidly learning large amounts of unintegrated factual information. Major concepts are poorly delineated from less important concepts, and few concepts are learned in depth. This is in contrast with an approach in which fewer, well-selected integrating concepts are carefully linked to form a framework for further learning.

A third problem lies in the division of knowledge, for convenience, into disciplines and fields. Such divisions may constrain the development of linkages among concepts across fields and so inhibit the development of an integrated cognitive

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model. Ball and McDiarmid (1991) point out that the outcomes of subject matter learning go beyond the substantive knowledge of the subject usually regarded as content knowledge. Students also develop an image of the subject that frames their dispositions toward it, in keeping with the well-known adage that the medium is the message.

The development of a clear, consistent integrating framework for science across disciplines is a stated national goal of science education. The National Science Education Standards, for example, outline a framework of unifying concepts and processes (themes) that underlies its model of knowledge in the natural sciences (National Research Council, 1996). These themes include: (a) systems, order and organization; (b) evidence, models and explanation; (c) constancy, change and measurement; (d) evolution and equilibrium; and (e) form and function. As an example of how these themes integrate subjects, consider how the theme of evolution and equilibrium unifies the concepts of equilibrium in chemistry, homeostasis in biology, geochemical processes in earth science, and thermodynamics in physics. In a similar vein, the "systems, order and organization" theme can, for example, unify concepts related to classification and the organization of knowledge in all disciplines. Other major concepts unify studies within more limited fields of study. For example, in biology, concepts such as adaptation, evolution, and community are important unifying themes.

The practice of separating subject matter content from the actions or processes from which it evolves has also been a concern of teacher educators. Many university science programs appear to regard laboratory experiences as ancillary to lecture, useful primarily to validate knowledge delivered by lecture and reading. Teachers who learn science didactically and abstractly cannot be expected to teach children constructively and concretely. Teachers who have never conducted investigations and research are unlikely to model investigative behaviors for their students. Teachers should have significant substantial and ongoing involvement in laboratory, including active inquiry research, that goes beyond traditional validation activities. Investigative projects should require formulation of research questions, development of procedures, implementation, collection and processing of data, and the reporting and defense of results.

Nature of science refers to:

• Characteristics distinguishing science from other ways of knowing. • Characteristics distinguishing basic science, applied science and

technology. • Processes and conventions of science as a professional activity. • Standards defining acceptable evidence and scientific explanation.

Thinking Question: What is an activity that you do with your students that engages them in real, authentic, meaningful science?

References Armstrong, Thomas. (1994). Multiple Intelligences in the Classroom. Alexandria, Va.: Association for Supervision and Curriculum Development.

Ausebel, D.P. (1960). The use of advance organizers in the learning and retention of meaningful verbal material. Journal of Educational Psychology (51), 267-272.

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Ball, D. L. & McDiarmid, G. W. (1991). The subject-matter preparation of teachers. In National Research Council, Moving beyond myths; Revitalizing undergraduate mathematics (pp. 437-447). Washington DC: National Academy Press.

Boyer, E. (1987). College: The undergraduate experience in America. New York: Harper and Row. Carlsen, W. S. (1991). Effects of new biology teachers' subject-matter knowledge on curricular planning. Science Education, 75(6), 631-47.

Brophy, J. (1992). Probing the subtleties of subject-matter teaching. Educational Leadership, 49(7), 4-8.

Bronowski, J. (1978). The Common Sense of Science. Cambridge: Harvard University Press. [Amazon.com]

Dunkin, M. J. & Barnes, J. (1986). Research on teaching in higher education. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 754-777). New York: McMillan.

Gardner, Howard. 1983. Frames of Mind: The Theory of Multiple Intelligences. New York: Basic Books.

Gobbo, C., and M. Chi. 1986. How Knowledge is Structured and Used by Expert and Novice Children. Cognitive Development 1: 221-37.

Lewis, T. 1978/1997. The Lives of a Cell: Notes of a Biology Watcher. Penguin. [1978 | 1997]

Lowery, Lawrence F. 1998. The Biological Basis of Thinking and Learning. Berkeley, Calif.: FOSS.

Lowery, Lawrence F. 2000. Presentation at the Cutting Edge Conference, 13-15 July at Chippewa Falls, Wis.

Madrazo, Gerry M. Jr., and Jack Rhoton. 2001. Principles and Practices in Multicultural Science Education: Implications for Professional Development. In Professional Development Leadership and the Diverse Learner, Arlington, Va.: National Science Teachers Association Press.

NCES (National Center for Education Statistics). 1996. Pursuing Excellence: A Study of U.S. Eighth-Grade Mathematics and Science Teaching, Learning, Curriculum, and Achievement in International Context, Initial Findings from the Third International Mathematics and Science Study (TIMSS). Washington, D.C.: U.S. Department of Education.

---. 1998. Linking the National Assessment of Educational Progress and the Third International Mathematics and Science Study: Eighth-Grade Results. Washington, D.C.: U.S. Department of Education.

---. 2000. Highlights from the Third International Mathematics and Science Study- Repeat (TIMSS-R). Washington, D.C.: U.S. Department of Education. NRC (National Research Council). 1996. National Science Education Standards. Washington, D.C.: National Academy Press.

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---. 1999a. How People Learn: Brain, Mind, Experience, and School. Washington, D.C.: National Academy Press.

---. 1999b. How People Learn: Bridging Research and Practice. Washington, D.C.: National Academy Press.

Renner, J. W., Grant, R. M. and Sutherland, J. (1978). Content and concrete thought. Science Education, 62(2), 215-221.

Rutherford, F. James, and Andrew Ahlgren. 1990. Science for All Americans. New York: Oxford University Press.

Schulte, P.L. 1996. A Definition of Constructivism. Science Scope 20(3): 25-27.

St. John, Mark. 2001. Presentation to the Council of State Science Supervisors, 26-28 January at Washington, D.C.

Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14.

Smith, E. L. & Anderson, C. W. (1984). The planning and teaching intermediate science study: Final report (Research series no. 147). Michigan State University, East Lansing MI: Institute for Research on Teaching.

Stahl, R. J. (1991, April). The information-constructivist perspective: Application to and implications for science education. Paper presented at the annual meeting of the National Association for Research in Science Teaching, Lake Geneva, WI.

Stepans, J. I., Beiswenger, R. E. & Dyche, S. (1986). Misconceptions die hard. The Science Teacher, 53(9), 65-69.

Sternberg, R. J. (1985). Human intelligence: The model is the message. Science, 230(4730), 1111-1118.

Wisconsin Department of Public Instruction. 1993. A Guide to Curriculum Planning in Science. 1986. Reprint. Madison, Wis.: Wisconsin Department of Public Instruction.

---. 1998. Wisconsin's Model Academic Standards for Science. Madison, Wis.: Wisconsin Department of Public Instruction.

Yager, Robert. 2000. Real-World Learning: A Necessity for the Success of Current Reform

Efforts. ENC Focus 7 (3): 18-19. Summarizing from Simpson, G.G. 1963. Biology and the Nature of Science. Science 139(3550): 81-88.

Additional Reading NCES (National Center for Education Statistics).1998. Linking the National Assessment of Educational Progress and the Third International Mathematics and Science Study: Eighth-Grade Results. Washington, D.C.: U.S. Department of Education.

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NRC (National Research Council). 1999. Global Perspectives for Local Action, Using TIMSS to Improve U.S. Mathematics and Science Education. Washington, D.C.: National Academy Press. Misconceptions in Science: Appendix A.

Driver, R., Ann Squires, Peter Rushworth, and Valerie Wood-Robinson. 1994. Making Sense of Secondary Science. Research Into Children's Ideas. London: Routledge.

Annenberg/CPB Corporation. 1995. Math and Science Collection. A Private Universe video and The Private Universe Teacher Workshop Guide. Harvard Smithsonian Center for Astrophysics at Harvard University.


http://gis.kuscied.org/



Chapter 3: Using Project-Based Learning to Support Classroom Scientific Inquiry

Science teachers ask a simple and powerful question about new instructional methods and materials: what works? This very practical question comes from a deep commitment to see their students successfully reach their learning goals. It is also driven by a high stakes testing environment in which classroom teachers know that student achievement will be reflected in the assessment of their teaching. Teaching within these boundaries can make adopting reform-based curriculum or materials feel risky. With the publication of the National Science Education Standards in 1995, science education reform efforts have stressed that developing students' understanding of the process of science is an objective of all high quality science instruction. The National Science Education Standards emphasize that, "Science as inquiry is basic to science education and a controlling principle in the ultimate organization and selection of students' activities" (NRC, 1995, p. 105). This emphasis requires a significant and yet unproven change in classroom instruction. Using the Project Based Learning (PBL) curriculum model to develop scientific inquiry is one way to organize the Standard's vision within a science classroom. Scaffolding student learning around this vision requires the use of a specific process structure, a roadmap to science process, for both students and teachers. Using this process model to organize a collaborative research network has supported changes in instructional behavior and resulted in improvement of student performance.

Project Based Learning Project based learning is instruction organized around a number of activities that lead to the production of a product. The September 1918 Teacher College Record published an article called, "The Project Method", in which William Kilpatrick develops a definition for project based learning. Even then he states, "The concept is not in fact newly born." Kilpatrick, through his advocacy of the project method, launched one of the most successful implementation efforts of PBL by starting curriculum development with student interests, then bringing in subject matter incrementally as it was relevant to pursing those interests. For Kilpatrick (1918), "purposing, the expression of the child's own interest in pursuing some activity, remained the essential first step in the curriculum-making process." Even with this fairly long history as an instructional technique it has never been widely implemented.

Recent research in cognitive psychology and learning tend to support Kilpatrick's opinion about the efficacy of this teaching technique. Drawing from another part of The National Research Council book, "How People Learn," cognitive science research indicates that "these investigations will provide fertile ground where their students can transfer their learning to multiple contexts. Learning that only occurs in a single context will become inert except within that context" (NRC, 1999). The Northwest Regional Lab (1997) embraces this notion of constructivist learning theory by stating, "Project-based learning engages students in complex, real-world issues and asks them to acquire and apply skills and knowledge in a variety of contexts." The National Research Council reinforces this connection to constructivism: "Problem-centered learning allows many experiences and prior knowledge to come into play as students develop new constructs" (NRC, 1999).

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Unfortunately a simple definition for PBL has lead to the criticism that it is unclear what exactly constitutes a project and how specifically instruction should proceed. In order to engage students in the linkage between project based learning and standards based scientific inquiry, students and teachers need an organizational framework. This framework must offer highly specific support and guidance, a roadmap of sorts, through the process of scientific inquiry. This process structure creates the scaffolding necessary for learning and an organizing framework for communication, reflection, and discussion of the process. The modified and extended Vee-diagram creates the scaffolding for organizing the process of scientific inquiry.

Additional Reading: Chapter 7 of Joseph Carr’s The Art of Science. See references.

Asking a good research questions drives scientific research in this process model. The V-diagram on the right, the Research Vee, represents the process driven by a very specific research question that emerged from the activities of Creating the Context.

The Vee process structure can be applied to the organization of many project-based learning projects that cross discipline areas and span grade levels. Teachers, acting as facilitators, help students developed research tied to the content curriculum. As I am sure you have seen on the PathFinder site, there are many projects already online (http://pathfinderscience.net) available for student participation. All of these projects follow the science process model. Current projects cover a vast array of topics: “Keeping An Eye on Ozone” uses plants to check out local ground level ozone readings; “Tardigrades” explores the world of these tiny "water-bears" of moss and lichen; “UVB and DNA” investigates the links between the sun’s harmful rays and cell damage; “Global Warming” tracks the changes in carbon dioxide by counting the stomata of leaves; “How Does Your Cookie Crumble” explores science process by deciding which commercial cookie brands hold up the best; “Out! Darn Spot!” works with getting pigments and dyes in and out; “Digital Monarch Watch” explores butterfly migration; “Lichens and SO2” helps create an understanding of the environmental impacts of Sulfur Dioxide on the density and diversity of lichens; “African-American Immigration” studies a historical immigration across the United States; and “Driving Me Crazy” looks at how fast are those cars really going by the school.

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These projects are exciting and an interesting way to beginning working within this process model. They may not, however, meet local needs. An online Project Builder allows classroom teachers to work within the Vee process structure to create their own project. A project developed by the students and teachers can meet curricular needs while being relevant to the students.

Does it work? It is absolutely necessary to answer this very practical question that classroom teachers ask. A dissertation study was conducted that explored what factors support changes in teacher's instructional behavior. The study showed that the impact that a science and technology network, like PathFinder Science, had on the teaching behaviors of the elementary (K-8) science teachers who had participated in the network for two years was significant. The findings, regarding change in science teaching behaviors as well as implications for future science teacher professional development, are substantial. In this study, teachers reported changes in instructional behavior as a result of the support offered; these changes were verified through classroom observation (Carroll, 2001).

In our work we have found that in order to engage students in meaningful and authentic scientific inquiry, they must have an organizational structure that offers both guidance to process and scaffolding to learning. It is important to ask if participation in this inquiry model provides compelling evidence of an increase in student achievement. More importantly, does the ability to apply these skills transfers to novel situations? In a recent study of student learning in the Kansas City Kansas Public Schools, a typical but small urban school district, the use of the research model was explored. A wealth of student data was available from the district-wide systemic reform effort. Since longitudinal measures of student attitudes are generally difficult to obtain, this study tapped into this wealth of attitude measures of ongoing district work. Using the statistical technique of Structural Equation Modeling, multiple learning variables were combined with participation in the research model. This study created and tested a model of science classroom variables related to scores on a science performance assessment. Models were run separately for samples of middle school students (grades 6-8) and high school students (grades 9-12). The middle school model indicates that participation in the KanCRN research model is an independent, positive, direct, and meaningful predictor of science performance (Case 2001).

Given a supporting environment, teacher instructional behavior will focus around scientific inquiry as the organizer of student activity. Students who engage in this research process and who are taught content in the context of process will show an increased understanding of science and an increased ability to transfer inquiry skills to new situations.

References: Carr, Joseph J. (1992). The Art of Science: A Practical Guide to Experiments, Observations, and Handling Data. Eagle Rock, VA: High Text.

Carroll, T. (2001). Change in science teacher behaviors: Evaluating the impact of a collaborative Learning network at the level of practice. Dissertation: University of Kansas.

Kilpatrick, W. H. (1918). The project method. New York: Columbia University Teachers College.

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McComas, W. (1996) Ten myths of science: Reexamining what we think we know about the nature of science. School Science and Mathematics. 96, 10-16

National Research Council. (1999) How People Learn. Washington, DC: National Academy Press.

National Research Council. (1996) National Science Education Standards. Washington, DC: National Academy Press.

Northwest Regional Educational Laboratory. (1997). Integrated workplace learning project. Portland, OR: NWREL Education and Work Program.

Novak, J.D.& Gowin, D.B. (1984). Learning how to Learn, London, Cambridge University Press TERC, (1996) Ed., National Conference on Student and Scientist Partnership (SSP) Conference Report, TERC Communications, 2067 Massachusetts Avenue, Cambridge, MA 02140.





Chapter 4: Student Scientist Partnerships - Meeting Everyone's Goals!

In the context of his welcoming remarks to the 1996 National Science Foundation planning conference on Student Scientist Partnerships, Dr. Neal Lane, NSF Director, said, "What I mean by integration of research and education is quite simply insuring that student learning benefits by being embedded in a research environment, a research culture in the very place where inquiry and discovery takes place" (TERC 1996). The organizing structure facilitates communication between students and scientist about scientific process and allows each to focus on their own goals. These partnerships provide a completely new way to do research that can be as potentially powerful for the researcher as they are for science students. School-based research can provide the capacity for worldwide data collection, ranging from simple observation to advanced research study. School-based research can make changes in scale to both temporal and spatial elements of data collection, enabling scientists to extend their research in new ways and produce research findings that would otherwise be impossible. In order to insure that the educational goals are met, it is necessary that student work be done within the larger context of generating new knowledge of the physical world. This helps them link new knowledge to what they already understand. Teacher and student's goals frequently revolve around understanding the basic science involved in the research. These questions can keep the research grounded on the basic science that creates the broader context of their research. If student research questions that emerge from their work are tangential to the work of the scientist/researcher, these questions can be the focus for the student's own research interests.





Chapter 5: A Link to the Social Sciences

It is important to realize that not all problem solving is scientific; neither is all project-based learning scientific inquiry. The standards are fairly specific about scientific inquiry; however, many of us are trained in the social science of education. There can be confusion about the nature and organization of research between the disciplines. The nature of the scientific research that we are proposing to do in schools can lead to an interesting interaction with the social sciences. The model below is an opportunity to distinguish social science process from scientific process and demonstrate that interaction.

Every time you turn on the news or pick up a newspaper or magazine to read, there is another story about the latest pending global disaster. Often based on current research, these stories speculate on global warming, decreasing ozone, too much ozone, increasing levels of ultraviolet light, acid rain, habitat loss, loss of biodiversity, decreasing water quality and the effect of increasing human population. All these stories create such a jumble of information that the knowledge can get lost. Instead, for most people, a simple message emerges: these problems are difficult to work on and appear to be overwhelming us. It is easy to have a dark view of the future as we face this jumbled mess of information. People tend to be problem-solvers who want to fix things. In this case we cannot help but wonder: what can one person do to fix these overwhelming, difficult, global issues? Faced with such problems many people have chosen to become environmental activists. Starting with the original Earth Day in 1970, they look to social action to cause change in the way human beings behave toward the planet. After all, who does not want clean water and clean air? The Clean Water Act and Clean Air Act were soon passed, and the Environmental Protection Agency was founded.

Looking back we realize that while this activism was very important, it is not enough as we continue to approach global problems. It is necessary to take a step beyond being an activist and become a decision-maker who collects valid information, analyzes that information and then develops an action plan based on that information. It is quite easy to confuse the "research" aspects of the sciences and social sciences. They are in fact very different disciplines. Education is a social science, and a relatively young social science at that. While the research methodologies of the two are different, they are not unrelated. The diagram below illustrates an interaction between the Sciences and the Social Sciences, which may help keep the differences clear in students' minds.

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www.pathfinderscience.com

This diagram illustrates the relationship in which the science problem-solving cycle and the social problem-solving cycle interact. In turn, each cycle continues to drive the other. If we believe our research has produced something of significance and of social value, our report should state the value claims as well as our knowledge. "The practice of science supposes the existence of a real and a common world, and assumes that its impact on each individual who is part of it is modified by him in a way which constitutes his/her personal experience. We do not construct the world from our experiences; we are aware of the world in our experiences. Science is a language for talking not about experience but about the world" (Brwonowski, 1976). In this model, social problem-solving is based on scientific knowledge. Teachers should, of course, be explicit in making a distinction between the two problem-solving cycles for their students. This kind of reflection will help students more clearly distinguish science process from other problem-solving. Social actions (activism to solve socio-cultural problems) are determined by the social group and are distinct from findings of scientific knowledge cycle. However, with social action is based on findings of the scientific knowledge cycle then citizens and students can move from being just activists, to being effective decision-makers.

The Values discussion emerges from discussion. In these discussions, we have found that there are value questions and categories that are helpful in starting the talk and the subsequent social problem-solving cycle.

• Instrumental value claims take the form, Is X good for Y? • Intrinsic value claims take the form, Is X good? or is X something society

values? • Comparative value claims take the form, Is X better than Y? • Decision value claims take the form, Is X right? • Idealized value claims take the form, Is X as good as it can be?

These value claims are useful to begin discussion under classical and personal values in the cycle above. These should take the form of reflections and begin the brainstorming for possible social solutions to social problems. It is important to note that as potential solutions emerge from these discussions, they can be implemented and tested by the science problem cycle. This feedback allows us to continuously propose and progress in our understandings.





Chapter 6: The National Science Education Standards

Standard - Science as Inquiry

The Standards not only brought scientific inquiry into its own content area, but they also distinguish scientific inquiry from inquiry instruction. As we have discussed, the Inquiry Standard gives vision to classroom scientific inquiry by stating, "Students at all grade levels and in every domain of science should have the opportunity to use scientific inquiry and develop the ability to think and act in ways associated with inquiry, including asking questions, planning and conducting investigations, using appropriate tools and techniques to gather data, thinking critically and logically about relationships between evidence and explanations, constructing and analyzing alternative explanations, and communicating scientific arguments" (NRC, 1995, p. 105). This Standard requires that student's have the "abilities necessary to do scientific inquiry" and "Understanding about scientific inquiry." One interesting aspect of the Inquiry Standard is that it includes the processes of science but extends to requiring that students combine scientific knowledge within process.

Content Standards - Physical science, Life science, Earth and space science

We take the approach that the scientific knowledge of the physical sciences, life sciences, and the earth and space sciences are organized and contextualized by the process of scientific inquiry. The learning theory already presented indicates that students learn more, retain knowledge longer, and transfer that knowledge to novel situation better when that knowledge is presented in context of scientific inquiry. Using project based learning for organizing scientific inquiry means that the specific scientific knowledge learned will depend on the project research focus. As illustrated by the current projects on the PathFinder Science site, projects can range across disciplines, requiring different background knowledge. These projects provide fertile ground where students can apply their learning in complex, real-world issues. It requires students to acquire and apply skills and knowledge in a variety of contexts.

Content Standards - Science and technology

The standard requires that student's have abilities in technological design and understanding of science and technology. In this project based learning approach we use technology, including GIS, as a tool to engage in science process. This allows students to develop a clear understanding of similarities and differences of technology and science

Content Standards - Science in personal and social perspectives

It is important to realize that not all problem solving is scientific nor is all project-based learning scientific inquiry. The model illustrating the interaction of the social science process from scientific process is intended to help students understand the interaction of the two disciplines. Hopefully, the model will help students

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conceptualize each process as being separate but interacting. It should be used instructionally to help student move from information consumers to becoming information producers and information decision makers.




Module 2: Foundations of GIS in K12 Education

It has been suggested that some technologies not only facilitate, but also encourage the use of PBL and scientific inquiry models in the classroom. Data analysis technologies, such as Geographic Information Systems, when used in a classroom setting, are accompanied by a shift in teaching practices from teacher-centered to student-centered instruction.


Module 2: Foundations of GIS in K12 Ed

Chapter 1: What is technology and why is it needed in classrooms?

What is technology? As an application of human understanding, technology is most often defined as "a rational discipline designed to assure the mastery of man over physical nature, through the application of scientifically determined laws" (Simon, 1983, p. 173). In more common language, technology is largely considered to be any "innovation" with an economic impact derived from science. "Technology" can also refer to human processes, managements, control mechanisms, or even the organizations that oversee innovative developments, such as agriculture groups or the military (Finn, 1960; AAAS, 1993).

Extending technology into education brings forth the term, "educational technology". On the whole, educational technology refers to the devices and systems of scientific research that attempt to improve any aspect of the educational process. Educational technology then, "is a complex, integrated process involving people, procedures, ideas, devices, and organization, for analyzing problems, and devising, implementing, evaluating and managing solutions to those problems, involved in all aspects of human learning" (AECT Task Force, 1977, p. 164). Unlike educational technology, instructional technology is used to refer to devices and systems determined through scientific research that attempt to improve teaching and learning (Engler, 1972; Armsey & Dahl, 1973).

Most definitions suggest that the key difference between instructional and educational technology lies in the scope of the application of the technology. If the technology is explicitly intended to enhance classroom teaching and learning, it is considered instructional technology. However, if a technology has more expansive applications, such as software for assigning student class schedules, bus routes, or reporting grades, it is likely to be considered educational technology. The delineation between these two concepts is not a dichotomous one, often allowing for overlap and integration between technologies and terms.

The need for technology in the classroom Technologies that allow for the manipulation, exploration, or representation of knowledge or data are seen as essential to the success of many constructivism-based instructional models, including Project Based Learning (PBL) (NRC, 1999; Eggen & Kauchak, 2001). These technologies are viewed as invaluable to students' investigatory processes as they "allow building the kinds of more intimate, supportive, learning environments called for by the constructivist perspective" (Perkins, 1987, p 54).

For example, PBL programs in middle school science have been developed using CD-ROMs to deliver instruction. Studies investigating these uses of technology-supported PBL claim gains in content knowledge, when compared to more traditional models of instruction (Williams, Hemstreet, Liu, and Smith, 1998).

It has been suggested that some technologies not only facilitate but also encourage the use of PBL and scientific inquiry models in the classroom. Data analysis

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technologies, such as Geographic Information Systems, when used in a classroom setting, are accompanied by a shift in teaching practices from teacher-centered to student-centered instruction (NRC, 1999; Kerski, 2000; Bednarz, 2000a). For example, when teachers are new and uncomfortable with data analysis technologies, they are more likely to use technologies in the most risk-free environment from a technological, pedagogical, and behavior management view. As teachers become increasingly confident with the software, the learning environment, the content, and behavior management practices, they tend to shift to greater student use of technology for answering inquiries. Kerski (2000) and Bednarz (2000b, 1999) have reported the shift to higher-order, open-ended inquiries exemplifies the uses of technology-supported PBL.

A standards-based approach to technology in the class The National Science Education Standards (NRC, 1995) and the Benchmarks for Scientific Literacy (AAAS, 1993) call for systemic change throughout science education, providing for environments that allow students to construct their knowledge through personal and social interactions with meaningful scientific experiences. These experiences are intended to be composed of inquiry-driven events, wherein students investigate questions of personal significance using scientific methodologies as a framework and technology to augment and extend discovery. The culmination of science education in public schools is ultimately the development of students who are scientifically literate.

The Geography for Life National Geography Standards (GESP, 1994) call for the education of geographically informed students who are able to ask and answer geographical questions such as "where is?" and "why there?" The use of geographic inquiry in the class is intended to be the primary method for developing students who are able to proceed through five stages of asking and answering geographic questions. Asking geographic questions, followed by acquiring, organizing, and analyzing the information, allows a student to answer the geographic inquiry, usually resulting in a new set of questions and subsequent inquiries (GESP, 1994; AAG & NCGE, 1984). Information technology, more specifically a Geographic Information System (GIS), is mentioned in one appendix of the standards. While the discussion is minimal, "the standards were written with geographic information systems in mind but not in sight" (p 257). The standards note the expense and gradual adoption of GIS technology in schools.

The National Educational Technology Standards for Students - Connecting Curriculum and Technology (ISTE, 2000) provides a general description of linkages between curriculum and technology for K-12 education. NETS profiles what a technology literate student is able to accomplish at any particular grade range and includes performance indicators and curriculum examples in English, math, science, and social studies. NETS articulates a vision where classroom technology is used to augment traditional teaching models while simultaneously supporting new student-centered, authentic learning environments, rich in multi-sensory and multi-path, collaborative inquiries.

Thinking Question: What role does technology play in your standards-based instruction? Do you use classroom technology as tool that supports or drives instruction and curriculum?

GIS to extend learning and science

As an instructional technology, a Geographic Information System (GIS) allows students to record, manipulate, and output data that contains a geographic coordinate. While a substantial degree of scientific data contains a geographic

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coordinate, science classrooms all to often downplay or completely overlook the spatial significance of the data. Geography holds an important position in the history and development of scientific thinking. It was the geographic proximity of related species that led Alfred Russell Wallace to conclude, "Every species has come into existence closely coincident both in space and time with a pre-existing closely allied species" (Wallace, 1855). Wallace's geographic conclusions brought to light one of the most basic patterns of nature, biologic evolution. Data with spatial components is as critical to understanding gene flow as it is to understanding the range and distribution of species. In the physical and earth sciences the spatial nature of data is equally important; molecular modeling and geologic time each require a precise understanding of spatial relationships through time. Geography is a critical element of good science and GIS is a critical tool in the technology toolbox science students and teachers should have at their disposal.

Additional Reading: Read Let GIS Be Your Guide by Baker and Case (Appendix A).

References

American Association for the Advancement of Science (AAAS). (1993). Benchmarks for science literacy. New York: Oxford University Press.

Armsey, J.W. & Dahl, N.C. (1973). An inquiry into the uses of instructional technology. New York: Ford Foundation Report.

Bednarz, S.W. (2000a). Connecting GIS and problem based learning. In R. Audet & G. Ludwig, GIS in Schools. Redlands, CA: ESRI Press.

Bednarz, S.W. (2000b). Problem based learning and GIS: PBL-GIS. Presented at Educational Applications of GIS 2000, San Bernadino, CA.

Eggen, P.D. & Kauchak, D.P. (2001). Learning and teaching, research-based methods (4th ed.). Boston, MA: Allyn and Bacon.

Engler, D. (1972). Instructional technology and the curriculum. In F.J. Pula and R.J. Goff (Eds.), Technology in education: Challenge and change. Worthington, OH: Charles A. Jones.

Finn, J.D. (1960). Technology and the instructional process. Audiovisual Communication Review, 8 (1), 9-10.

Geography Education Standards Project (GESP). (1994). Geography for life: National Geography Standards 1994. Washington, DC: National Geographic Research and Exploration.

International Society for Technology in Education (ISTE). (2000). National educational technology standards for students: Connecting curriculum and technology. Eugene, Oregon: ISTE Press.

Kerski, J.J. (2000). The implementation and effectiveness of geographic information systems technology and methods in secondary education. Ph.D. dissertation. University of Colorado at Boulder.

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National Research Council (NRC). (1999). How people learn: Brain, mind, experience, and school. Washington, D.C.: National Academy Press.

Simon, Y.R. (1983). Pursuit of happiness and lust for power in the technological society. In C. Mitcham & R. Mackey (Eds.), Philosophy and technology. New York: Free Press.

Wallace A. R. (1855), On the Law which has Regulated the Introduction of New Species. Annals & Magazine of Natural History, 26:184-196.

Williams, D.C., Hemstreet, S., Liu, M., & Smith, V.D. (1998). Examining how middle school students use problem-based learning software. Proceedings of the ED-MEDIA/ED-Telecom 98 World Conference on Educational Multimedia and Hypermedia, Frieburg, Germany.





Chapter 2: What is a GIScience?

GIScience seeks to discover the general principles and theories that emerge from the systematic study of the nature and use of geographic information and to model their use in the context of geographic information systems (Goodchild 1992, 1999). All relationships, processes, and attributes of geographic information, as well as its users and GIS itself, fall within the scope of GIScience. GIScience attempts to better describe the principles of geographic knowledge and the extension of that knowledge through research, theory building, and the application of technological tools. GIScience embraces the technical developer, the geographer-researcher, and the end user with equal interest.

As a field first coined over a decade ago, GIScience stands as the nexus of digital cartography, cognition, remote sensing, and quantitative methods, while establishing an epistemological paradigm for future generations. It has emerged as the users of GISystems have generated questions about basic issues spurred on by ever-changing technology. Issues of scale, precision, the nature of representation, point of view, change over time, and uncertainty have largely been driven by the use of technology in geographic information. While GIScience as a field does not suppose to answer these issues, it is creating a framework and model for their exploration and discovery.

Optional Resource: Explore the links to current research areas in GIScience at: • University Consortium for GIS: http://www.cobblestoneconcepts.com/

ucgis2summer2002/researchagendafinal.htm • The Varenius Project:

http://www.ncgia.ucsb.edu/varenius/initiatives/ncgia.html • Thinking Spatially by S.W. Bednarz and The Biological Basis on Thinking by L.F.

Lowery (Appendix A). What is G.I.S?

A Geographic Information System (GIS) is a tool for spatial data analysis that utilizes color scales or other symbology to represent variables on a map (NRC, 1999). A GIS can serve as an effective instructional technology for promoting data collection and analysis in scientific inquiries for students (Baker, 2002; Bednarz, 2000b). The typical display of a GIS portrays an image where layers represent distinct components or types of information. These layers can be added in any sequence the user prefers, and based upon the data available to the user, various analyses can be performed on those data. Such analyses may include determining densities, distances, size, proximities, inclusions, exclusions, and commonalities (ESRI, 1995).

As Geographic Information Systems evolve, largely due to recent hardware and software developments, the field has yet to arrive at a single, descriptive definition of GIS. From research and theory emerge three dominant definitions, each exercising a unique perspective on GIS and its functions. The first is the toolbox view, emphasizing GIS as an information system capable of input, processing, and output (Parker, 1988). A database or information system approach to defining GIS describes the system as largely composed of a "computer based set of procedures" for handling geodata (Arnoff, 1989). An organization-based definition of GIS typically

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includes "professionals integrating technology" or the use of "decision support systems," computer applications using spatial data to solve a specific problem (Carter, 1989; Cowen, 1988). Over a decade after its formal proposal, the view of GIS as a toolbox seems to still entertain the majority of users, defining GIS as a tool that allows for the collection, storage, analysis, and display of geographic data (ESRI, 1995).

Many of the earliest developments of GIS can be seen in the pursuits of the Harvard Laboratory for Computing Graphics, a Ford foundation project, established in 1966 by Howard Fisher. Fisher wanted to automate mapping using typewriter symbols that could print overstrike characters creating a variety of shading effects. Unfortunately, few cartographers appreciated the aesthetics of the output of the resulting application, SYMAP. It was this attempt at automated mapping that first tinkered with integrating computing technologies in cartography (Slocum, 1999). Shortly after SYMAP, Harvard Lab efforts produced another automated mapping application, GRID. It was from GRID that many modern GIS applications developed, including ESRI's ArcInfo and ERDAS's Imagine. Each of the companies that develop these applications now stands as a leader in the field of Geographic Information Systems (Clarke, 1999).

The pursuit of GIS technology has continued, with a recognized need for continued emphasis on the topic. As the development and gathering of new geographically related information continues, the field of geography has concluded that a general science of geographic information technology must be established (Goodchild, 1992). Hailed as geographic information science, or GIScience, this emerging multidisciplinary field is proposed to address "the generic issues that surround the use of GIS technology, impede its successful implementation, or emerge from an understanding of its potential capabilities" (Goodchild, 1992).

With continually improving hardware, software, training, and many freely available data sets, widespread support and adoption of GIS for problem solving and spatial analysis is occurring in diverse industries. The GIS industry, including education, government, and business, has an estimated valuation of $7 billion dollars annually in hardware, software, and services (Geoplace, 2001).

Today, we commonly see Geographic Information Systems used in a variety of fields and activities for the display of data with geographic components. In a single GIS trade journal, we can readily find examples of GIS in diverse fields such as oceanography, law enforcement, anthropology, utility companies, environmental resource mapping, economics, emergency response routing, public transportation, and pipeline industries (ESRI, 2001a).

Data availability and expansion. Since 1994, the Federal Geographic Data Committee (FGDC) has spurred the use and adoption of the National Spatial Data Infrastructure, a set of spatial standards, data, and metadata. The intent of the FGDC was largely to support a common framework for the use and exchange of data. These efforts were later reinforced by the development of the Spatial Data Transfer Standard to support an open data format and expanded metadata guidelines including origin, resolution, and developmental history.

Obtaining ready-made data at an appropriate scale, projection, and content matter is no longer the challenge it has been in the past. Not only has the federal government increased the number of media types used for dissemination, including CD-ROMs and DVDs, private companies have also started reformulating data formats and projections for easier use by the novice. Many GIS-based websites now feature free

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data downloads for every county in the United States, typically sporting dozens of different data sets per county. Topo!, another easy-to-use data set provided by National Geographic, offers seamless topographic maps for the continental U.S. integrated into the ESRI ArcView GIS. In some cases, where proprietary data types possess great benefit, viewers and translators are provided free or at a reduced fee. LizardTech's MrSID viewer is one such application for viewing highly compressed, remotely sensed data. Such viewers and tools are now commonly built-in to many GIS packages.

Training and education The need for qualified GIS analysts is as equally dramatic as the rise in GIS use. Major university programs, community college certifications, and online training are expanding to fill the demand for qualified professionals. In 1999, Clarke suggested, "Just about every major academic institution in the United States and in many other countries now teaches at least one class in GIS" (p. 6). While there is no national GIS curriculum, the National Center for Geographic Information and Analysis (NCGIA) has created a core curriculum for developing GIS skills for analysts. GIS certifications, largely offered through community colleges, have also experienced massive growth. The ESRI Higher Education web indicates that within the past few years, community college programs have grown from 20 to over 300 (ESRI, 2001b). Finally, online courses have recently experienced substantial growth. To answer increasing need, GIS courses have been enhanced by distance learning experts to create Internet-based courses (Green, 2001). ESRI's Virtual Campus is a prime example, boasting over 100,000 enrolled students in nearly 180 countries. Learning modules and full courses at the Virtual Campus focus on GIScience, GIS Technology, and GIS Applications. Similarly, other GIS manufactures such as MapInfo and ERDAS also have some elements of online training available through their corporate websites.

Internet-based GIS The Xerox Palo Alto Research Center was first responsible for attempts at online mapping. In 1993, the center launched the Map Server, a geography-based information retrieval system, where all the underlying code was painstakingly developed by hand using early primitive web markup and programming languages (Harder, 1998). Later that same year, the University of Tromso in Norway created an online map of web servers in that country. At this pioneering stage in the web, it was considered a source of national pride to have and exhibit Internet capacities (Plewe, 1997). The Virtual Tourist mapping application, one of the largest geographic indices of the late 1990s, was later launched based upon the work in Norway. Other early online mapping projects were undertaken, including the USGS's GeoData for viewing and downloading GIS data and the U.S. Census Bureau's statistical data called the TIGER mapping service. Today, many commercial entities exist just to produce online maps, including MapQuest (http://www.mapquest.com), MapBlast (http://www.mapblast.com), and the TerraServer (http://terraserver.microsoft.com), an Internet-mapping application that tiles aerial and satellite photography into maps for viewing or downloading.

Internet mapping, unlike many forms of traditional desktop GIS, possesses many advantages. The user interface of an Internet-based map service can be configured to hide unused or inappropriate features, while features commonly used in the map can be accentuated. In essence, the map designer is empowered by not only designing the map, but also the agents and interface cues for user interaction. Secondly, Internet-based mapping can more easily disguise the complexities of data formats, projections, and scale. Dependencies and requirements can be coded into the system to avoid the display of inappropriate data under certain circumstances. Thirdly, user collected data, including attributes and commentary, can be added to

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existing datasets through Internet-based mapping systems, as the designer allows. This removes complexities of database structure and design, data entry, and representation that exist in a desktop system. Finally, in some Internet-based mapping networks, such as the Geography Network (http://www.geographynetwork.com/), data can be shared among and between map servers with data conversion and transport occurring seamlessly and hidden from the user. These features, when combined, allow users to build their own Internet-based maps, using data from any locale in the world, while placing their own data collection atop it all It is for these reasons that Internet-based mapping applications are experiencing growth and present themselves as excellent environments for supporting student learning.

With the onset of Internet-based, interactive mapping in the late 1990s, another law of growth may be applied to current and future GIS use. In general, new technologies have greater value as more people use them. Robert Metcalfe, founder of 3Com Corporation, predicted that the usefulness of a network would equal the square of the number of network users. In practice, this suggests the more people that use a network application, the more likely new users are to be drawn into similar usage patterns (Downs & Mui, 1998). This idea becomes even more relevant as Internet-based GIS increase in use and popularity.

Collaborative GIS As PBL often requires collaboration among multiple schools, data commonly originate and are analyzed by many students at different times and from different locations. Because of this, desktop GIS is difficult to use as a support tool of PBL. However, the use of collaborative GIS or Internet-based GIS applications, which focus on a single issue and allow for dispersed, real-time data entry and representation, may serve non-geography classrooms very well. It is highly appropriate for science classrooms to engage science with collaborative GIS, reducing software, data, interface, and content complexity. Exhaustive time expenditures are no longer needed, as any student and teacher capable of using a web browser can quickly and successfully use a collaborative GIS. Ultimately, this allows science teachers and students the time and energy to direct attention to the heart of the question directing the PBL (Baker & White, 2003).

Thinking Question: Why use GIS in the K12 setting? It requires substantial time, effort, and technology. Are there better solutions?

References

Arnoff, S. (1989). Geographic Information Systems: A Management Perspective. Ottawa, Canada: WDL Publications.

Bednarz, S.W. (2000a). Connecting GIS and problem based learning. In R. Audet & G. Ludwig, GIS in Schools. Redlands, CA: ESRI Press.

Bednarz, S.W. (2000b). Problem based learning and GIS: PBL-GIS. Presented at Educational Applications of GIS 2000, San Bernadino, CA.

Carter, J.R. (1989). On defining the geographic information system. In W.J. Rippler (ed.), Fundamentals of Geographical Information Systems: A Compendium. Falls Church, VA: ASPRS/ACSM. Pp. 3-7.

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Clarke, K.C. (1999). Getting started with geographic information systems. Upper Saddle River, NJ: Prentice Hall.

Cowen, D.J. (1988). GIS versus CAD versus DBMS: what are the differences? Photogrammetric Engineering and Remote Sensing, 54, 1551-4.

Downes, L. & Mui, C. (1998). Unleashing the killer app, digital strategies for market dominance. Boston, MA: Harvard Business School Press.

Environmental Systems Research Institute (ESRI). (1995). Getting to know desktop GIS. Redlands, CA: ESRI Press.

Environmental Systems Research Institute (ESRI). (2001b). The Community Atlas. Retrieved August, 2001 from http://www.esri.com/industries/k-12/atlas/

GeoPlace. (2001). GIS software revenue tops $939 million in 2000. GEOResouces. November 16, 2001.

Goodchild, M.F. (1992). Geographic information science. International Journal of Geographical Information Systems 6 (1), 31-46.

Green, D.R. (2001). GIS in school education: you don’t necessarily need a microcomputer. In D. Green (Ed.) GIS: A Sourcebook for Schools. London: Taylor and Francis.

Harder, C. (1998). Serving maps on the Internet. Redlands, CA: ESRI Press.

National Research Council (NRC). (1999). How people learn: Brain, mind, experience, and school. Washington, D.C.: National Academy Press.

Parker, H.D. (1988). The unique qualities of a geographic information system: a commentary. Photogrammetric Engineering and Remote Sensing, 54, 1547-9.

Plewe, B. (1997). GIS online: Information, retrieval, mapping, and the Internet. Santa Fe, NM: OnWord Press.

Slocum, T. (1999). Course notes for GEOG 558: Principles of geographic information systems. Lawrence, KS: Kansas University Press.





Chapter 3: How GIS has been used in K-12 education

[Source: TR Baker. (2002). Dissertation. Abridged.]

The successful implementation of any systemic movement requires support, organization, and communication far beyond the classroom walls. Many events, organizations, grant programs, and companies have provided a variety of unique support for the growth and continued success of GIS in K-12 education. The chronology below briefly describes some of the efforts that have had the most substantial impact upon GIS in K-12 education.

1986 Devised in 1986 by the National Geographic Society, the Geographic Alliance is a network that aims to bring geography teachers together to promote scholarly communications and activities. Principally, each state has a local alliance, primarily tasked with promoting geographic literacy for all people (NGS, 2001). Although activities vary from state to state, the alliances strongly support GIS training for teachers, idea sharing through newsletters, and forming collaborative partnerships for data and further teacher training. The National Geographic Society is also the sponsor of Geography Awareness Week. Held in November of each year, this program recognizes the importance of geography to students and the public.

1988 In 1988, the National Center for Geographic Information and Analysis (NCGIA) was founded from a consortium of U.S. universities with a mission of advancing geographic information research. This mission, indirectly, included education and public outreach activities designed to help meet the increasing demand for trained GIS professionals. Two years after its inception, NCGIA released the Core Curriculum, intended to provide a scope and sequence for GIS education at the undergraduate level. Despite the pioneering efforts of the Core Curriculum, it was largely regarded as a product that was hurried to market and as a result lacked appropriate pedagogical, cognitive, or affective components. The Core Curriculum reportedly was analogous to reading a "military training manual" (Audet, 1993, p. 7). While some consideration was given to adapting this curriculum to the high school level, it was largely regarded by classroom teachers as too dense and too technical for supporting classroom instruction (Palladino, 1998). As a result, the Secondary Education Project (SEP) was launched in 1992 with the dual purposes of identifying existing GIS activities for schools and creating new ones. The birth of SEP I, consisted of a prototype teacher institute, held for the development, testing, and dissemination of K-12 education materials (Palladino, 1994). NCGIA was unsuccessful at immediately securing funds for the continuation and development of the SEP I program. The results of the early program are likened to a series of compromised solutions, constrained time frames, and hints of waning pedagogical clarity (Audet, 1993).

1989 Following the discovery of the RMS Titanic in 1989, the JASON Project used the Internet, printed curriculum, video, and

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hile is.

teleconferencing technologies to bring explorations in science, technology, math, and social studies to students. In this project, maps and GIS technology are used to support general student exploration of a yearly theme. In many cases, GIS technologies are also used by project staff and presented to students for enhancing the unit of study (JASON, 2001).

1992 The Center for Image Processing in Education (CIPE) is a nonprofit organization founded in 1992 that attempts to train teachers and students in the use of data visualization tools, including image processing packages and geographic information systems. CIPE has also produced extensive training manuals and curriculum that follow approaches similar to Project Based Learning. To date, CIPE has over 30 certified instructors who have trained in excess of 3,500 teachers in image processing or geographic information systems. CIPE uses ArcView GIS software for teachers and students (CIPE, 2001).

1994 The development of any field within academia or industry is typically marked by a surge of professional meetings and conferences; GIS in K-12 education is no different. In January 1994, the first annual conference on the pre-college educational applications of Geographic Information Systems was held at the National Geographic Society. Heralded as EdGIS, the conference was a great success and has grown substantially in subsequent years with participants from education, the cognitive sciences, geography, GIS, remote sensing, government, and industry.

1995 The Environmental and Spatial Technology (EAST), initially supported by the University of Arkansas' Center for Advanced Spatial Technologies, is a collaborative of high schools, companies, and governmental agencies. The collaborative claims over 150 participating schools, focusing on a performance-based, service learning model while integrating GeoMedia GIS and other scaffolding technologies. Since 2000, EAST has sponsored student conferences, which now boast over two thousand students.

The Visualizing Earth (VisEarth) project, a 1995 collaboration between Pennsylvania State's GeoVista program and the Technology in Education Research Consortium (TERC), had middle school students analyzing remotely sensed and aerial photography data. The project included a cooperative partnership with the NASA-sponsored EarthKAM, a movable camera within a spaceship controlled by students across the Internet. The outcome of this effort has produced some of the latest studies in spatial cognition and recommendations for teaching. Areas of research include scale, point of view, 3-dimensionality, symbolic representation, and change over time (Barstow, Frost, Liben, Ride, & Souviney, 1999).

1997 The Kansas Collaborative Research Network (KanCRN) was created in 1997 as an Internet-based network of schools designed to facilitate student research in the natural sciences. Unlike programs before it, KanCRN involved students as researchers, not simply research assistants. In this way, KanCRN created online environments capable of allowing students to collect, contribute, and analyze data. The use of GIS in the organization, wrooted in ArcView GIS, quickly extended into Internet-based mapping and analysKanCRN was one of the first educational organizations in the world that supports real-time mapping of student-generated data in a complete framework for scientific research utilizing a variety of desktop and Internet-based GIS solutions.

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1998 ESRI created the ESRI Virtual Campus, the largest and arguably the most successful online environment for learning about GIScience, GIS technology, and industry-specific applications. The campus has over 160,000 students from 180 countries enrolled in courses.

1999 World Resources Institute (WRI) collects and distributes large amounts of biodiversity and environmental data. In the fall of 1999, WRI and ESRI published an ArcView GIS extension for schools called DataScape. The application contains over 450 data layers that students and teachers can use independently or to which they can add their own class-collected data.

As a collaboration between ESRI, the National Geographic Society, and the Association for Geographic Information, GIS Day is held annually as a part of Geography Awareness Week. Initiated in 1999, the intent of GIS Day is to educate students and the general public about GIS. Typically, GIS specialists from business and government visit classrooms and demonstrate the technologies, lead tours, or assist in other ways related to GIS awareness. Aside from these traditional GIS Day activities, Internet web casts and map galleries have been created to support student and teacher awareness (GISDay, 2001).

2000 As a part of the Earth Observing System's Education Center, the Lewis and Clark Education Center (LCEC) provides students and educators with historical and present-day vision of the natural and cultural aesthetics of the Lewis and Clark Trail. The program, founded in 2000, uses multimedia and geospatial data to create a rich context of understanding. Through the use of GIS, global positioning systems, and a variety of remotely sensed data sources, the center is creating animations, fly-throughs, and a vast data and document archive. The center plans to sponsor a number of events in 2003, the bicentennial of the expedition, including a national conference and trail trips (Philp, 2001).

2002 The US State Department and the Association of American Geographers in partnership with several other organizations sponsored an international competition, My Community, Our Earth (MyCOE). The focus of MyCOE was to direct student attention on sustainable development throughout the world by empowering learners through geographic knowledge and technology. The MyCOE project had over 500 volunteer mentors, 200 submitted student projects, and over 2,000 people make formal inquiries into the project. Although the project was largely considered to be successful, its future remains uncertain, given its initial development was intimately tied to a decennial conference, the World Summit on Sustainable Development.

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References

Audet, R.H. (1993). Developing a theoretical basis for introducing geographic information systems into high schools: Cognitive implications. Ph. D dissertation. Boston University, School of Education.

Barstow, D., Frost, E., Liben, L., Ridge, S., Souveiney R. (1999). Final report to the National Science Foundation: Visualizing Earth. Retrieved August 2001 from http://visualizingearth.ucsd.edu

Center for Image Processing in Education (CIPE). (2001). The center for image processing in education. Retrieved August 2001 from http://www.airedesign.com/CIPESite/

GISDay. (2001). GISDay. Retrieved August 2001 from http://www.gisday.com

JASON Project. (2001). The Jason Project. Retrieved August 2001 from http://www.jason.org

National Geographic Society (NGS). (2001). Education at National Geographic. Retrieved August, 2001 from http://www.nationalgeographic.com/education/

National Center for Geographic Information and Analysis (NCGIA). (2001). The National Center for Geographic Information and Analysis. Retrieved August, 2001 from http://www.ncgia.org

Paladino, S. (1994). A role geographic information systems in the secondary schools: An assessment of the current status and future possibilities. Retrieved March, 1999 from http://www.ncgia.ucsb.edu/~spalladi/thesis/title.html

Philp, A. (2001). The Lewis and Clark education center. Retrieved August 2001 from http://lewisandclarkeducationcenter.com

All art are trademark icons from listed references or web pages.





Chapter 4: Why Use GIS?

While some research has been conducted to show the benefits and costs of using GIS in the K12 classroom, it is largely agreed that GIS can benefit students through at least one of three ways: increasing marketable workforce skills, promoting spatial reasoning, and serving as a tool for data analysis. Secondarily, some research and anecdotal reports have suggested that GIS can aid in promoting problem solving, a sense of place, collaboration skills, and information literacy.

GIS in the classroom offers great promise for supporting problem solving and problem-centered instruction. It extends the abilities of teachers and students to differentiate and categorize problem-solving styles of students. Novice GIS users tend to exhibit particular characteristics when attempting to solve a problem with a GIS. The characteristics include the tendency to exhibit trial and error strategies, to act on incomplete or unverified knowledge, and to rely on personal interpretations of syntax (Audet, 1993). Audet's research is one of the first attempts at identifying student-to-GIS interactions and student problem solving with GIS. Furthermore, GIS terminology and concepts play a significant role in the acquisition of GIS skills (Audet & Abegg, 1996). The implications for such research indicate that targeting the user interface and reducing system complexity will likely better support early student learning. As such, collaborative GIS applications are likely an ideal platform from which Audet's conclusions can be extended, as collaborative GIS applications allow for a greater degree of customization and honing to the student research.

Many other distinct advantages of GIS technology exist for K-12 students. Spatial literacy and geographic competence, defined as the ability to recognize the location of map points and attributes, are two such advantages. Interpersonal skill development fostered through cooperative grouping and an enhanced "sense of existence of the wider world" often follows from the proper implementation of GIS instruction. Finally, the understanding of scale and resolution seems to be a critically important task for students, most readily nurtured through the use of GIS (Mackaness, 1994).

GIS allows students the ability to piece together apparently unrelated data into a coherent whole, allowing for integration of multiple subject areas in a real world context (Green, 2001). GIS supports the latest findings in teaching and learning; it extends students' abilities to ask and answer questions related to authentic situations (Bednarz, 1999). This helps to encourage a questioning attitude, in part, to support specific and transferable skills (Coggins, 1990). Finally, the use of GIS in classrooms promotes information literacy and technical skills applicable in and out of the classroom (Green, 2001).

Workforce Justification [Source: Bednarz, IGU Finland]

Additional Reading: Thinking spatially: Incorporating geographic information science in pre and post secondary education, by S.W. Bednarz. IGU Finland. (Appendix A) Much of this reading is included below as the Workforce and Spatial Thinking justifications.

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Many of the arguments for teaching GIS in elementary and secondary schools use workplace skills (employment opportunities) as the most important justification. The position paper written to establish the Thinking Spatially committee supports this justification, citing "concerns about the capacity of the American workforce to compete successfully in a context defined by intense international competition in global markets and rapid technological change in the nature of the work process." The paper presents GIS as a decision support system able to manipulate an increasingly important commodity, spatial data; likewise, the paper links GIS to the need for "knowledge workers" able to collect data, calculate and analyze data, and communicate knowledge, stating, "A GIS can both support and help implement the critical thinking skills that are central to creating smarter workers" (Committee on Geography, 2000). In the influential document, NCGIA Core Curriculum, Goodchild and Kemp (1990) rationalized incorporating GIS into pre-collegiate education because 1) it is a key tool to analyze the environment and solve problems, particularly at the local scale; 2) it enhances student interest in geography and related subjects; and 3) GIS is an attractive technology capable of motivating students to careers in science and engineering. At the post secondary level, the growth and proliferation of GIS certificate programs--on-line, web-based distance learning GIS modules such as ESRI's Virtual Campus, and related self-teaching opportunities for "busy professionals"--further illustrate the workplace value of GIS. The inclusion of GIS in pre- and post-secondary geography education is thus justified to meet workplace needs and to smooth the school-to-work transition.

The educative justification may be more appropriately within the realm of geography and science education. While providing students with key technological skills is a worthwhile and legitimate concern of education, educators are cautious about serving a merely essentialist role providing trained knowledge workers for the Information Economy as opposed to preparing educated, spatially-skilled individuals. A review of a partial list of skills mastered by students using GIS (Table 2) indicates that a large number are related to the technology of GIS and not to anything inherently geographic. The question that needs to be considered relates to time. Given the limited amount of time in the curriculum for subject matter, is it worthwhile to spend it mastering GIS?

General computer skills Learning Skillsfile management downloading and uploading Internet-based data data manipulation (unzipping, saving, printing, formatting data)

working in teams to tackle real world problems creating reports and presentations communication with peers

Database Skills Geographic, Cartographic, and Visualization Skills

classifying data differently observing the results on maps sorting, querying, creating and populating new fields

Image & network analysis clipping, unioning, dissolving reprojecting data symbolizing points, lines, areas creating a map layout three-dimensional analysis skills

Table 1: Skills addressed by the use of GIS

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Spatial Reasoning [Source: Bednarz, IGU Finland] For geographic educators the most important and powerful argument for incorporating GIS into the curriculum is its purported ability to enhance spatial thinking skills. In the United States, the National Geography Standards (1994) specifically encouraged the inclusion of GIS in pre-collegiate education, noting that GIS could be used to enhance students' geographic skills and ability to think spatially.

There are three dimensions of spatial thinking: spatial visualization, spatial orientation, and spatial relations (Golledge and Stimson, 1997, p.158). Spatial relations, listed in the left hand column of Table 1, are the aspects of spatial thinking most often developed in geography and earth science classrooms.

Spatial Relations Processes used in cognitive mapping and GIS

Abilities (skills) that recognize spatial distribution and spatial patterns Identifying shapes Recalling and representing layouts Connecting locations Associating and correlating spatially distributed phenomena Comprehending and using spatial hierarchiesRegionalizing Comprehending distance decay and nearest neighbor effects in distributions (buffering) Wayfinding in real world frames of reference Imagining maps from verbal descriptions Sketch mapping Comparing maps Overlaying and dissolving maps (windowing)

Constructing gradients and surfaces Layering Regionalizing Decomposing Aggregating Correlating Evaluating regularity or randomness Associating Assessing similarity Forming hierarchies Assessing proximity (requires knowing location) Measuring distance Measuring directions Defining shapes Defining patterns Determining cluster Determining dispersion

Table 2: Spatial Thinking Skills

Spatial relations parallel closely the processes used by individuals to develop cognitive maps, shown in the right hand column of Table 1. Cognitive maps are the store of knowledge an individual has about environments organized as internal models of the world. Cognitive maps are the basis of both spatial and non-spatial decision-making. They are produced by the interaction of spatial relational data, spatial thinking processes, and environmental attributes as filtered through perceptions, beliefs, values, and attitudes. It has been suggested that cognitive maps are an internalized geographic information system. "It is difficult to think of a single functionality embedded in the GIS that does not have a parallel in human information processing capability" (Golledge and Bell, 1995 cited in Golledge and Stimson 1997, p.236).

Thus, it is asserted that GIS helps students to learn geography and science content by practicing spatial thinking such as associating and correlating spatially distributed

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phenomena and enhancing cognitive mapping skills such as assessing similarity and proximity. A publication showcasing examples of GIS in US and Canadian schools states, "GIS is a powerful analytic tool that helps people understand the significance of spatial distribution patterns, whether the issues involve the sitting of a new professional sports stadium, animal migratory patterns, or designs for cost-effective school bus routes" (Audet and Ludwig, 2000, p.4). However, research has not confirmed the connection between spatial relational skills and GIS instruction. A review of research in the US on measures of effectiveness of GIS concludes that "much of the potential is garnered from intuition or related more broadly to the combination of technology and constructivist learning environments" (Keiper, 1999, 49). Attempts to measure GIS versus paper mapping effects have resulted in little substantive difference between groups on spatial reasoning tasks (Kerski, 2000), while similar studies measuring student data analysis have found modest yet significant improvements for those using GIS (Baker, 2002; Crabb, 2001). While it seems intuitive that GIS will compliment the development of key spatial skills, it is not yet proven.

Additional Reading: The biological basis of thinking and learning, by L.Lowry, (1998). Lawrence Hall of Science, University of California. (Appendix A)

Data Analysis For well over two decades, scientists in biology, geology, and environmental studies have used geographic information systems to examine field data. Many of the earliest applications of GIS were driven by the need for representation of natural phenomena, such as mapping home ranges of species, exposed sediment and soil depositions, known point-source pollutions, and other intertwined natural events. Throughout the course of time, science has continued to

erwise rely on geographic data as a linchpin between othdiverse and complex data sets for theoretical and practical applications.

As an example of practice, let's look at the International Crane Foundation (http://www.savingcranes.org/), an organization that works to conserve cranes and the wetland and grassland communities that sustain them. Focusing on educational and conservation activities, the ICF maintains a headquarters near Baraboo, Wisconsin where a small collection of cranes is maintained for captive breeding and reintroduction into the wild. At a local level:

"work is proceeding with a project to model the habitat requirements of Greater Sandhill Cranes (Grus canadensis) near Briggsville, Wisconsin. Sandhill populations in Wisconsin are in the midst of an impressive recovery, from 15 to 20 pairs in the entire state in 1928 to 2,883 observed breeding pairs in 1995. Unfortunately, growing crane populations in a time of shrinking wetlands have led to conflicts between humans and birds….. The problem could be mitigated, though, by predicting where such conflicts are likely to occur and taking steps to alleviate the problem while it was still small. To this end, we are constructing a model of the habitat needs of Sandhill cranes and attempting to come to a more detailed knowledge of how their use of agricultural fields interacts with their

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need for wetlands. Years of field observations of the locations of banded, breeding cranes will be overlaid on a series of detailed land-cover maps of the Briggsville area….. We are still in the process of creating a land-cover map of the area. Our base is the road and stream network, drawn from Digital Line Graphs (DLGs) in the US Census Bureau's 1990 digital TIGER line files. Using this as ground control, we are rectifying 1992 National Aerial Photography Program airphotos from which we digitize the boundaries of fields, forested areas, human structures and other land-cover categories. The result of this is a land cover template which can be updated for each year with specific crop information and relevant changes in land use. Such year-specific modifications are based upon field observations and annual Agricultural Soil Conservation Service (ASCS) air-photos. Crop reports supplied to the US Farm Service Agency may be used as ground-control to estimate our accuracy in classification, while wetland distribution has been obtained separately from the Wisconsin Digital Wetland Inventory (WDWI)" (ESRI, 2002).

Just as a conservation biologist can use a GIS to extend meaningful, authentic science, so too can students use GIS to engage in real scientific tasks with legitimate learning outcomes for student scientists. For example, a student studying tardigrade density and diversity across his local town (http://pathfinderscience.net/tardigrades) would map the collection of organisms in a GIS. The student's data points are then placed atop other geographically-referenced data sets including local hydrology, elevation, urbanization, land use, roads, and railways. Each dataset may lend unique and exciting clues that help the student to better analyze with the precision and analytical capacity of a GIS. Using this particular scenario, a student could likely find a relationship between tardigrade density and diversity as a function of urbanization and (potentially) commuter traffic. In this real-life example, an eighth grade science student was able to identify a preliminary, generalized pattern of tardigrade diversity as a function of urbanization. The resulting map uses a Digital Orthophoto Quarter Quadrangle, land cover (not visible), hydrology, elevation (not visible), a line of transect through town, and the green data points (where larger circles represent greater density of tardigrades).

The ways in which a science teacher may use GIS typically vary dramatically from ways in which the technology is used by geography teachers. Whereas many geography teachers seek to teach place-names or, perhaps, geographic inquiry, science teachers are generally interested in the technology due to its purported ability to extend science by helping to better manage data and to provide new ways of looking at or visualizing the data. Imagine a high school teacher engaged in stream monitoring with her 10th grade students, all 132 of them! Every other week, the teacher marches the class out to the stream behind the school and divides the class in five teams, each measuring nitrates, phosphates, and dissolved oxygen as well as examining for the presence of ten biotic organisms, all during a blocked class period. Over the course of two days, she is able to get her five classes to the stream, with her students collecting a total of (13 samples x 5 groups x 5 class period) 325 pieces of data. By the end of the year, our biology teacher now has ((36 weeks x .5 sampling frame) x 325 pieces of data per event) 5850 pieces of data. By the end of the fourth year, the teacher will have a grand total of 23,400 pieces of data! As the teacher's students are only able to sample over a fairly small section of the stream, the teacher takes every opportunity to share data with other biology teachers up and down stream, adding even more pieces of data to mix.

Many science teachers also have students collect information about organisms near the school or students' homes. While in some cases, students are tasked with creating a presence/absence map of a particular species across a region, other

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situations demand that students participate in collecting data as a species migrates through a locale. As the location of particularly species can change daily, being able to map dynamic data is often very important to science. The Journey North, The Monarch Watch, and PathFinder Science support student collection of data related to migratory species.

We most often see GIS and similar data analysis technologies used in the science classroom when data sets are complex, large, or dynamic. Using a collaborative GIS, we can add community or collaborative data sources to list of reasons for using a GIS in science. Today, GIS continues to be used to support and drive scientific investigations with extremely powerful computers, sophisticated modeling applications, and real-time, dynamic rendering of diverse data. Science continues to heavily draw upon geographic-based representation of data in a subfield called "scientific visualization."

The Standards also call for the application of various data analysis technologies to extend student inquiries. Technologies, such as GIS, are advocated in science education literature, identifying these technologies with the capacity to "represent data in new ways… [allowing scientists to] discern patterns more quickly and detect relationships not previously noticed" (NRC, 1999, p 215). Science seeks to explain the natural world, often by describing repetitions and patterns. The use of data with a geographic component provides a valuable opportunity to encourage students to observe, describe, question, and explain patterns found in nature. Whether it is a fractal, global fault lines, or the distribution of salamanders across California, patterns can instigate and direct meaningful student research. While the notion of "pattern seeking" is quite appealing to scientists, it is more appropriately described in spatial reasoning language as an adaptation of spatial relations, where correlations, clusters, and regionalization of data become an amalgam of cognitive, scientific, and geographic processes. In short, pattern seeking is the application of spatial relations to form a generalized statement of proximity and relationship between data.

Some organizations have minimally addressed pattern-seeking activities in science curricula. Activities Integrating Math and Science (AIMS) has released some instructional resources (Wiebe, 2001) but has not extensively addressed the issues. Similarly, the Full Option Science System (FOSS) from Lawrence Hall of Science provides a brief white paper that addresses developmental issues and related pattern seeking processes (Lowry, 1998). Pattern seeking in science, as a central focus of PBL and as a necessary cognitive tool for digital mapping, must be addressed with supporting materials to develop both students and instructors.

When using geographic data analysis, it is often helpful to have specific and identifiable subsets of pattern seeking. Breaking down pattern seeking activities makes them easier to define, teach, and assess. Using Mitchell's key components of geographic data analysis, science students and teachers can more completely utilize and assess geographic data analysis. Mitchell's key components of geographic data analysis include:

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• Where are things? - This element includes conducting searches for a specific point or points meeting identified parameters, such as, where is Ann Tubal's sample taken? Where does 29th Street and Kensington Avenue intersect?

• High-Averages-Lows (HAL) - This element denotes the need for maximums, minimums, determining averages, and potentially mapping bivariate color schemes. Example questions may include, where is the highest recorded sample of ground level ozone in Manhattan City?

• Density is Represented - The frequency of samples (and resulting map representations) is considered. Clusters of samples or density of samples across a region are considered. The relationship between density of samples and resulting interpolations or uncertainty within the data area considered. For example, why are error indices smaller near highly populated areas on many regional weather maps?

• Assessing Inside/Outside Elements - This element could include determining boundaries or regions in which data express commonalities. For example, how are lichen densities decreased inside of areas defined as urbanized? How does elevation differ in Wyandotte County versus Saline County, KS?

http://gis.kuscied.org/online/course_contents/mod2/img/1100penn.bmp

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• Assessing proximities - This element includes measures between a point or points and other nature features. The measures may be between points or between a point and an identifiable feature in another data layer. For example, students may be asked to identify and derive the distance to the nearest EPA Environmental Risk site.

Only a few educational research efforts have attempted to address geographic data analysis in the science classroom. Baker's 2002 dissertation found that GIS can extend student's scientific inquiries but concluded that explicit, prerequisite pattern-seeking or data analysis activities were needed to help students more fully leverage and frame GIS as a tool for data analysis.

"In this study, GIS was particularly suited to the exploration and analysis of large datasets collected by a great numbers of students. The speed and accuracy with which a GIS can represent large datasets makes it a very valuable tool for classrooms working within a network of schools focused on a single question or issue. The use of distributed GIS applications or Internet-based mapping contains great potential to move students quickly beyond the practice of mapping where things occur and immediately launches them into determining why things occur. This type of analytical activity is a substantial goal of the Standards and appears to be dramatically improved through the use of GIS technologies to support problem-driven, collaborative research in schools.

"During the observations, the researcher noted that without additional instruction, students at the eighth grade are capable of targeted searches using a GIS, essentially extracting information from the GIS about a particular point or points. This process was also repeatedly observed in the paper-mapping classrooms, however the extraction of specific point-based information was a much slower task.

"Students in this study did not appear to be sufficiently capable of creating generalizations across a series of data points, engaging in basic pattern seeking and explanatory activities. While the GIS-based PBL students were rapidly able to generate maps indicating where sampling events occurred, they were unable to make generalized statement about the trends in the data. Paper-based mapping students where also not able to perform these generalizations. It is likely that if all students had engaged in pattern-seeking activities prior to the onset of the unit, substantial differences may have been realized between the GIS and paper-map supported classes. These results would be expected as the GIS mapping group was able to quickly generate maps, presumably allowing for more time to explore and

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manipulate existing data. In addition, the GIS mapping group had more powerful analytical tools within the computing application that could have potentially allowed for extensive data analysis and generalization. The traditional mapping group has neither advantage, but in either event, no students were adequately prepared to fully leverage the data set."

The conclusions of the researcher suggested that geographic data analysis and pattern-seeking are crucial elements in scientific inquiry; GIS can help to extend student research, if they have previous and sufficient knowledge to fully leverage the software. Similar to many other instructional technologies, when a proper pedagogical framework surrounds the inclusion of technology, students can benefit with increased achievement, appreciation of technology, and self-efficacy in science. For day-to-day usage, these findings tell us that more time must be spent teaching students how to consider data geographically in frameworks similar to Mitchell's (1999). By teaching students Mitchell's five basic approaches to data analysis with or without GIS, students will be better prepared to think, talk, and teach others about the geographic significance of scientific data.

Thinking Question: As a classroom instructor, you have received a free copy of the latest GIS and wish to install it on at least a small cluster of computers for student use. Unfortunately, for the software to install and work correctly, you need to buy four new computers and a new printer totaling about $6,000. Write a letter to your district administrators or any other organization that could provide the school with the needed funds. Justify the use of GIS in education and convince the reader to provide you with funds.

References

Audet, R.H. (1993). Developing a theoretical basis for introducing geographic information systems into high schools: Cognitive implications. Ph. D dissertation. Boston University, School of Education.

Audet, R.H. & Abegg, G.L. (1996). Geographic information systems: Implications for problem solving. Journal of Research in Science Teaching, 33 (1), 21-45.

Bednarz, S.W. (1999). Impact and success: Evaluating a GIS training institute. Retrieved December 2001 from: http://gis.esri.com/uc99/PROCEED/PAPERS/PAP895/P895.HTM.

Coggins, P.C. (1990). Horses for courses: Education in GIS. Presented at Mapping Awareness '90 Conference, Blenheim, London.

Golledge, R.G. & Stimson, R.J. (1997). Spatial behavior: A geographic perspective. New York: The Guilford Press.

Green, D.R. (2001). GIS in school education: you don’t necessarily need a microcomputer. In D. Green (Ed.) GIS: A Sourcebook for Schools. London: Taylor and Francis.

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Kerski, J.J. (2000). The implementation and effectiveness of geographic information systems technology and methods in secondary education. Ph.D. dissertation. University of Colorado at Boulder.

Lowry, L. (1998). The biological basis of thinking and learning. Full Option Science System: Lawrence Hall of Science, University of California. Retrieved December 2001 from http://fossweb.com

Mackaness, W.A. (1994). Curriculum issues in GIS in K12. Retrieved February 1999 from http://www.odyssey.maine.edu/gisweb/spatdb/gis-l

Mitchell, A. (1999). The ESRI guide to GIS analysis volume 1: Geographic patterns & relationships. ESRI Press: Redlands, CA.

Weibe, A. (2001). AIMS plans pattern-based math/science curriculum. Retrieved May 2002 from http://www.aimsedu.org/Documents/Pattern/Pat.html




Module 3: Types of GIS Software and Data

You already understand why a GIS might be used in the classroom, now let's focus on how the software and data interact to create a functioning GIS! This module focuses broadly on the types of GIS, basic data models and the more specific data types. For the most part, our discussion of GIS technology is constrained to the limits of the GIS that we are using in this online course. The advantage of this limit is that teaching about and using a GIS becomes a more attainable goal in this virtual setting. At the conclusion of this module, you will download, install, and learn ESRI's ArcExplorer 4.



Chapter 1: GIS Software

Many argue that a GIS is comprised of four separate components: software, hardware, data, and a thinking user. In the following module, GIS software and data will be discussed as relevant to the online course and as relevant to the ESIC onsite classes (occurring during the summers of 2003 and 2004). By limiting the discussion to software used during ESIC, we can exponentially trim the field of required GIS knowledge, leaving more time to focus on integration of technology, teaching, and learning in the science class.

GIS software is the pivotal element in our quartet, often acting as both a barrier and as a tool for driving creativity and inspiration. GIS software becomes the workspace wherein all things (data, thinker, and hardware) form a nexus of inquiry, exploration, and visualization. For the purposes of the online course, GIS software is furnished by Environmental Systems Research Institute (ESRI, Inc.) of Redlands, California. ESRI is the largest GIS vendor in the world and has a rich history of supporting education at all levels (see http://www.esri.com).

When using GIS software, we have over the past decade drawn an imaginary line

ages

between client GIS software packthat are either distributed or desktop-based. In the case of desktop-based GIS, such as ESRI's ArcView 3.x or ArcVoyager, data from the local computer may be displayed and analyzed. For K-12 audiences, ESRI has created a desktop GIS with data and curriculum packages called ArcVoyager. Built on ArcView GIS, ArcVoyager is capable of handling any number of full-fledged GIS tasks with the added benefit of an onboard tutorial for mentoring learners through the software. It can provide significantly more analytical power than ArcExplorer but cannot load data from across a network or distant database. Ultimately, this means that both ArcView and ArcVoyager are somewhat difficult GISs to work with when collaborative data collection or Internet-based mapping are heavily used.

Newer distributed GIS applications include ArcExplorer 4, ArcView 8.2, and ArcGIS software. These GIS packages have the ability to display and analyze data on local computers or remote data sources, such as spatial data servers, web services, or other network-based data. As you might imagine, the potential for collaborative student research, including data collection and analysis, can be greatly simplified through the use of distributed GIS applications.

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When a GIS publishes a map onto a webpage (often allowing users to interact with it by finding locations, querying out data, panning, or zooming), it is said to be an Internet-based GIS. Yahoo! Maps, MapBlast, and the Geography Network are all Internet-based GISs. In many cases, these Internet-based maps are rendering "smart images" based on data stored in enterprise spatial databases. ESIC uses Internet-based mapping for displaying real-time participant-generated data. The advantage of these maps is that they can be viewed and queried on the widest possible range of computer systems, requiring relatively little processing power, and they reflect data collected through collaborative efforts with great ease.





Chapter 2: GIS Data

GIS data are typically stored in one or more files on a computer. Data are stored thematically, in that data concerning rivers are stored in one set of files while county boundary data are stored in anther set of files. Similarly, data of wetland coverage or census blocks are stored in other files. Each of these themes is generally represented in a table of contents within a GIS display and can be turned on or off individually. As GIS data are stored thematically, new themes can be added to a GIS's display, or old ones can be deleted.

In order to store data, one of two models is generally used: Raster or Vector. Raster data includes photographs, satellite imagery, scanned maps, and more, whereas vector data treat real-world phenomena as objects (points, lines, or polygons).

Raster data Raster data (also called cell-based data) are comprised of a series of gridded cells, with each cell having a color value in one or more bands. In the case of a black and white photo, each cell would have one value, a number corresponding to value along a white-to-black continuum. Many color orthographic photos have three bands representing red, blue, and green bands of the electromagnetic spectrum [link to external source]. Bands can be thought of as specialized "eyes" of the sensor. One band may just "see" green light, another band red, and another infrared. In the case of an ordinary camera, it "sees" in one band encompassing the whole visible spectrum. All of the bands from a specific sensor are combined as layers to form remotely sensed images. Some satellite imagery contains seven bands or more, which include bands from the visible spectrum (like red, blue, and green) and bands that sense infrared and thermal radiation.

Raster or cell-based data are typically divided into imagery or thematic data. Imagery, as mentioned above, might include aerial photography, satellite imagery, or even everyday photography. The difference between an image and a photograph is that an image is usually stored digitally. For example, if you were to scan a family photo on a scanner at home or work, an image file would be generated that stores your family's photo in a raster format. For those more technically proficient, most graphics used on the web are raster-based, including JPEG, GIF, and BMP file types.

Raster data can also be thematic, where each cell contains a quantified measure of some phenomena. For example, elevation data is stored in a thematic raster model, where each cell has a color representing the quantified measure of elevation. In this case, elevation would be the theme that the image would represent. Thematic raster data is further described as being continuous or discrete. In the case of continuous thematic raster data, data represent a continuous surface or measure, such as elevation or rainfall. Discrete data represent categorical data, with adjacent cells

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either looking identical or changing abruptly. Land cover, soils, and geology maps typically display discrete thematic raster data.

Explore the various raster data types below, and then identify how you might use one or more of these data types to support your classroom instruction:

• Photography o Aerial photography: Digital Orthophoto Quadrangles

[http://mac.usgs.gov/mac/isb/pubs/factsheets/fs05701.html] o Understanding Color-Infrared Photographs

[http://erg.usgs.gov/isb/pubs/factsheets/fs12901.html] • Satellite Imagery

o Landsat: A Global Land-Observing Program [http://erg.usgs.gov/isb/pubs/factsheets/fs02303.html#data]

o Space Imaging [http://www.spaceimaging.com/gallery/] • Digital Raster Graphics

[http://mac.usgs.gov/mac/isb/pubs/factsheets/fs08801.html] • Elevation Models

o DEM: Digital Elevation Data [http://mac.usgs.gov/mac/isb/pubs/factsheets/fs04000.html]

o NED: National Elevation Data [http://mac.usgs.gov/mac/isb/pubs/factsheets/fs14899.html]

o Land Cover [http://mac.usgs.gov/mac/isb/pubs/factsheets/fs10800.html]

Thinking Question: Describe one or more ways in which you might use raster data to support an existing or planned curriculum.

Vector data When GIS data is stored in a vector model, data are treated as objects represented by points, lines or shapes (polygons). For example, a lake might be considered an object with a nearby pond a second object. In vector models, roads are objects as are states, cities, and homes. All geographic entities are objects in the vector model and each object can contain one or more attributes. For example, common attributes of states as objects are listed in the table below.

AREA STATE_NAMESTATE_FIPS SUB_REGIONSTATE_ABBRPOP1990 POP1997 POP90_SQMI HOUSEHOLDSMALES FEMALES WHITE BLACK AMERI_ES ASIAN_PI OTHER HISPANIC AGE_UNDER5AGE_5_17

WIDOWED DIVORCED HSEHLD_1_MHSEHLD_1_F MARHH_CHDMARHH_NO_CMHH_CHILD FHH_CHILD HSE_UNITS VACANT OWNER_OCCRENTER_OCC MEDIAN_VAL MEDIANRENT UNITS_1DET UNITS_1ATT UNITS2 UNITS3_9 UNITS10_49

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AGE_18_29 AGE_30_49 AGE_50_64 AGE_65_UP NEVERMARRYMARRIED SEPARATED

UNITS50_UP MOBILEHOME NO_FARMS87 AVG_SIZE87 CROP_ACR87 AVG_SALE87

The most common GIS software in the world primarily uses the vector model data. ESRI's ArcView predominantly uses shapefiles to store GIS data. A shapefile is actually a combination of three or more computer files storing geographic, attribute, and layout data separately. Shapefiles store topology data--data that describes the location and relationship of features such as those being represented by vector data--in files ending in .shp. Attribute data end in .dbf, and layout data are stored in .shx files. Put together in ArcView and most other common GIS data viewers, these three files allow a user to load properly formatted thematic data that is aligned to a geographic coordinate.

Vector objects are most often one of three types: point, line, and polygon. Data themes (layers) are always represented with one type of vector data. Vector data are high-resolution and are often very efficient methods of file storage. For example, roads are always represented as lines because they are generally considered to have a significant length but not a significant width. However, an object that can be suitably represented in two dimensions, such as a lake is typically represented as a polygon. Polygons represent area, requiring the real world feature to have an appreciable surface. Finally, points are generally considered to have neither a significant length nor width, essentially a zero-dimension. Many times, a sampling point from a stream is represented on a map with a single point. In many ways, the assignment of natural phenomena to an object class is theoretical but based on practical considerations such as data storage and processing speed. While these considerations are less critical today, they were very much at the forefront of thought not so long ago.

An informal quiz: What type of vector object would best represent a ...

• river? • country? • National Weather Service recording station? • interstate highway? • statewide wetland inventory?

[See Vector Quiz Answers in Appendix A for suggested answers]

Explore each vector data sets (can include point, line, polygon):

• Soils/SSURGO [briefly visit http://www.ncgc.nrcs.usda.gov/branch/ssb/products/ssurgo/]

• National Wetland Inventory (NWI) [explore http://wetlands.fws.gov/] • BASINS [explore http://www.epa.gov/OST/BASINS/]

o Watershed and hydrology data o Elevation data converted to vector format also available

• TIGER [overview from US Census Bureau http://www.census.gov/geo/www/tiger/overview.html]

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o Roads o Railroads o Census blocks o School district boundaries o Water o Population o and more....

• National Hydrography [Fact Sheet at http://gis.kuscied.org/online/course_contents/mod3/USGS_factsheets/fs06002_NatHydro.pdf]

Thinking Question: What is one or more ways in which you might use vector data to support an existing or planned curriculum?

Data models, data types, and file formats While there are basically two GIS data models (vector and raster) there are many different data types (such as satellite imagery, photos, DRGs, points, lines, and polygons). Each data type is generally stored in one or more file formats on your computer. File formats, on a Windows PC, always end with a period and three letter extension (such as my_report.doc or my_finances.xls). In the previous cases the ".doc" and ".xls" are extensions that denote a file should be read and edited with Microsoft Word (.doc) and Microsoft Excel (.xls) respectively. In using GIS, you also notice filename with three letter extensions. These extensions will help you to determine the data model and data type of the information stored within the file. The image on the right indicates data types and file formats that ESRI's ArcExplorer 4 is able to read and display.

Projections, datums, and GPS data If you have not already, read the Primer on Projections and Datums (Appendix A) that is posted in the library under Module 3. In addition, a USGS Fact sheet on GPS and an article from the Science Teacher have been placed there for increasing your background knowledge of GPS and its part in student-collected data in the classroom.


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Chapter 3: ESRI's ArcExplorer 4

This chapter will walk you through installing the necessary components of ArcExplorer and some sample data. When you have successfully completed the installations, you will then complete a quick start tutorial and create a map image for posting to the ESIC website.

ArcExplorer 4 is a complete, easy-to-use GIS data viewer that displays and queries locally stored GIS data as well as MapServices from ArcIMS. ArcExplorer 4 is built with Java, which allows you to enjoy cross-platform support. And, as always, ArcExplorer is absolutely free! All you need to do to get started is to download ArcExplorer 4 from ESRI's Web site.

With ArcExplorer 4 you can

• Pan and zoom through multiple map layers • Query spatial and attribute data • Create a buffer around selected features • Measure distances on a map • Create map layers with one symbol, unique symbols,

and graduated symbols • Label map features, with many options for effects (such

as highway shields) • Locate an address • Incorporate image formats (BMP, TIFF, PNG, JPG, and

GIF) • Save and retrieve projects • Print maps • Incorporate overview maps • View legends and scale bars • Quickly access the Geography Network, a collaborative system that

connects users with data and services via the Internet • Add ArcSDE layers • ArcExplorer 4 also includes comprehensive on-line help.

In this chapter, you will need to complete the following three sections:

1. Install the Java Runtime 1.3.1+ and AE4: 2. Download and Install the ESIC Data Pack, Volume 2

This task must be completed before beginning the quick start tutorial. 3. Quick start: Get Going with AE4

A primer on AE4 that requires you to create and upload your map-image.

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Section 1: Install the Java Runtime 1.3.1+ and AE4

Visit the ESRI website (http://www.esri.com/software/arcexplorer/download4.html) and follow their instructions for downloading and installing ArcExplorer 4. You must have one of the following operating systems:

o Microsoft Windows 98 or newer (about 15.7 MB download with JRE) o Macintosh OS X or newer (about 13.2 MB download with JRE o Red Hat Linux 7.1 or newer

Section 2: Download and Install the ESIC Data Pack, Volume 2 This task must be completed before beginning the quick start tutorial.

Volume 2 contains three ESRI data layers. The data contained in the file includes ESRI counties, ESRI rivers, and ESRI cities. These data are normally shipped freely with ArcView, ArcVoyager, and ArcGIS. If you have any of these software products already installed, you do not need to download this data pack.

In this section, you will:

1. Download a compressed data file, based on your operating system 2. Uncompress the file and save to a known location on your computer 3. Discard the compressed file initially downloaded

Download data

For all Windows users, download the data pack below. We suggest saving it in My Documents and then decompressing it, as described later.

• Visit this site: http://gis.kuscied.org/online/course_contents/mod3/data_pack_vol2.cfm

• Click on ESIC_Vol2.ZIP (21 files, 1.8 MB) and save it.

Decompress the data packs

The data pack can be uncompressed and saved to your computer's harddrive many different ways. Below, we describe the most common scenarios for saving the uncompressed data.

For Windows XP users,

1. Double-click the ZIP file and you'll notice a single folder called ESIC_Vol2.zip.

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2. View your computer's C: drive (local disk), typically done by double-clicking on desktop icon called My Computer. A link to My Computer may also be available in your start menu. In some cases, My Computer may actually be the name of your computer. Double click on your C: drive to display its contents.

3. Drag-and-drop the folder displayed in the compressed file into your C: drive folder.

For Windows 95, 98,Me,NT, or 2000 users,

1. If you don't already have a decompression program, we suggest downloading a shareware program for decompressing files, such as WinZIP from www.download.com.

2. After downloading the program and saving it to your desktop, you will need to install the decompression program, by double-clicking on it and following the wizard through the installation process.

3. Once the decompression program is installed, you will be able to double-click on the ESIC data pack and see its contents.

4. If you prefer to unzip the ESIC data immediately after installing WinZip, see Decompressing Data Packs with WinZip in Appendix A.

Discard the compressed file initially downloaded

Remember to delete the compressed data file (.ZIP) that you downloaded from the ESIC website. Generally, you don't need to retain zip files after you have decompressed and saved the data. Retaining these zip files just consumes space on your hard drive, when you can always revisit the ESIC website and download it again.

Section 3: Quick start: Get Going with AE4 A primer on AE4 that requires you to create and upload your map-image.

This quick start lesson assumes that you have downloaded and install the Java Run Time (JRT 1.3.1 or higher), ESRI's ArcExplorer 4, and the ESIC data pack volume 1.

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1. Double click the ArcExplorer icon on your desktop or locate it in your start menu.

2. Locate the GIS data you previously downloaded by clicking the

add data button in ArcExplorer 4. You will need to add Cities, Rivers, and Counties.

3. The left side of the screen

displays the Table of Contents (left-side) while the right side is your map viewer (right-side).

4. In a GIS, each piece of data is in

the Table of Contents can be turned on or off with the small box next to the data’s name. For example, the rivers data is turned off, but the counties data are on.

5. Sometimes data layers must be

made active. To make a data layer active, click once on its name in the Table of Contents. The active data will become outlined.

6. You can also double-click a local

data layer to change its Properties. The color, size, label, or shape can be changed. Double click the COUNTIES data layer to access its properties. On the Symbols tab, change the "Draw Features Using" from One Symbol to Graduated Symbols. Set the Field to Pop1999 and press the "OK" button. This should map all U.S. counties on a color ramp, based on the reported 1999 state population.

7. Repeat step 5, but choose a different field to map your data. Also,

increase your classes from 5 to 7. Note that increasing your classes increases the number of data groups (and colors).

8. Return your counties data to ONE SYMBOL in the COUNTIES properties

dialog box. Set the color to Yellow. Check the REMOVE OUTLINE option on the properties dialog.

9. Now we will work with the CITIES data layer. Be sure the CITIES data layer

is visible by turning on the check box. Double-click on the CITIES data layer to open its properties. Set the "Draw Features using:" to Unique Symbols and set the "Field for values" as CAPITAL. Set the "Style" to STAR. You will notice that values in the table are "Y" and "N". Click on the color for the "N" (not capitals) and set it to Light Blue. Set the capitals ("Y") to Black. Press the "OK" button to see a U.S. map of capitals.

10. Turn on your rivers data layer. You may explore its properties, if you wish.

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11. Zoom in to your approximate region of the country using the "Zoom in" tool. If you make a mistake, you can either Zoom out, or Zoom to the Full

Extent of the Map .

12. Set your active data layer to CITIES. When it's active, the data layer's name

will be outlined in black. With your identify tool active , click on a CITY near you. Note the information provided by the identify tool. This is attribute data, stored in a shapefile's database (see Chapter 2 for more info). Find your state's capital and determine its population from 1990.

13. The Find tool helps us locate data by searching text, much like an Internet search engine. Press the Find button and type Johnson into the " Value ( case sensitive)" field. Select the COUNTIES layer. Press "FIND". The GIS will return a listing of all counties in the U.S. named Johnson. Notice that you can pan or zoom to any selected county in the list. Close the Find dialog when you are finished.

14. The Query Builder is similar to the Find tool, however it is much more

powerful allowing users to build logical queries. For example, with the Query Builder, we can mark all U.S. counties with populations exceeding 100K. To do this

1. Turn off CITIES and RIVERS. 2. Access the properties of COUNTIES and "Draw features using:"

UNIQUE SYMBOLS. Set the "Field for values" as STATE_NAME and set the "Color Scheme" to RANDOM (solid fill). Press "OK".

3. Make COUNTIES the active layer, resulting in a black outline around the data layer's name and classes.

4. Press the Query Builder button. 5. Click on POP1999 and press "NO" when the dialog asks to display

all results. POP1999 should now be listed in parentheses inside the Execute window.

6. Press the "greater than or equal to" button. It will also appear in the Execute window.

7. Type 100000 directly into the Execute window. It will appear as: 8. g box,

press "Yes" to display all results. The listed counties have populations exceeding 100K.

. To map all counties with populations exceeding 100K, click on the

ck

corner of

Press the "Execute" button however, in the subsequent dialo

9first country listed so that it becomes highlighted (as figured). Use the scroll bar on the right to move to the bottom of the list. Holding down the SHIFT KEY, clion the last entry in COUNTIES list. Press the "HIGHLIGHT" button. Closethe Query Builder using the "X" in the upper-right the dialog.

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15. Copy your new map image to a new image file using the Copy to map-image button. You will need to select a location to store the new image file

,

16. nt icon

(JPG) on your computer. Many times the desktop or "My Documents" is a convenient and easy place to store files. The Copy to map-image will createa file that you can email, include in a web page, word processed documentor slide show. Every modern computer has a way to view this image. Note that these image files (JPG) do not retain any attribute or geographic image. They become a simple picture of your GIS map.

You may, if you wish, print your map using the Pri . The Table of Contents and Map View will be included in your print-out.

17. Finally, you may optionally Save ( ) your ArcExplorer Project. When you save an ArcExplorer project, you are actually creating a text file with links to

al

recap of some GIS features that you will need to remember:

•

your data sources and other person settings and map configurations. Saving an ArcExplorer project does NOT save the data; the data are simply linked to. This means that you will not be able to open your ArcExplorer project on another computer, unless you have placed that data in the exact same directory.

A

The Zoom to Full Extent button allows you to see the entire county

•

map.

Add uter or an Internet server. data from your local comp

The Zoom in button allows you click-and-drag your mouse• to zoom into a sp

•

ecific area.

The Pan button allows to you to move the map left, right, up, or down.

The Measure button will help you measure distances on the map. Set • the measuremile

•

ment unit, by pressing the down arrow next to this button. Set s as your unit.

The Identify button will tell you about a specific piece of the map. The data layer you are interested in must be active to use the Information tool. Whe

•

n a data layer is active its title is light blue in the Table of Contents

When you create a map, you may print it, after asking your instructor for permission. Keep these printed maps in your folder.

When you are ready to save you work, press the disk icon. You should save your work often.

•

Copy a map-image• or screen-shot of your map as a digital image for a web page, word processing document, or slide show.



Module 4: Integrating GIS Into the Classroom PBL

As an abstract representation, maps reduce the number of variables and simplify reality into understandable information. Even a simple map, however, contains a great deal of information. Students need help and guidance as they try to interpret and understand this complex visualization, whether the map is on paper or generated from a database in a Geographic Information System. By identifying a series of steps for map interpretation, we can better understand how to help students on a pathway to thinking in more abstract modes, where divergent and creative efforts are required. With a series of steps, it is also possible to design GIS supports for all levels of existing classroom instruction, including Project Based Learning.


Module 4: Integrating GIS into the Classroom PBL

Chapter 1: Teaching Science as Research

In Module One, we introduced the Vee diagram science process model, as a guide for engaging in scientific inquiry. The following diagram is the "roadmap" we discussed in Module One (otherwise known as a science research process guide).

This graphical guide is intended to help organize and direct student scientific inquiry (research). Instructionally, this graphical representation allows teachers to talk with students about where they are in their research process and about what they need to do. In this module we are going to integrate the use of geographic representations using geographic information systems (GIS) into the science research process guide.

A Quick Review of the Process Guide The research process model makes use of a modified and extended Vee-diagram (Novak and Gowin, 1984). The Vee was designed as a heuristic tool to organize and support student learning. In our modified model, the extended, double Vee-diagram creates the scaffolding for organizing and discussing the process of scientific inquiry from start to finish. It offers students guidance without being overly prescriptive and helps students understand where they are in the research process. The process guide helps teachers facilitate student work by providing a specific process structure on which they can base class discussion, student reflection, and/or the explicit teaching of science process.

Creating a Context for Scientific Inquiry The Creating the Context Vee diagram (the left of the two Vee diagrams) reflects the view that good research questions emerge from a rich context of understanding. Activities of Creating the Context include an engaging research focus, background information, standardized protocols and methods of measurement, active experience in gathering and analyzing data, looking for broad general patterns in data, and discussion with peers and collaborators.

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Asking a good research question drives scientific research, and in this process, asking a research question happens in the middle! As the first step, the research focus questions in Creating the Context give a broad, general focus to the research area, but these questions are far to general to be good research questions. As students explore the information and data that emerge from the research focus of Creating the Context, they develop questions about what is causing the patterns they see. The development of the research questions to explore those patterns is the first activity of the Research Vee.





Chapter 2: Managing a Research-Based Class

The practicality of implementing research activity with students in a classroom is quite a bit different and more demanding then the theoretical philosophical issues we have been discussing so far. In other words, talk is cheap! In this chapter we will discuss a suggested instructional strategy for implementing this science research process guide with students.

It is very important that students be introduced to and have a chance to develop general ideas of the practices and process of scientific inquiry. For the most part it is a new experience for students. Even if they have participated in research before, they probably have not reflected on the process. The education literature makes it quite clear that it is essential to pull students out of the activity (scientific inquiry) and have them reflect on the process if they are going to understand the nature of science. It is quite possible to be so busy with the activity that there is not time for developing an understanding of the larger process.

Activity: Read Introducing Inquiry and the Nature of Science, Cube Activity from Teaching about Evolution and the Nature of Science (Appendix A). If at all possible, do this lesson with your students. This activity has been a very effective way to introduce students to the science process model.

Thinking Question: How could the cube activity could be used as a metaphor for the double Vee process model? Write a brief paper answering this question

Example of the Introduction of the Science Process Model Using the Cube Activity is a good way to engage students in thinking about science process. It is important to have them begin work on a project (much as we did with you). The Global Warming Project on PathFinder Science http://pathfinderscience.net/stomata/index.cfm is a project that I used with mostly 10th grade students in a General Biology class. The following discussion will make more sense if you glance through the project!

Additional Reading: Read through the Creating the Context pages of the Global Warming project at PathFinder Science http://pathfinderscience.net/stomata.

Without explanation I took the students outside to collect leaves from school yard trees. We would come back in and make a cast of the leaf surface (project protocol) using the clear fingernail polish and observe the leaf surface under a microscope (200X). Just based on this simple observation, students would begin to ask questions about the lip-like circles (stomata) they saw on the leaf surface. A complete lesson plan for this engagement can be found in Appendix B under the title Leaf Stomata as Bioindicators of Environmental Change.

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Using this simple engagement, the class would work through the activities of the Creating the Context Vee diagram as a teacher led and directed the group. The class would be divided into research teams, 3 students in each. Each team was responsible for collecting one bit of data, meaning each team counted leaf stomata, following the protocol, for one leaf. Each team was responsible for submitting the data on the website for their leaf. Analysis of this collaboratively collected data was done as a whole class discussion.

In a teacher led discussion, questions would be asked about the students observations. Finally, each student research team developed their own research question and worked their way through the activities of the Research Vee diagram. Each team produced a research paper or poster at the end of their work.

Online you may have noticed the Guided Research Vee diagram. In the online environment that we work in, we found that it was too great a gulf between the collaborative data collection and the individual research questions. In my classroom the teacher (me) could be the instructional intervention and guide students to their research work. Online we found that many students and teachers were uncomfortable going on to research and would stop after the collaborative data collection. The Guided Research Vee on the website is online to be a teaching tutorial to help you take the next step. This tutorial will walk you through the research process as shown in the Research Vee.

The Guided Research Area is provided as an authentic, meaningful research project that allows students to engage in research without the need to develop the entire project themselves. It is designed to be a tutorial that takes students through a research project and asks them to stop, reflect on, and discuss each step of research as they progress. Each part of the guided research is interactive and asks students to contribute the new information they are developing as they proceed.

Additional Reading: Read the pages of the Guided Research Vee of the Global Warming project at http://pathfinderscience.net/stomata/gquest.cfm.

When students have gone through all parts of a research project, they will have gained valuable experience with the scientific process. For example, in the "research question" step of Guided Research, students are provided with the research questions that drive the other steps of the research. At the beginning of this page, the question is asked clearly and explores the general relationship between two variables. This is followed by a discussion of how to develop a good research question. Students are then asked to build and submit a collection of research questions relating to other possible research areas. At the bottom of this page the research question is formalized into a "If… then…" statement. If something is true, then we should see some result. The question becomes a predictive statement for the research. The students submit questions that are interesting to them, but, within the tutorial, they re not responsible for formulating their own guiding research question.

Summary As an example, with a classroom full of general biology students, instructors should:

1. introduce inquiry through discussion during the Cube Activity; 2. engage the students with an intriguing activity focusing on the research

area; 3. engage the whole class, divided into research teams of three, in the

activities of Creating the Context;

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4. have each research team work through all parts of the Guided Research Vee online;

5. have each team produces a poster or paper presentation of their work.




Module 4: Integrating GIS Into the Classroom PBL

Chapter 3: A Process Framework for GIS Inclusion in Instruction

A map is a two-dimensional graphic representation of the three-dimensional configuration of the earth's surface. As an abstract representation, maps reduce the number of variables and simplify reality into understandable information. Even a simple map, however, contains a great deal of information. Students need help and guidance as they try to interpret and understand this complex visualization, whether the map is on paper or generated from a database in a Geographic Information System. By identifying a series of steps for map interpretation, we can better understand how to help students on a pathway to thinking in more abstract modes, where divergent and creative efforts are required. With a series of steps, it is also possible to design GIS supports for all levels of existing classroom instruction, including Project Based Learning. Without an adequate understanding of the complexities of the GIS and where learners are in the learning environment, we are likely to overwhelm students with abstract information that they cannot understand. We end up with fragmented units that only teach how to use the GIS software or lessons in isolated circumstances.

Foundations In 1994, geographer Alan MacEachren presented his ideas for "map use tasks". In his model, the software user interacted with a GIS at differing levels of complexity, privacy, and discovery. In MacEachren's diagram, the user progressed along a diagonal from communication to visualization (figure 1). Along this diagonal, MacEachren assigned stages to map use activities that can be adapted as a learning sequence for introducing students to GIS. The sequence presented here is not MacEachren's sequence but rather an adaptation for instruction in classroom use of GIS:

• Presentation - the display of a static map or GIS output, usually for an audience

• Exploration - a simple examination of data presented in the GIS

• Analysis - selection of features based upon criteria

• Synthesis - recombination of existing data or using one's own data to creating a new map

• Visualization - a dynamic process of searching for new spatial patterns by altering the way the data is represented.

MacEachren's original sequence was represented within the cube (figure 1), where stages were placed upon the communication-visualization diagonal. In his cartography-cubed diagram, each axis represented a variable on a continuum:

• X Axis - human-map interaction, ranging from high to low

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• Z Axis - known information versus unknown information • Y Axis - single viewer versus a large public group.

Figure 1 Similar to MacEachren's sequence, in 1956 Benjamin Bloom presented a classification of intellectual behaviors, where task complexity basically increased with each taxonomic branch (WestEd, nd.):

• Knowledge - seeking facts, testing recall and recognition • Comprehension - translating, interpreting, and describing • Application - demonstrating situations that are new or unfamiliar • Analysis - creating categories or distinguishing events or behaviors • Synthesis - combining or organizing components into a new pattern • Evaluation - judging according to some criteria and providing a rationale

This sequence represents a general but valuable structure of intellectual behavior that should be applied as we develop a GIS process framework. Bloom's intellectual behaviors lead us to the present framework, adapted from Mac Eachren's original sequence, for student interactions with GIS. It is important to realize that a one-to-one match between process framework and Bloom's taxonomy is not intended. The two sequences are placed side-by-side to illustrate the relationships between them. Likewise, it is important to realize that any specific GIS task can breach into multiple areas of the process framework. We will now examine the steps of the present framework and explain this learning sequence for introducing students to GIS.

Presentation In Presentation, students or teachers show maps to an audience. The audience does not interact with the map but passively receives the information and context selected by the map presenter. An example of presentation may include a teacher incorporating a GIS map into a PowerPoint presentation. The map is static and does not allow for any interactivity. Other examples may include a traditional weather map in a newspaper or "You are here!" maps at the mall. These maps are characterized as having the lowest levels of interactivity, simply presenting known information. MacEachren argues that in Presentation maps are most often experienced in large groups. This level of map use, arguably, requires the least skill by the student during the presentation task itself.

This level of GIS use is compatible with didactic, lecture-style teaching formats. Map use in presentations does not allow for interaction with data or evaluation of unknowns and is typically set in whole class or lecture hall settings. Presentation of GIS maps is relatively stable, with few chances of technical problems or errors. (Presentation of map data from a GIS will be the focus of module five.)

Exploration The task of exploration is one of data discovery, investigation, and untargeted or unintentional searching. At this point, students start to "fool around" with GIS software and data. Likely, at this point, students will turn layers on and off, make layers active, or even add/delete existing data layers in a GIS. Exploration is a stage where students can investigate available data layers and simply see what is there. For example, a student may wish to turn on a layer representing state boundaries as an overlay for other geographic features, later adding cities, fault lines, and points of seismic activity to get a feel for how close to danger his or her local community lies.

With the popularity of Internet-based mapping on the rise, it is important to note that many GIS applications fit into the Exploration node on this sequence. Most Internet-based maps, like the Winter Bird Survey map

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(http://pathfinerscience.net/maps/winterbirds), allow the user to query different combinations of data, providing a limited set of data options for Exploration and representation. The use of Exploration is an ideal starting point for students working with a GIS in a situation where students must understand the concepts and content of new data. While Exploration activities can be directed through worksheets, this is usually an informal process that is relatively unorganized, random, and untargeted. At this stage of the sequence, technical stability and human error are still reasonably negligible. However, teachers and students will often begin to recognize the limitations of local PCs and networks, particularly when working with raster-based GIS data. The phase does not have a module dedicated to it, but the informal process of Exploration will be identifiable in all of the remaining lessons.

Analysis Analysis moves past Exploration to the preformance of specific tasks. Analysis is generally considered to consist of processes where data layers are compared and contrasted against one another. In some cases, data are identified based upon relationships with other data. For example, a common Analysis task is the selection of a suitable location, based upon some criteria. A biologist may want to identify all westward-facing slopes with an angle greater than 20 degrees along the Wakarusa watershed. By identifying these locations, the biologist can better choose study sites for an investigation into local riparian habitats.

Analysis tasks could include identifying what is inside, outside, or nearby another object or class of objects. For example, a developer may want to identify land parcels within 150 feet of primary roads inside Douglas County. Analysis could also consist of looking for points where deer populations have been identified within the boundaries of state or federal parks. Likewise, Analysis may be used to recognize areas of the greatest or least density of people per square mile.

The use of GIS Analysis in the classroom is often directed with worksheet activities. Specific directions indicating which buttons to push and which layers to manipulate can rigidly control classroom activity. Analysis activities are often used to teach about the GIS using this focus on buttons and operational tasks--"buttonology" activities. On the other hand, Analysis could consist of a question presented by a teacher or generated by students. Such a question may sound something like, "Where are the most likely locations in Johnson County for animal road-kills to occur?" Such an open-ended question could require far more thought and creativity by students than following a simple worksheet. In this example, students must be able to identify animals' sources of shelter, food, water, as well as their mating practices in conjunction with major roadways with projected traffic patterns. In some cases, this may even involve students doing field-work to collect their own data. Some published collections of Analysis lessons can be found in ESRI's Mapping Our World, a text of world geography activities that use GIS and ArcLessons. The ESRI ArcLessons' website provides an online library of science and social science lessons (http://kangis.org/arclessons).

Depending upon the comfort of the teacher, the GIS and science skills of the students, and the classroom environment, Analysis activities can be performed at varying levels. Analysis will likely require that data be acquired from beyond the immediate computer. Data projections, datums, file formats, and compression issues will likely come into play. Analysis typically tests the robustness of the local PC computers, the network, and cognitive abilities of the students. Module six presents a typical Analysis activity that could be provided for classroom use.

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Synthesis The idea of creating new data layers and/or recombining existing data layers into new, previously unknown patterns is the hallmark of Synthesis. In the processes of Synthesis, students take the knowledge they have previously learned about science and GIS use and apply the knowledge to a new, unknown situation. A simple Synthesis activity may include adapting a previous study to conform to the question of the current research. For example, students may wish to investigate deer road-kills in Johnson County. In this way, they may be able to take elements of a deer population study and the road kill study, creating a potential solution set using the same processes and original data sets. The students synthesize or recapitulate previous data in such a way as to make it useful for the present study.

Like Analysis, Synthesis exercises require a robust computer and network capable of transferring large data files or processing graphically intense commands. Pedagogically, Synthesis can be framed within a worksheet environment, but it is substantially more thought-provoking if left open-ended. A suitable question for guiding student research that focuses on Synthesis may include, "How and why are local deer populations represented in county road-kill counts?" Such questions, when preceded by the proper units of deer population and road kill surveys, represent an excellent point of engagement for Synthesis questions. Module seven uses Synthesis to organize GIS activities.

Visualization While exact descriptions may vary, Visualization is generally the process of searching for new patterns within the data layers. Often such patterns can only emerge after numerous iterations within an analysis or synthesis activity. Specifically, Visualization can include the manipulation of the way the data is represented by the mapmaker. Alterations of the classification (including color palette and legend types) or map styles are considered potential Visualization approaches. More recently three and four dimensional modeling and animations have been considered among the foremost efforts to create Visualizations.

In the 1987, McCormick et al. first named a new subfield in cartography referred to as "scientific data visualization," a field designed to "leverage existing scientific methods by providing… insight through visual methods" (p. 3). In scientific data, multivariate data sets are explored in spatial and non-spatial ways, which could include medical imaging, molecular modeling, and fluid flow, to name just a few.

Visualization is not readily controlled with worksheet-driven GIS activities. As Visualization is the search for unknown patterns within the dataset, it only makes sense that such patterns not be pointed out by using directive, worksheet activities. Visualization is often most compatible with classroom environments using Problem Based Learning (PBL) or open-ended scientific inquiry. Each of these efforts demands a creative and intelligent view of the data, an effort most likely tackled within the realm of Visualization. These approaches to teaching require an instructor who acts more often as a guide or assistant than a conveyor of knowledge.

Visualization requires computer technology with substantially high power, memory, and bandwidth; likewise, the user needs to have interest in and knowledge of the content related to the map, advanced skills in the use of a GIS, and detailed knowledge of cartographic and design principles. In nearly every case, Visualization is an independent effort, where a student searches for patterns in a highly interactive, personal manner. Visualization activities are covered in module eight.

Summary As complexity within the stages of the GIS framework increase, a more divergent

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and open inquiry style of teaching and learning is demanded. While Presentation and Exploration support a rather didactic teaching approach, Synthesis and Visualization require highly skilled students engaged in a rich, dynamic research-based study. These levels would fit well with a project based learning instructional approach. The properties associated with each GIS stage are related to the cognitive levels of Bloom's taxonomy. Understanding and identifying various stages in the process framework is the first step in developing your teaching practices in order to present GIS concepts in the classroom.

Thinking Question: Where do you believe your use of GIS would best fit into the framework? Do you see yourself presenting map-based data to students or encouraging them to use synthesis or high-level analysis?

References: Baker, T.R. (2000). Introducing GIS in the classroom: A process framework. Available on http://kangis.org/ed_docs/process.pdf [January 2002].

MacEachren, A.M. 1994. Visualization in modern cartography: Setting the agenda. In Visualization in modern cartography, ed. A.M.MacEachren and D.R. Taylor. pgs. 1-12. Oxford: Pergamon Press.

McCormick, B.H., DeFanti, T.A., & Brown, M.D. (1987). Visualization in scientific computing. Computer Graphics 21(6).

Slocum, T.A. 1999. Thematic Cartography and Visualization. Upper Saddle River, NJ: Prentice Hall.

WestEd. nd. DLRN Technology Resource Guide, Chapter 4. [WWW online] DLRN Technology Resource Guide, Chapter 4.





Chapter 4: Integrating GIS into the Science Process

Graphical displays of data can provide one way of searching for regularly occurring patterns in data. Graphical displays can span a variety of types, including spreadsheets, graphs, histograms, modeling and geographic representations, and statistical graphics like box plots and stem and leaf diagrams. Like good writing, good graphical displays of data should communicate ideas with clarity, precision, and efficiency. Like poor writing, bad graphical displays can distort or obscure the data, making it difficult if not impossible to understand. This can thwart the analytic and communicative effect which the graph should have.

In this course, we are using geographic representations to look for relationships in data. We are using GIS as a cooperative technology to increase the acuity of observations. Much in the same way we use microscopes to extend our vision to make new observations, geographic representations allow us to explore data for patterns in geographic relationships. However, the power of GIS technology means that the data should be accurate, precise, clear, and efficient. It is quite possible to create a map that models patterns in data that reflect the technology and not the real world. At this point, analysis requires geographic thinking. We can focus on a broad view of patterns in data by using the general elements of geographic relations from Module Two:

• Where are things? - Searches are conducted for a specific point or points meeting identified parameters.

• Highs-Averages-Lows (HAL) - This element denotes the need for maximums, minimums, determining averages, and potentially mapping bivariate color schemes.

• Density is Represented - The frequency of samples (and resulting map representations) is considered.

• Assessing Inside/Outside Elements - This element could include determining boundaries or regions in which data express commonalties.

• Assessing proximities - This element includes measures between a point or points and other natural features.

Thinking within these geographic bounds guides us in asking questions about geographic relationships that can help us observe and articulate patterns (relationships) in our data set.

GIS is a powerful tool to explore the five geographic relationships listed above. This level of thinking about geographic relationships and searching for patterns is an important aspect of "Creating the Context" for research. Remember that the focus of the Creating the Context Vee-diagram (the left of the two Vee-diagrams) is to create a rich and deep context for asking a well-formed research question. Geographic representations dramatically enhance students' ability to observe relationships in data, which stimulates them to begin to ask questions about causality (i.e. what is cause of the pattern I am looking at?)

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In the context of the Framework to Introduce GIS to students (Presentation - Visualization), discussed in chapter three of this module, students going through the Creating the Context Vee-diagram would experience Presentation, Exploration, and some Analysis level activities during their GIS interaction. To explore this further, we are going to look at three different kinds of maps. Each map is intended to create a context for asking research questions. The discussion questions are intended to focus your geographic thinking.

Observe and work with the maps presented below. Submit answers to the following questions in the discussion area.

Map 1 Below is a map of lichen distribution across a small town. The darker, larger green squares represent sampling locations where eighth-grade students recorded higher lichen density on oak trees. The smaller, lighter green squares represent locations where fewer lichens were recorded. The orange shaded areas on the east and west sides of town represent airports, and the school is represented with a yellow triangle. (Note: this data was not "cleaned" or verified by a lichenologist and expert tree identifier.)

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Thinking Questions: Can you observe and patterns in the represented lichen data? What can you say about where the lichen data points are on the map? Is there a relationship between the data points? Are the points more densely located in any one part of the map? Can you determine a boundary that appears to contain all of the points that

represent a low level of lichen coverage? Is the lichen coverage higher near any geographic feature(s)? What question would you like to ask based on the information presented by this

map? Map 2 The following is a map generated by ArcIMS. Use the features and tools offered by this map to answer the following questions. Find this ArcExplorer screen at http://gis.kuscied.org/online/course_contents/mod4/chapter4.cfm and launch the map program.

Thinking Questions: Over the past 100 years, can you observe and patterns on rapidly growing areas

in the U.S.? Where would you buy land in 1910, if you wanted to make a lot of money? Why? Is there a relationship between where people are moving and any geologic

features on the map? Where are the people in the year 2000 most densely located? Is this a shift from

1900? Can you determine a boundary(s) that appears to contain most (80%) of the

people in the U.S. in the year 2000? What question would you like to ask based on the information presented by this

map?

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Map 3 The following is a map of ozone levels in the city of St. Louis, Missouri for August 03, 2002.

Thinking Questions: Can you observe any patterns in ozone formation (think spatial and temporal)? What can you say about where the ozone forms first? Is there a relationship between downtown (inside the I-270 loop) and ozone

levels? What part of the map does ozone reach its highest point? Can you determine a boundary that appears to contains the highest levels of

ozone? Is the ozone higher near any geographic features? What question would you like to ask based on the information presented by this

map?

The Research Vee-diagram on the right represents the process driven by a very specific research question. In the language of Chapter 1 of "The Art of Science," students are taking a "systematic approach to gathering knowledge" about the natural world. During the activities of the Research Vee, students are actively engaged in science, driven by a well-formed research question. The systematic approach is achieved by progressing through the elements of the Research Vee diagram. Creating the Context for research is critical, but it is the Research Vee that systemizes the quest of knowledge. Frequently, when science moves beyond survey or observational work, it begins to look at the nature of the relationship between variables, often exploring some sort of causality. For example, what do you think the

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source of the ground level ozone is for the map above? Is your answer to this question a testable hypothesis?

The conclusions of science depend on experiments. Well-designed experiments, created with a degree of uncertainty, can clarify many issues. The analysis and interpretation required to reach a conclusion is the critical thinking we desire from our students. When students have made measurements and collected data, they need to become fully immersed in that data if they are to develop critical and logical thought processes about relationships between evidence and explanations, constructing and analyzing alternative explanations.

The questions about geographic relationships become more focused around the research question that is driving the work. Again, using the language of the Framework to Introduce GIS (chapter 3 of this module), science research (the Research Vee diagram) will require interaction and activities involving GIS at the Analysis and Syntheses levels. Analysis and Syntheses level GIS activities will be explored in the practical activities of this course.




Module 5: Practical: Presentation

Presenting scientific data in a map-based format is probably not a new teaching support for the veteran instructor. The use of maps to illustrate principles, concepts, and patterns in nature is a powerful concept, allowing the spatial nature of the data to be represented simultaneously with other attributes. Presenting map-based data means that the instructor can use little or no technology in the classroom, avoiding the risks, time, and confusion often associated with initial GIS solutions in the classroom.



Chapter 1: Case study – Map Making in the Science Classroom

In a predominantly agricultural area of rural Kansas, Mr. Hubbard's sophomore honors Earth Systems Ecology students are studying how ecosystems are interconnected by physical, chemical, and biological processes, how changes in one ecosystem can alter the processes of another, and how human decisions and activities alter the stability and biodiversity of local ecosystems. To do this, they have decided to study a local river ecosystem. There is a small stream running adjacent to the school grounds called South Creek. Mr. Hubbard divides the students into groups and asks the groups to pose questions related to ways human impact on the health of South Creek could be measured. The groups define the parameters of land use, water temperature, oxygen levels, and stream insects as indicators of the stream's health. In addition, Mr. Hubbard led the students on a visual inspection of the stream to identify specific sites for investigation.

After making the initial inspection of the stream, Mr. Hubbard led the students through the process of mapping out the South Creek Watershed. He had obtained the appropriate Quadrangle map for the area, made copies for individual groups, led the class in drawing the outline of the watershed on his map, and had each group use tracing paper to transfer the outline onto their own maps.

Over the course of the next few weeks, weather providing, the students collected their data, created tables to represent their findings, and plotted their findings on the maps along the specified stream sites. The individual groups then traced their data layer onto their original tracing paper. The class added layer after layer to Mr. Hubbard's original map. Patterns emerged as did subsequent discussions. Students suspected there was a connection between the pig farm above one of their stream sites and the lower oxygen levels found there. In addition, a farmer had decided to plow adjacent to the waterway at another site. The students found fewer mayflies and caddis flies here than at other spots along the stream and felt a strong correlation could be made between the plowing and increased amount of siltation at that site. As the discussion continued, students made several other connections regarding how human decisions in terrestrial ecosystems had affected the stability and biodiversity in an adjoining aquatic ecosystem. Assessing the impact of human activities spurred a sense of stewardship in the students of their school waterway. The next task, they decided, was to share information with local residents about the management practices that would help keep the stream beautiful for the future.

Thinking Questions: What struck you most about the use of GIS mapping applications for the science

classroom? What are two questions you still have regarding the use of GIS in a science

classroom? What GIS applications can you see in your own classroom?

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After reading the above scenario, visit some of the following links that illustrate mapping applications and GIS use in science classrooms:

• A survey of GIS/Mapping in Science: http://kangis.org/research/standards

• ESRI "Feel Good" case studies of GIS in K12: http://www.esri.com/industries/k-12/hrpapers.html

• ArcLessons: http://kangis.org/arclessons • Chagrin Watershed Institute: http://cwi.us.edu/

Online Mapping Links:

• Geography Network: http://www.geographynetwork.com • George Dailey's list of online mapping sources (zip compresses):

http://kangis.org/ed_docs/Webmap6.zip

Thinking Question: After visiting these links, what do you think you could do with the data the students gathered in the description above?





Chapter 2: Creating and Presenting with Map-Based Scientific Data

Maps have the unique advantage of representing at least three pieces of data from each sampling event: both dimensions of a coordinate pair (such as latitude and longitude) and an additional value (such as rainfall amount). Because coordinates are expressed inherently in a map, and because the location is usually critical to understanding the data, a map is a powerful medium for sharing information.

Presenting Your Data In this activity, you will create and present scientific data in a map-based format to an audience of your choosing. In module one, you were asked to collect 30 lichen sampling points. These points may have been collected by yourself or by your students, individually or in groups. Here, in module five, you will present either the lichen data or other map-based scientific data to an audience of your choosing (maybe your students). (Please note that you will need to help any novices with background information so that they are able to understand your map-based presentation). Although the lichen data is recommended for your presentation and will serve as the model presentation, you are welcome to find, present, and report on any map-based scientific data you choose. Although a GIS does not have to be used in this module, you must present multiple data layers for the same geographic region at the same geographic scale. Ideally, these additional base data (such as hydrology, elevation, political boundaries) will help support a conversation about the scientific data on the map

This activity requires you to incorporate several elements:

1. Identify your presentation audience. 2. Use the lichen data or find map-based scientific data appropriate for

presentation to your audience. 3. Identify prerequisite background information needed by your audience to

understand your map. 4. Develop your presentation of map-based scientific data using multiple data

layers for the same geographic space at the same geographic scale. 5. Present using any one of the following presentation technologies:

o a desktop or Internet-based GIS (computer with projector) o multiple acetates with each acetate representing a different data set

(overhead projector) o a PowerPoint presentation with multiple map images imported as

GIF or JPEG images (computer with projector) 6. Make the presentation. 7. Share your activity and progress at the ESIC online forum.

Developing the Presentation: Instructions

Loading the lichen data

1. Load up Arc Explorer 4.

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2. Press the add layers button . 3. You will get a Catalog window that looks like this:

4. Click on the WWW link. 5. Click on the Add a Website Link. 6. You will get a window that looks like this:

7. Type in the following: http://saturn.pathfinderscience.net

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8. Make sure the "Connect to all ArcIMS Services" is activated. 9. Double click on the Lichen map service.

10. It will appear on the right hand side of the window.

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y ping.

15. sparency p

n

11. Double click on the right box for lichen data. Notice a map with lichen data appears in the viewer.

12. Repeat the last two steps, adding the SO2 data to your map. Click on the SO2 service in the left window pane. It contains base map data. Double click on the SO2 service name when it appears in the right window. The SO2 base maps should be added to your map viewer.

13. Close the catalogue window.

14. Move the lichen map service to the top of the Table of Contents list bdragging and dropSet the tranlevel for the lichen maservice. By right clicking on the map service name, lichen, you caaccess its properties.

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16. Use the "Transparency" slider near the bottom to set the transparency level to 80%. Press the "OK" button. You should now be able to see the lichen points and base map data.

Working with the lichen data

17. Now look at the nationally scaled map with lichen data. Select the zoom in

tool button and zoom in on your area. 18. Notice you have a map with the following data layers:

o streets o railroads o hydrological information o political boundaries.

19. Click the third button on the tool bar. Click copy image to file. 20. Save the file as a JPG file (an image file). Name it lichen.

Activity: Save your image map of lichen (or other scientific data). This image should contain at least lichen points and one or more additional base data layers (such as roads, hydrology, or land cover).

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Separating Data Layers

1. Now, using your map's table of contents, deactivate all data layers except hydrology.

2. Copy image to file again (as you did above), and save it as hydrology. Print a copy.

3. Go back to the base map, and deactivate all layers except Streets. 4. Copy image to file again (as above), and save it as Streets. Print a copy. 5. Go back to the base map, and deactivate at least one more data layer (ie.

political boundaries, rails, urban areas). Copy it to file again and print a copy.

Present your work You now have at least 3 data layers in separate files. You can easily copy them onto overhead transparencies, copy and paste them into a Power Point Presentation, or use them as a desktop or Internet-based GIS computer with a projector presentation.

Select the format that you feel most comfortable with, and present your work to your selected audience.

Describe your experience

Thinking Questions: Describe your experience in the construction process. Analyze what you did (tell how or why you did what you did). Reflect upon the experience. What did you learn? What applications are you

seeing for your classroom?

Activity: Be sure to save all materials, presentations, and data from this practical. During the onsite class, you will be expected to display the materials you developed for this module, in a “map gallery”. You may find it helpful to prepare a short description that other attendees may read as a synopsis of your efforts, when viewing your “Presentation” products.




Module 6: Practical: Exploration and Basic Analysis

Using a map to analyze data from natural or social sources is a well-established practice, easily dating back to the mid-18th century. John Snow used a map of London to identify the spatial pattern created by cholera outbreaks. He deduced, from his mapped data, that particular wells were acting as distribution points and ordered the wells closed. The same type of spatial analysis can be preformed on a wide variety of datasets, either by visual/manual methods or technical approaches. In this module, we will use visual methods and charts to understand map-based data analysis. [More on Snow at http://www.ph.ucla.edu/epi/snow.html]



Chapter 1: Exploring the Map in Preparation of Analysis

Exploring Data The desire to explore is nothing less than an innate quality of all humans, a drive or curiosity that leads us to investigate, seek, or just wonder! For most people, when not restricted by lock-step sequences, exploration comes naturally. Exploration, like searching, allows us to conduct both targeted and untargeted searches of information, in order to get a stronger sense of what is available and how we can use it in our daily lives. For novices, data exploration often begins as an untargeted search; we wonder about the data trying to "figure out" what is available. We survey the available data and begin to form basic questions in our mind that drive further and more focused searches for information. Exploration is an act of initialization. It provides one sort of context from which further geography-based investigations can be made. Geographic and non-geographic data can be explored in a multitude of ways.

Some additional web-based tools for non-geographic data exploration include:

• Kartoo (http://kartoo.com): [Flash] a web search engine that uses visual exploration techniques to scour the web

• Map.Net (http://maps.map.net): a visual search engine • Table Lens (http://www.tablelens.com): [Java] exploratory data analysis by

applying information visualization principles like “focus+context” and “putting the graphics first” to the problem of interacting with large tabular datasets

• Star Tree (http://www.inxight.com/map/): [Java] a website map using a Star-Tree data explorer

• Lifelines (http://www.cs.umd.edu/hcil/lifelines/latestdemo/chi.html): [Java Applet] an exploration of longevity variables

• TreeMap (http://www.smartmoney.com/marketmap/): a TreeMap of the stock market [More on TreeMap (http://www.cs.umd.edu/hcil/treemap/)]

• Visual Thesaurus (http://www.visualthesaurus.com): [Flash] • Tilebar (http://elib.cs.berkeley.edu/tilebars/): [Java] a search engine that

queries text documents in the Berkeley Digital Library Web site

The role of geographic exploration in science What is the difference between the Sahara and the Everglades, or the Amazon basin and the Alps? The differences abound, but for most of us the obvious differences are the climate and the subsequent vegetation. Across the face of the earth, there are great differences in climate relating to temperature, precipitation, and sunlight (energy) received. These three climatic factors make climates innately geographical, as they vary by latitude or by proximity to physical features such as water bodies and mountains. General climatic patterns occur in broad belts that encircle the earth; however, climates are much more complex than this would lead you to believe. For instance, Nigeria’s climate is unique not only because of its low latitude and, thus, high amount of yearly solar energy, but also because of its altitude, its interaction with the ocean currents and air currents from the Gulf of Guinea, and other factors. For the purposes of this module, however, we will focus on general geographical trends of climate and associated vegetation.

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Through the past four billion years, the differing complex climatic factors of a place contributed to the evolution of diverse ecosystems that consist of unique combinations of plants, animals, and microorganisms. Within each major type of climatic belt, a characteristic type of vegetation develops. The resulting ecological community is called a biome.

Climate graphs known as climographs show monthly variations in only two climatic factors: precipitation and temperature. As we have discussed, many additional factors also affect climate and local weather, but a climograph gives a rough idea of the climate in a particular area. By daily observation, you can associate a climate with a particular biome. This activity allows you to study the general worldwide relationships between climates and biomes.

Many people are familiar with climographs, in which a "dual purpose chart" is prepared for a given site using monthly temperature and precipitation data. The horizontal axis represents months of the year, a series of vertical bars represents monthly precipitation (with precipitation numbers shown along one Y- axis), and a "connect-the-dots" line represents monthly temperature (with temperature numbers shown along another Y-axis). Each site with good data can thus be shown in a single graph, and easily compared visually with other sites in a rough but reasonable "apples-to-apples" comparison.

Additional Reading: Look through the EPA's Ecoregion website to learn more. Available at: http://www.epa.gov/bioindicators/html/usecoregions.html. A key to ecoregions is available at http://kgsweb.uky.edu/download/geology/useco.pdf.

The power of the computer makes it easy to shuffle between climographs in a way that is not easy with paper versions. The map below is a link to an ARCIMS map. Data represented in this map includes elevation, ecological regions of North America, mean monthly and yearly temperature and precipitation from some 275 sites from the U.S.

http://saturn.pathfinderscience.net/arcims/climate/climate.cfm

http://saturn.pathfinderscience.net/arcims/climate/climate.cfm

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Set-up procedure

1. Go to http://saturn.pathfinderscience.net/arcims/climate/climate.cfm to bring up the ARC IMS base map containing the lower 48 States.

2. Explore the data available through this map. On the right side of the map is a list of the data layers; Cities, States, Ecoregions, and Elevation. This data becomes visible (or disappears) if the check box has a check in it and you need to hit the Refresh Map button.

3. Turn on each data layer, one at a time and observe how the data is presented on the map.

4. Elevation data is scale dependent (notice the note at the bottom of the map). You will need to zoom into the map before this data will appear on the map.

5. Click on the data layer title, i.e. Cities, States, Ecoregions or Elevation to get the Legend for each data layer. This will include metadata and a key to the map data.

6. Explore the Tools available through this map. Across the top is a zoom function, a pan tool, an identify tool, and a reset the map option.

7. In order for the Identify tool to work, the Cities data must be visible. Once you see the cities on the map, activate the Identify tool and click on one of the cities. Data for that city will appear. (Look below the map graphic!)

8. On the city data below the map graphic, click on the underlined city name to launch that city's chart.

9. You can now select individual cities in the map, one by one, and see how each city's climate varies with a direct comparison to the ecoregion that the city is located in.

10. As you explore the climograph chart for each ecoregion, a list of cities within that region will appear above the graphs. A new window will open so that you can line up charts from different cities or areas for comparison.





Chapter 2: Using the Map for Analysis.

If the climate map isn't open, launch it now (http://saturn.pathfinderscience.net/arcims/climate/climate.cfm) to begin analyzing data. We are going to use our geographic analysis questions first identified in Module 4, Chapter 4: Where are things? High-Averages-Lows? Densities? Assessing Inside/Outside Elements?, and Assessing proximities to frame these questions.

11. What ecoregion is your city in? 12. Compare your city's (or the one on the map closest to your location)

climograph to the climograph for your ecoregion. How closely do the characteristics of your city match your ecoregion characteristics?

13. Compare the climographs from two different ecoregions. Describe the temperature and rainfall differences that seem to characterize the two ecoregions. These differences may relate to annual, seasonal, or monthly variations in precipitation rates or average temperatures.

14. Select an ecoregion that you are not very familiar with. Predict the climograph characteristics for the ecoregion. How accurate was you prediction? Can you determine something that may have cause an unexpected variation?

15. There is a significant relationship between landforms and some of these climate patterns. See if you can open two climographs that illustrate the rainfall shadow of the Rocky Mountains. What two cities did you choose? What is the elevation of each?

Thinking Question: What are the differences observed between the two climographs when determining rain shadow effects?

Further Discussion Now, let's step out of the activity and reflect on what we have been doing. We have been using several characteristics of GIS to allow for the analysis of data including the ability to make charts of interesting data. We have been using the five geographic questions to frame questions that help us understand the data represented on this map.

Thinking Question: What area in your curriculum could you use a map like this to stimulate your students to do geographic-based analysis? Think about a specific concept that this activity would enhance.

Thinking Question: Write a specific lesson to which you could apply this mapped based analysis, and describe in detail how the lesson would proceed.




Module 7: Practical: Analysis & Synthesis

The ability to view data in a multitude of ways is critical to its analysis. The processes of blending and joining multiple data layers to support decision making is a critical task of higher-order GIS applications. It is a task best supported with desktop GIS but is described and initiated in this web-format.



Chapter 1: Applying Visual Methods for Geographic Data Analysis

In this chapter, you will be using ArcExplorer Web Services Edition to examine relationships between lichen data, ecoregions, census data, and some TIGER data (political boundaries). You will be asked to fully leverage all visual analysis approaches.

ArcExplorer Web Services Edition may "timeout" on you, during certain times of the day or when performing certain functions or database calls. If you receive an error, press the "REFRESH" button at the bottom of the Table of Contents. If this fails to produce the required map within five attempts, we suggest you try again later. In general, we've found the Geography Network to operate the fastest during the early morning.

Go to http://gis.kuscied.org/online/course_contents/mod7/chapter1.cfm and click on the icon to launch ArcExplorer Web Services Edition

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ArcExplorer has some built-in tools (buttons) for extending your visual analytic abilities, including resequencing data layers and setting transparency levels. We will use the tools (or buttons) listed below for the following activity.

• Move Services: The Move Services icon will allow you to resequence the data layers. By changing the order of data layers, you can restrict or increase the amount of data available to the eye at one moment in time. Remember that when shuffling data in the Table of Contents, keep point-based data on top, followed by linear and then polygon data.

• Set Transparency: Transparency settings can be critical for viewing multiple data layers simultaneously, especially in ArcExplorer Web Services Edition. In ArcExplorer 4 Java, transparency settings are available by right-clicking on the data layer name and selecting "Properties".

• View Map Legend: The map legend is your key to understanding your map. In ArcExplorer Web, the legend is not automatically visible for the user. You will have to click on this button to see the legend and use the information given to interpret the data represented on your map.

Prior to the current ESRI Arc family of GIS software, even a simple "visual blending" of layers was often challenging. In most GIS data, it became necessary for users to simply turn layers on and off in a rather rapid fashion to determine geographic areas of interest or similarity (unless of course, one actually used the GIS to perform a technical approach). For example, the "Missing Ship" and "Magic Dan" are ArcVoyager classroom lesson packs that have users flipping and clicking through a staggering array of data layers in the hopes of visually identifying features specified as desirable and undesirable. While much of the polygon vector data could have been set to allow for some transparency, few used this setting, and even fewer used transparency with more than two layers of data. To many, the activities appeared more like an Easter egg hunt, looking here and there rather randomly for a single location of instructor-specified interest.

In more current GIS software, such as the ArcGIS family (including ArcExplorer 4 & ArcView 8.x), a new tool is available that extends our ability to perform visual synthesis of a various data layers. Each data layer can now be assigned a transparency level. This transparency level, particularly for polygon data, can allow us to see through data layers and view multiple data layers at the same time. By setting a low percent transparency on the top most layers, it allows us to peer down through our geographic layers, creating new combinations of colors and patterns as a result of the data present.

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To analyze data, we will use the three ArcExplorer features listed above and our geographical analysis questions. Keep these in mind as you create, manipulate, and interpret your map:

• Where are things? • High-Averages-Lows (HAL) • Density is Represented • Assessing Inside/Outside Elements • Assessing proximities • Assessing change over time

Ideally, we could evaluate the change in lichen growth and other changes in environmental or human activities over time. There are most likely multiple reasons (causality) for the patterns we observe in the lichen distribution. In this visual analysis, we are going to combine data layers into a transparency and input our collected lichen data over the top.

1. Open the ArcExplorer Web Services Edition map by following the link above and clicking the image.

o Turn on (put a check in the box) the lichen data layer. The box underneath the lichen service (it also says lichens) should also be checked.

o Remove the check mark from the climate box at the bottom of the list - you may have to scroll down a bit to be able to see it.

o Refresh map. This is the lichen data presented with no background. o Now turn on the SO2 Layer and turn the Climate layer back on.

Under Climate, have the states box checked and also put a check in the ecoregion box.

o Refresh map. Notice the color of the background. This is a combination of data layers. What data can you observe and what data layers are active to create the combination for background color?

2. Now turn on the census layer. o Leave the state, county and 2000 census data checked. o Refresh map. o Zoom in on the lower 48 states - To do this click the magnifying

glass (+) on the tool bar. o Move the cursor to a point just off the coast of Oregon. Click the

mouse and drag the cursor to a point just below and east of Florida. 3. This is an interesting and colorful map; however, it may be difficult to

interpret what we see. We are going to take advantage of two of the buttons on the tool bar and see if we can make the map easier to use.

o First, we need to "Move Services" by clicking on the "Move Services" button. In the pop up window, move SO2 to the bottom.

o Refresh map. Close the pop up window. How did the map change? Can you make any general observations about population density and percent lichen coverage across the lower 48 states? (You will need to click on the "View Map Legend" icon to better understand the meaning given to the symbols and colors represented in the map.)

o Now we are going to change the transparency settings. Click the Set Transparency button and the service layers will come up in a pop up window. Set the lichen transparency at 20%. Set the transparency for census at 30%.

o Refresh map and close the pop up window.

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4. Investigate the blended layers. o On the left of the map, under SO2, turn off Mexico_states,

Canada_provinces, USA_states and NA. o Refresh map. o In southern California, notice that we have three sets of data points.

Zoom into this area so that you can see all three sets of points with the background of population density.

o Can you make a testable hypothesis about population density and average lichen percent coverage for this area of Southern California? (Once again, the legend will provide information you need to develop your hypothesis.)

o Zoom to full extent. o There is a lot of data points in the Midwest. Zoom into Northeast

Kansas until Douglas and Johnson County fill your screen. County names should appear if all of the correct data layers are active.

o Does what you see support your testable hypothesis about population density and average lichen percent coverage?

Thinking Question: State a testable hypothesis about the population density and percent lichen coverage for the area in southern California. What, specifically, did you observe that lead you to develop this testable hypothesis?





Chapter 2: Visual Methods for Synthesizing Geographic Data

Introduction Creating new layers from data and/or recombining existing data layers into new, previously unknown combinations, is the hallmark of Synthesis. Synthesis means that students take the knowledge they have previously learned about the natural world and, in combination with the GIS skills, apply their new knowledge to a different, unknown situation. A simple Synthesis activity may include adapting previous work to answer a new research question. For example, students may wish to investigate deer road-kills in their county. In this way, they may be able to take elements of a deer population study and the road kill study, creating a potential solution set using the same processes and original data sets. The students synthesize or recapitulate previous data in such a way as to make it useful for the present study.

Like Analysis, Synthesis exercises require a robust computer and network, capable of transferring large data files or processing graphically intense commands. Pedagogically, Synthesis requires a higher level of thinking (Bloom's taxonomy) and requires substantially more thought-provoking, open-ended questions to guide student work. For example, questions for guiding student research at a Synthesis level could be,

Where are the greatest numbers of deer-related car accidents occurring in the county?

Why are these "hot spots"? How can we address this problem?

Such questions, when preceded by the proper units of deer population and road kill surveys, represent an excellent point of engagement for Synthesis questions.

Synthesis is driven by a guiding question that requires a new representation of data, built from previous GIS datasets, to be created. The guiding question requires an advanced, multi-step approach to geographic data analysis, with data and operators (merge, intersects, overlays, etc.) chosen and conducted by students, as they search for ways to answer the question. Synthesis is informed by a substantial knowledge of cartography, GIS, and the subject area of the study. Synthesis can occur in at least two approaches: a visual/manual approach and a technical approach.

Visual/manual Approaches to Synthesis Visual/manual synthesis will best describe many of the activities we pursue in this online course. The technical limitations of Internet-based GIS software become an increasing barrier as we try to delve more deeply into the data. The data available becomes a limiting factor to moving from Presentation and Exploration of data into these advanced analytical strategies. This bottleneck increasingly forces us to use visual/manual methods of advanced analysis and synthesis. In Chapter 8,

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Visualization, we don't even have the luxury of visual/manual methods and will have to resort to examples rather than engagement in visualizations.

Go to http://gis.kuscied.org/online/course_contents/mod7/chapter2.cfm and click on the icon to launch ArcExplorer Web Services Edition

Additional Tools in ArcExplorer ArcExplorer has some built-in tools for extending your visual analytic abilities. Unlike some Internet-based applications, resequencing data layers, setting transparency levels as well as performing targeted searches and basic address matching are all useful tools.

• Adding data: Add your own data or data from the Geography Network to your ArcExplorer map to provide more or better base data for extending your inquiries or starting new ones.

• TTargeted text searches: Often a feature is easier to find using its name or other text attribute. By focusing your view on a targeted object, it often provides the correct extent or frame or reference to study some phenomenon.

• Point creation/Address location: Similar to a targeted search, ArcExplorer Web Services Edition will allow you to enter an address location, allowing you to properly frame your study area.

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Do not forget about these three buttons!

• Move Services: By changing the order of data layers, you can restrict or increase the amount of data available to the eye at one moment in time. Remember that when shuffling data in the Table of Contents, keep point-based data on top, followed by linear and then polygon data.

• Set Transparency: Can be critical for viewing multiple data layers simultaneously, especially in ArcExplorer Web Services Edition. In ArcExplorer 4 Java, transparency settings are available by right-clicking on the data layer name and selecting "Properties".

• View Map Legend: The map legend is your key to understanding your map. In ArcExplorer Web, the legend is not automatically visible for the user. You will have to click on this button to see the legend and use the information given to interpret the data represented on your map.

1. If your map application is not open, launch it now from the link listed above to begin this activity. We are going to use the geographic analysis questions to organize our thinking and to begin visual synthesis of our lichen data with various environmental and human demographic data layers.

2. With the map launched, turn on (check in the box) for the lichen data layer. o The box underneath the lichen service (it also says lichens) should

also be checked. o Remove the check mark from all of the other main boxes on the list

on the left of map. o Scroll down to remove the check from the climate box. o Refresh map. This is the lichen data presented with no background

data. 3. Now we need to add layers to our map. Click the Add Data Button.

o In the pop up window, pull down the choices in the "Look in" text box. Choose ESRI. This will put http://www.geographynetwork.com into the server box. Click OK.

o The pop up window will now offer services to add. Choose Atlas_ Precipitation, and click "add".

o Close the pop up window, and take a look at the data added to the list on the left of the page.

o Put a check in the box for the main area Atlas _ Precipitation. Turn off (remove the check) from Land, Non-U.S. Land, and Oceans and Seas. Leave the check in the box for Average Annual Precipitation.

o Refresh map.

o Zoom in on the lower 48 states - to do this, click the button on the tool bar. Move the cursor to a point just off the coast of Oregon. Click the mouse and drag the cursor to a point just below and east of Florida.

o Turn the Climate layer back on. Have the States box checked and also put a check in the Ecoregion box.

o Refresh map. Notice that the lichens change color and fade and the color of the background is also changed. This is a combination of data layers, a transparency, and it has to do with the order that the data layers.

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4. We are going to shift the order of the layers and the opaqueness of the data layers to make a map that clearly shows the data we are interested in.

o First, we need to "Move Services". Click on the "Move Services" button.

o In the pop up window, move Atlas _ Precipitation down the list so that the order of the services from top to bottom is - Lichen, SO2, Census, and Atlas _ Precipitation, and Climate as the base layer.

o Refresh map. Close the pop up window. 5. Now we are going to change the transparency settings.

o Click the "Set Transparency" button and the service layers will come up in a pop up window.

o Set the lichen transparency at 20%. o Set the transparency for SO2 at 75%. o Set the transparency for census at 100% (think about why). o Set the transparency for Atlas _ Precipitation at 40%. o Refresh map, and close the pop up window.

6. There are a large number of trees that have data for lichen coverage in the Midwest. Zoom into this area so that you can see sets of points from Northeast Kansas, including the Wichita area and Northern Arkansas, in the same window.

7. Use any of the map features/data that you find useful, including zooming into and out of specific areas and turning layers off and on, to answer the following questions:

o How does the annual precipitation change as you go from East to West on the map?

o Is there an observable relationship between Ecoregion and Annual precipitation?

o Is there an observable relationship between Elevation and Annual precipitation?

o Is there an observable relationship between population density (2000) and the lichen data?

o Can you make a testable hypothesis about annual precipitation and average lichen percent coverage for this area of the Midwest?

o How would you use this map to guide additional data collection on percent lichen data?

o What additional data layers would be interesting to bring in to help us explore the variation of percent lichen coverage on trees?

Activity: Use the ArcExplorer "Add data" button to locate at least one additional data layer. Create a map incorporating this new data and lichens. Answer the following question with your map window open.

Thinking Question: Why did you initially choose this data (what were you looking for)? Explain, in detail, what your map shows once you actually added the data.





Chapter 3: Technical Methods of Geographic Analysis

To this point, we've spent the majority of time using visual methods or very simple technical methods for exploring, analyzing, and synthesizing data. While ESRI considers ArcExplorer to be a "GIS data viewer", it does allow for some technical analysis, such as geocoding and buffering. However, when a desktop GIS is used, such as ArcView, Geomedia, or ArcInfo, substantially richer geographic analysis can be performed with the software.

Geographic Analysis Operators Buffering A buffer is an area drawn at a uniform distance around a feature. A buffer can be set at an absolute value (such as 100 meters) or can be a function of the feature's attribute. For example, a buffer of 10 feet may be set around 1st order streams in a county, whereas a buffer of 25 feet can be set around 2nd order streams. These buffers are intended to reflect the estimated riparian areas within the county. With the buffers set, it is a fairly simple task to compute the estimated amount of riparian area within the county. How might buffers be useful in extending one or more of your current classroom efforts?

The buffers in the image on the right, indicated in the light blue shading, were set at a single value of one kilometer around all hydrologic features. These data are from Brown county, MN and processed in ArcView 8. Buffers may be created in ArcExplorer 4 as well as any desktop GIS.

Geocoding Geocoding is the process of assigning a geographic location to some phenomenon. In most cases, when someone speaks of geocoding they are referring to address-matching procedures, the alignment of a street address to a geographic coordinate (such as latitude-longitude). Geocoding usually falls in one of three categories:

• Address matching is the most precise form of geocoding. A street file is interpreted by the GIS and allows users to locate a specific street address on the GIS, based solely on house number and street address. Using this geocoding approach, an input address would look like: "1122 West Campus Road". This style of geocoding is often based on TIGER data and is easily incorporated into a GIS application.

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• Intersection matching is less precise than full address matching and is usually implemented when a full street address is unavailable or when a given street address cannot be located in a street file. In essence, the GIS tries to match an address like "Maple Rd and 3rd Street". As you can imagine, geocoding using a street intersection match usually provides a latitude-longitude that is several dozen meters (or more) away from the target address.

• Zip code matching can be used to determine an approximate latitude-longitude in the event a street database is unavailable or a zip code is the only address information provided. Each zip code in the United States has been assigned a value and is often referred to as a centroid. In reality, the actual latitude-longitude assigned to a zip code may be the geographic center of the zip code or it may be the latitude-longitude of the local post office.

Geoprocessing Operators for Desktop GIS A common data set has been used to describe the

phic

visual tic"

four geographic analysis procedures below. The illustration on the right depicts two different layersof data, one containing a red circle the other containing a green square. These are geograpolygons and could represent any number of physical features. In the cases below, we have used the abstract blocks to avoid additionalclutter often generated when using more "realisdatasets.

In the four procedures identified below, these two data layers were the geographic input. The depictions in subsequent sections indicate the net result of the identified procedure on the original dataset.

Intersect This operation cuts an input layer (green box) with the features from an overlay layer (red circle) to produce a single output layer, with features that have attribute data from both layers. Intersects create a new data layer from the common geographic extent of the smaller (overlay) layer.

Intersects are very similar to "Cut", "Cookie cutter", or "Clip" procedures, where one geographic feature is cut from a larger data set. For example, rivers of San Diego County could be cut from the "Rivers of North America" dataset, using a overlay layer in the shape of the San Diego county boundary.

http://gis.kuscied.org/online/course_contents/mod7/img/ORI_INTERSECTION.gif

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Union This operation combines features of an input layer with the polygons from an overlay layer to produce

and full an output layer that contains the attributesextent of both layers.

In a union, all data from all layers are placed into a single, new layer. Unlike an Intersection, unions are not concerned with overlapping geographic extents.

In ArcGIS 8 terminology, a union is a type of Overlay and can be preformed through the use of the Geoprocessing Wizard. Unions are not conducted in ArcExplorer or Internet-based GIS application.

Merge This operation combines the features of two or more layers into a single layer. Attributes will be retained if they have the same name.

A merge is most often used to combine adjacent data layers. For example, elevation data is typically acquired at 1:24,000 scale - typically not large enough to cover a countywide study. A merge of several different elevation datasets for a county would result in one single elevation data layer. Combining similar data into a single layer makes additional analysis less time consuming and ensures consistency.

Dissolves A dissolve procedure aggregates features that have the same value for a specified attribute. Using the two original data layers, the features were dissolved into a single data layer having a single polygon feature. The features were dissolved on an attribute value that each of the original data layers had in common.

The dissolve procedure is usually considered to be a data reduction technique and is often a "first step" in analyzing a number of complex data layers. For example, all of the counties in a given state could be dissolved into 2 or 3 polygons sharing a common attribute (such as dominate land use classification: urban, agriculture, or mixed). An analysis of urban versus agricultural geographies across a state could then be conducted.

http://gis.kuscied.org/online/course_contents/mod7/img/ORI_MERGE.gif

http://gis.kuscied.org/online/course_contents/mod7/img/ORI_DISSOLVE.gif

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Thinking Question: Develop a metaphor for describing to students either the Union or Merge operator. This metaphor must be appropriate for your classroom.





Chapter 4: Technical Methods of Geographic Synthesis

For those experienced with GIS, you may have realized that the operators and procedures that we have classified under "Synthesis" are largely advanced data analysis techniques that produce new datasets from other, possibly diverse data. Many of the data analysis operators presented in the previous chapter are a required step for most Synthesis activities.

When is Synthesis different from Analysis? The stage of Synthesis is used to denote a particular type of data analysis when:

• Activities are driven by a guiding question and are situated within a contextualized knowledge of many different fields (including GIScience and the content area the data represent). Synthesis activities are not directed with a worksheet or tightly controlled through pedagogy (this is a step-wise approach to analysis).

o The guiding question or issue requires a new data layer or dataset to be created, based on previous GIS datasets.

o The guiding question or issue requires an advanced, multi-step approach to geographic data analysis, with data and operators (merge, intersects, overlays, etc.) chosen and conducted by students as they strive to reach their intended question.

• Activities represent a deep blending or fusion of: o Data: old datasets support the creation of new. o Skills: cross-disciplinary skills and knowledge are fused to support

the investigation. o Ideas: ideas and thoughts emerge from a contextualized

understanding of geography, science, and GIScience.

Interpolation: A Synthesis Operation

Interpolation is the insertion of representative data between existing data points, usually in an effort to provide an estimate value of an attribute across a surface. For example, weather maps often display interpolated data: data is initially collected at only a few weather stations, and the results are generalized across a region.

Frequently, when using a GIS, it is necessary to know more information about a region than direct measurement allows. Often demands for funding, time, or other resources are simply too great to warrant actual collection of additional data points. It is in cases such as these that methods of interpolation are often found most useful. Interpolation is the estimation of an unvisited point's value based upon other nearby known points.

Whenever a smooth continuous phenomenon is mapped, such as precipitation across Nebraska, it is often plotted as an isarithmic or contour map, because the interpolation techniques assume that data are smooth and continuous. Isarithmic mapping uses interpolation techniques between sample points of known value to

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establish isolines, or lines of equal value. In mapping precipitation, interpolating values between known points, taken at weather stations, creates the isarithmic map.

Isarithmic mapping can be used for both true and conceptual data. True point data are data from the actual geographic location. As an example we could measure an attribute value at a particular latitude and longitude. Conversely, conceptual data represent an attribute value of a geographic area or region. The population density of a county or wheat harvested per county acre are each examples of conceptual data. In the case of conceptual data, it must be standardized or normalized. This normalization is needed to ensure that the map clearly conveys the nature of the phenomenon for a given region, resulting in an isopleth map.

Isarithmic maps can be generated by data that represents information at exact points or data that represents information collected over an area. Interpolation techniques for exact points include triangulation, inverse distance weighting, and kriging. Each of these methods can vary substantially in computation and output. Advantages, disadvantages, and (when possible) illustrations will be provided in the descriptions below to help you understand the methods of various interpolation techniques.

Triangulation With triangulation interpolations, randomly sampled control points (figure 1) are connected with straight lines (figure 2) forming triangles called Delaunay triangles. Halfway between points, a perpendicular side of a new shape, called a Thessian polygon, intersects the Delaunay triangle line (figure 3). Each data point now lies in one Thessian polygon. Any spot inside a Thessian polygon is closer to its enclosed control point than any other control point. In effect, we can then interpolate that the polygon region surrounding a control point takes the same value as that control point (figure 4). In this way, triangulation methods are said to "honor the control point," as the surrounding region takes the exact value of the control point. This method can produce very "blocky" interpolations, particularly if the control points are sparse. As a result of the nature of triangulation, many geographers suggest using other methods for representing gradually changing phenomena, particularly when few data points exist across a region (Burrough & McDonnell, 1998). The method of triangulation would be particularly good for mapping nominal data, such as vegetation, aspect, or slope.

Figure 1. Figure 2. Figure 3. Figure 4.

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Inverse Distance Weighting (IDW) The inverse distance weighting (IDW) interpolation combines the propinquity functions of triangulation with gradual changes inherent to smooth, continuous phenomena. The IDW, or gridding approach, interpolates unvisited points by overlaying a grid atop the control points. IDW then estimates the grid intersections (or nodes) as a function of control points' distances and values. Interpolations are then made between the grid nodes. Using this method, distant control points have varying effect on the interpolated point's value. To compute an IDW, one estimates the node point's attribute value (Z) by using the following formula, based on Euclidean geometry:

Z = z1/d1 + z2/d2 + z3/d3 + ……… 1/d1 + 1/d2 + 1/d3 + ……… z (lowercase) = attribute value at a control point d = distance of control point to interpolated point

Contour lines connect Z points of equal value, real or interpolated. The result is a jagged isoline. The user typically sets the number of control points used in the interpolation when the interpolation is executed. Using splinning procedures, the jagged isoline is smoothed to better fit the nature of the phenomenon. Splinning is a technique consisting of fitting a mathematical function to the points in the contour line. An advantage of IDW (over kriging described below) is that large data sets are easier and faster to interpolate. The mathematical calculations involved with IDW are far simpler and, therefore, require less processing, resulting in faster outputs.

Many different search decisions are commonly associated with IDW. The number of control points must be considered because not all control points should carry equal weight (or even be included) in determining an unvisited point's value. Methods were developed that only used a portion of the available control points. One such approach, available in ArcView GIS, is called "nearest neighbors." Using nearest neighbors, a user can choose the number of closest control points that they may wish to use in interpolation. By limiting the number of nearest neighbors (defined as the nearest control points used in the interpolation formula) we can vary the interpolation effects on distant points. Another method available in ArcView is called "fixed radius," in which all control points falling within a specified radius of an unvisited point are used in the interpolation.

A similar option, available in the Surfer GIS package, is the use of a "search radius." The search radius, similar to the fixed radius, presumably allows the user to specify the distance, within which all points will be used in the interpolation. As an additional way of utilizing only a certain subset of the control points within the search radius, one can also determine a direction and angle from which to choose our control points. For example, a user may wish to only use the northeast quadrant of the search radius when interpolating (figure 5). Similarly, an octant (or 1/8 of the total search radius) could be used (figure 6). This technique can be used effectively when an unvisited point's value should not be based on all surrounding points but, rather, based on points in a specific direction. It should be noted that Surfer's "all" option is also available, denoting a search within the entire search radius (figure 7). Typically, the more distant data that is used to determine an interpolated point, the more generalized the resulting interpolation grid will appear.

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Figure 5. Figure 6. Figure 7.

Aside from the seemingly arbitrary distance of a chosen search radius or nearest neighbors, the key disadvantage to IDW is that it cannot account for trends in the data. As IDW interpolates a value based on averaging, it would be impossible for a computed value to be higher or lower than its surrounding control points, as in the case of a mountaintop or valley bottom. In short, data extremes cannot be represented by traditional IDW methods. One measure for overcoming this problem is to fit a trend surface--a technique that uses mathematical functions to derive a better approximation of the local control points, making the data extremes are more readily represented.

Kriging Kriging, like the inverse distance weighting approach, uses a grid overlaid atop the control points. At each grid node (intersection), a value is estimated, and an interpolation is arrived at based upon the distance and weight of those selected grid node values. Unlike IDW, kriging does not examine the unvisited points independently. It uses spatial autocorrelation, based on the idea that like things occur near each other. Kriging accounts for the spatial pattern of points from one grid node to another or between unvisited points. Kriging, when properly conducted, can produce a more accurate method of interpolation, a so-called "optimal interpolation."

To more completely understand the concept of spatial autocorrelation, one must be familiar with the ideas of semivariance and the semivariogram. Semivariance is simply a measure of the change in data values across various geographic directions. Mathematically, the semivariance is represented by:

Yh = ∑ (Zi - Zi+h)2 2(n-h) Zi = values of control points h= multiple of the distance between control points n=number of sampled points

The semivariance is represented by a semivariogram, which shows the distance between points on the x-axis and the semivariance on the y-axis. Typically, the semivariogram will illustrate that as distance between points increases, the semivariance increases. This corresponds to the notion of spatial autocorrelation by holding to the idea that geographically close points tend to contain similar values.

The semivariogram, for larger data sets, can be modeled using a curve to summarize the points. The curve is typically one of four types: linear, spherical,

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exponential, or gaussian (figure 8) (Burrough & McDonnell, 1996). It is these models that allow one to choose a specific distance between points and derive a suitable value. In the last three curves mentioned, a sill or plateau will form near the upper-end of the curve. At this sill (representing a semivariance value), the data point is no longer similar to neighboring values.

Figure 8.

Using a simpler form of kriging, known as punctuated or ordinary kriging, we assume there is no trend or drift in the data. In this method of kriging, we use the weighted average to calculate a grid point's value:

Z = w1z1 + w2z2 + w3z3 ... Z = estimated grid point value z=control point value w=weight for each control point

In the above equation, the (w) value (weight for each control point) is established through a series of complex mathematical processes and the semivariogram, wherein the semivariance associated with distances between points is calculated. The weight is primarily determined by two factors: the distance between the estimated grid node and the selected control point and distances between nearby control points. Each weight value, (w), is summed, forming a total weight of 1 for any given control point. As a side note, typically more than a single semivariogram model (curve) may be used, as the spatial autocorrelation may vary systemically with the direction of the semivariance. Likewise, more than three control points are typically used in kriging interpolations.

Aside from potentially producing a more accurate map as an optimal interpolation method, kriging also allows for the creation of an error map, known as a standard error of the estimate map. This map basically describes the confidence with which we interpolate values for any given point. The creation of such an error map is not possible with either triangulation or IDW approaches.

Choosing an interpolation type Choosing the most appropriate method of interpolation can be challenging. If one wishes to honor the control point, by using the exact value across a region, then triangulation is the interpolation of choice. This argument assumes there is no error

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in the control point value (unlikely) and that the entire region is best represented by a single value.

If a user is more concerned about the correctness of data at non-control points, various studies seem to indicate that there is very little difference between the results produced by IDW or kriging. Even the nature of the phenomena, smooth or abrupt, made very little difference in the method of choice. Triangulation, however, was not recommended.

Some users may be concerned with how well a particular method will handle discontinuities within the data, such as a geologic fault line. For this concern, the triangulation method is recommended by McCullagh, as one could simply enter the locations of these lines as a "logically connected set of points." When triangulating, the discontinuity would be recognized as an independent region.

For our purposes, if a large data set is being worked with and time is short, then triangulation is suggested as the fastest approach, as it requires the least computations. IDW, followed by kriging, are substantially slower; however, with commonly sized data sets and the power of the PC processor quickly increasing, this issue is often negligible.

Finally, ease of understanding and developing an interpolation could be of concern to some. The ease of understanding the conceptual processes underlying an interpolation method vary greatly from triangulation (relatively easy), to IDW, to kriging (the most complex). In terms of ease of developing the interpolation, many common GIS programs now allow for a user to simply "go with the defaults." This approach can, however, be relatively dangerous if a user is not sure what it is that they are doing. Typically, the ease of development runs parallel to the ease of understanding, where triangulation, IDW, and kriging are increasingly complex.

Thinking Question: What kind of student-collected data would be most helpful to interpolate? Assuming that you are using a desktop GIS, which method of interpolation do you think would be most appropriate for your chosen dataset and why?

Synthesis Examples Location-Allocation Location-Allocation (L-A) is a process of selecting one or more geographic locations that meet specified criteria. L-A commonly ask for the analysis and synthesis of several layers of geographic data into a single geographic dataset that demarks desirable geographic locations. L-A can be beneficial in an array of professional and academic activities including commercial development, biological monitoring, environmental conservation, and more. For our purposes, we can divide L-A into three subtopics:

• Site Suitability • Prediction Modeling • Hazard Mapping

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A brief description of these subtopics, as follows, will give you a better idea about the diversity and utility of Location-Allocation. Links to examples of each type of L-A, taken primarily from ESRI's yearly conference and covering a variety of subjects, will follow its description. Please take time to read at least one of the articles for each subtopic.

Site Suitability or site selection is most closely associated with Location Allocation, often being used interchangeably. Site suitability is just that - using datasets to decipher the most suitable site. The site might be a location for a new McDonalds, ascertained from marketing, transportation, and zoning regulation data. Likewise, city officials could use topographic, vegetation, and hydrologic data to find the most suitable site for a new wetland.

Examples are Knowledge Based GIS for Site Suitability Assessment (http://gis.esri.com/library/userconf/proc02/pap1185/p1185.htm) and Locating Potential School Sites (http://gis.esri.com/library/userconf/proc01/professional/papers/pap618/p618.htm).

Prediction Modeling has two different purposes. First, one can use prediction modeling to locate an area that is most likely to contain a certain phenomenon. For example, there are current efforts to develop and deploy species prediction modeling. By compiling data that describe habitat choices of a species, like proximity to running water or abundance of deciduous trees, specific locales can be pinpointed as likely habitats for a certain species. Second, prediction modeling is used to determine what might happen given certain circumstances. Given accurate hydrologic data along with topographic, climatic, and soil information, it will be possible to predict erosion for a given area.

Examples are ArcView Extension for Site-specific Soil and Water Conservation (http://gis.esri.com/library/userconf/proc01/professional/papers/pap1051/p1051.htm), Using GIS to Model and Predict Likely Archaeological Sites (http://gis.esri.com/library/userconf/proc01/professional/papers/pap651/p651.htm) and Species Prediction Modeling (http://www.lifemapper.org/).

Hazard Mapping is closely related to the latter purpose of prediction modeling. The primary difference is the end user. Hazard mapping predicts the progress and results of a given disaster. For example, given the wind speed, air moisture, time of year, and the quality of vegetation, the speed and direction of a spreading wild fire can be predicted.

Examples are Using GIS for avalanche hazard mapping in Switzerland (http://gis.esri.com/library/userconf/proc01/professional/papers/pap660/p660.htm) and Integrating Biophysical, Climate, and Human Factors in a Wildfire Model (http://gis.esri.com/library/userconf/proc02/pap0285/p0285.htm).

Thinking Question: How might your students use a site suitability or similar synthesis technique in the classroom?




Module 8: Practical: Approximating Visualization

Visualizing scientific data using map-based representations requires advanced knowledge and skill in GIScience, cartography, and the subject matter of the data - all coupled with an inquisitive mind, interested in searching for unknown patterns in the data. This modules explores the technologies and techniques of visualization, preparing participants for advanced use of desktop G.I.S.



Chapter 1: Symbology and Data Classification

Symbology Maps may use a variety of symbology (or more specifically visual variables) to help communicate information. Generally speaking, symbology is the language of the map. For instance, when you look at a road map, you are perfectly aware of what those black lines are trying to communicate - the location of roads in the area. While these visual variables are not typically used in a classed (chloropleth) map (see Data Classification below), they are used for many other types of point, dot, or symbolic maps (See Slocum, 1999 for a complete review for a review of Bertin's Visual Variables).

• Shape: The shape of a point can be changed to represent a particular value. Churches can be assigned a cross, while schools can be assigned to a small building with flag.

• Orientation: The general direction or pattern made by several simultaneous points or lines. Often vertical hatches are used to denote a specific region, whereas diagonal hatches may be used to represent a different region.

• Spacing: The use of "white space" within a symbol (point or line) may be used to denote variance. Of the types of symbology listed, this is the least common visual variable to exploit.

• Size: The width and/or length of a point, line, or polygon can be changed to reflect the value of any attribute. For example, the thickness of a road network may indicate the road's importance, such that heavier lines represent major transportation arteries or highways.

• Perspective height: Most often used in polygon or area maps, perspective is often attributed to 3D mapping, where the height of a surface represents some attribute. Elevation data is often mapped in three dimensions, playing on the visual variable of perspective height.

• Arrangement: Arrangement is most often seen as patterns within lines or points. For example, a bike trail may be marked with a series of two dots followed by two dashes, in lieu of a single solid line. Similarly, a hiking trail may be composed of dots rather than a single, solid line feature. In these cases, the arrangement of points and/or dashes helps to distinguish the variable of "trail type".

• Color: The concept of color combines three attributes: hue, lightness, and saturation. The combination of these attributes allows for a distinct communication of color on a map.

o Hue is understood as traditional color. For instance, red, yellow, purple, and green are all hues.

o Lightness/Value refers to the lightness or darkness of a specific hue. Think "brightness."

o Saturation can be thought of as a mixture of grey and a pure hue.

Data Classification Data classification is a process of assigning symbolic representation to some phenomenon's value. Simply stated, classifying data means grouping like data values in order to represent it spatially (on a map). While not all data are classified, most of the data we will work with are classified. The way you classify data will affect how visually effective and detailed your GIS map is. You may have noticed in

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Module 3 Chapter 3 that when you mapped the 1999 population of the counties in the US, almost the entire country appeared one color. Only a few counties, such as those containing large cities like New York or Los Angeles, appeared distinct from the others. This occurred because most of the counties in the US have relatively low populations compared to the populations of these densely packed counties. To make that map more detailed and probably more effective depending on the end use, it would be important to change the type of classification used to split up the population categories. (Unfortunately, ArcExplorer does not allow for classification change.)

For our purposes, data classification will be represented in a chloropleth map. Chloropleth maps represent data by shading areas of the map with intensity proportional to the data values. The precipitation maps we often see on the weather forecast are good examples. Areas generally shaded yellow or red are expecting high rainfall, whereas light green areas should expect less rain. In this example, the color ramp ranges from light green to dark green (for less severe rain events) and then from yellow to red (for more severe rain events.) To further understand how chloropleth maps are made, we must look at the specific decisions typically required to classify data:

1. Number of classes or groups of data. Using more classes typically means more computer processing power is required to create and display the map, but a finer color ramp is generated. Using too many colors can make distinguishing features difficult. When selecting the number of colors to use, consider whether you want the audience to be able to understand general trends (use many colors) or search for targeted criteria (fewer colors).

The two maps below depict the same data, but they use a differing number of classes and, thus, colors. The left map has two classes while the right map has 30 classes.

2. Normalization should be used when creating classed (chloropleth) maps. When data are normalized, they are divided by some attribute to "equalize" or "normalize" the visual display. For example, all map images below are normalized. Country populations are normalized by the country's size and then mapped. This provides a map of people per square mile, providing greater information about the living conditions of the country.

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If you presented students with Map A (below left) and asked, "Which country is more crowded?" they would likely choose Russia or Lithuania, however Map B (the normalized) map would likely lead students to more correct answers (Germany or the UK). As a basic rule of map making, data in classed maps should be normalized by some meaningful attribute. Normalization is available when using "QUANTITIES" in ArcView 8.x.

3. Method of classification may vary depending upon the data and its intended usage. When data are classed or grouped, they may be "broken up" using several different mathematical algorithms. Different methods of classification may result in dramatically different representations of the same data. Specific methods of classification, including equal interval, natural breaks, quantile, and standard deviation, will be discussed below.

The following images depict European population (1992) normalized by area (square mile). You may click on the images below for an extended screen dump of the GIS interface. This will allow to see how many classes are used, the color assignment, and the upper and lower bounds of each class.

o Natural Breaks (ArcView 3.x default) identifies "breakpoints" or natural clumps, gaps, or patterns in the data. The statistics used in natural breaks classification minimize the variation within each class. Natural breaks is generally considered to be the most appropriate classification method to use, particularly when the data or mapping system are not well understood.

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o Quantiles

distributes data evenly across the available groups, resulting in consistently colorful maps. For example, when classifying 12 countries into 3 groups by population, the 4 countries with lowest populations will be in the first class, the next 4 countries in terms of population will be in the second class, and so on. Unfortunately, it tends to hide extremes (highs and lows) in the dataset and may also "wash-out" natural breakpoints in the data.

o Equal Interval

divides data into equal intervals. The differences between the low and high data point in each class is the same. If we use our population example again and assume that the range of populations goes from zero people to 12 million people, all the countries that have 0-3 million people will fit in one class, the countries with 3-6 million in the next class, 6-9 million in the next, and so on.

o Defined Intervals

is similar to Equal Intervals. Defined Intervals allows the user to select the width (upper to lower limit) of the interval. In many cases, this causes the number of classes to increase or decrease. In this example, the defined interval was set to 100,000 causing an additional four classes to be automatically created. In ArcView 8.x, Defined Intervals are set to Equal Intervals by default.

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o Standard Deviation

classifies features by how much data values for that feature vary from the mean of all the values. In our population example, our tiny country with only 100 people will probably vary from the mean a great deal, thus showing up with a highly negative standard deviation and probably mapped in its own class.

Standard deviation maps often use a bivariate color scheme. For example, data with sub-average values may be mapped in shades of red, while on par data are mapped white and data with above average values are mapped in shades of green.

o Manually Created Classes is generally the least appropriate way to classify data. By creating your own classes, you are reducing the statistical significance of your data, and your map may be considered haphazard or even dishonest. You should only manually create classes if you are looking for your data to meet certain criteria. For example, if you are working with a specific regulation that requires a certain percentage of a state's water to be polluted, then you will want to manually change the classification to delineate at that given percentile. However, most of the time, you will want to use one of the standard classification schemes listed above.





Chapter 2: Volumetric Mapping

A GIS can not only map data in two dimensions, but also in the third and even fourth (time) dimensions. Volumetric mapping, typically, is used to depict perspective height at some location on a map. For example, elevation models often display x, y, and z coordinate data, creating a virtual landscape. Hills, valleys, and plains emerge when the z value (vertical height) in the elevation data is displayed. With a GIS, even relatively flat terrain, like the plains of Kansas, can be exaggerated to better emphasize geographic features for students.

Types of Volumetric Mapping As a number of attributes could be mapped with perspective height, geographers differentiate volumetric mapping into "2½" and "3" dimensional mapping. It is important to understand that most maps identified as "3D" are (cartographically speaking) improperly labeled. What you likely know to be a "3D" map is most likely a "2½ D" map.

A "2½ D" phenomenon is typically a map of a surface. For example, an elevation model is (strictly speaking) fodder for a 2½ dimensional map. Elevation models typically contain x, y, and z data as they contain a latitude, longitude, and elevation. They offer no additional information (attributes) about the x, y, z position (such as air temperature at that x, y, z position). As such, elevation models are useful for creating simple surfaces. If each latitude/longitude point has single value, then it would most likely be a 2½ dimensional phenomena being mapped. Other examples of 2½ dimensional phenomena include a map of rainfall or snowfall. Each of these phenomena has up to one value for one geographic location (x, y).

Thinking Question: What physiographic conditions might cause "elevation" to be consider a 3D phenomenon?

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A 2½ dimensional phenomenon can also include the mapping of other social, aggregated, or abstract variables where one attribute (z) is mapped at one geographic location (x, y). For example, prism maps, or perspective maps, are similar to choropleth maps, where the attribute's abundance is indicated by vertical height. Prism maps are often used to map demographic, political, or other sociological data. In the prism map to the right, mobile homes per square mile are plotted on the z-axis. Notice that the abundance in Florida is so large that Georgia and other states are blocked from our view. This is one criticism of this map type. To correct this problem, we might adjust the exaggeration of the z-axis or simply rotate the map. In what parts of the country are mobile homes most dense? Why do think this pattern exists? What other data might be interesting to map with mobile home density?

True 3D phenomena would include at least 4 pieces of data: latitude, longitude, a z value, and an attribute at the x, y, z coordinate. Most often, true 3D phenomena has multiple z values with attribute data (height data with some attribute at the specified height). Such things as atmospheric temperature, atmospheric CO2, or the constitution of geologic strata would represent 3D phenomena. Each 3D phenomenon occurs at multiple times for one geographic location (x, y) and has attributes that vary with the height. True 3D phenomena do not simply create a hollow surface; they create a 3D shape filled with data describing the phenomena throughout the volume. Relatively little research and development work has been conducted in mapping true 3D phenomena. Even with the use of extensions such as ESRI's 3D Analyst, true 3D GIS mapping is still in its infancy and quite difficult, if not impossible.

Collecting and Recognizing Volumetric Data Creating surface or 2½ D data is a relatively easy and useful task. Oftentimes, acquiring elevation data of school property and the surrounding neighborhoods can be acquired through USGS topographic maps, surveying benchmarks, and some GPS units. While the most basic GPS units will provide extremely sporadic elevation readings, newer, more costly models can provide elevation data reportedly accurate with a few feet. The significance difference between newer and older GPS units is the way in which elevation data is acquired. Early, inexpensive GPS units (under $150) used GPS satellites to estimate elevation. Due largely to variations in atmospheric conditions and technical factors, the elevation provided by older GPS receivers was seldom precise or accurate. Newer GPS receivers now use barometric altimeters to determine elevation, such as the Garmin eTrex Summit model (retails about $225). These models provide enough elevation accuracy to map school grounds, potentially creating surface models.

Creating volumetric maps Using GIS extensions, such as ESRI's 3D Analyst, realistic surface maps can be created using perspective height. Maps,

http://gis.kuscied.org/online/course_contents/mod8/img/eTrexSummitBig.jpg

http://gis.kuscied.org/online/course_contents/mod8/img/arcscene.gif

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such as elevation maps, can be created by imported USGS Digital Elevation Models (DEMs). A map can be "flown-through" as a bird and, then, even exported to a digital movie or virtual landscape (using VRML technology). These exported movies and landscapes can be placed on the web or presented as a stand-alone multimedia file.

Two sample virtual landscapes have been created using the ESRI 3D data pack. These virtual landscapes must be viewed using the Cosmo VRML player which must be downloaded and installed. If you are using a computer where file download or installation is difficult, feel free to not view these two files. Download the VRML viewer, Cosmo at http://www.cai.com/cosmo/. The version identified for Win 9x or NT will also work under XP and 2000. Once you have downloaded and installed the application, you may click on either link below. The player's controls are somewhat difficult to master.

• A basic elevation model with buildings: http://gis.kuscied.org/online/course_contents/mod8/3d/1/s1.wrl (2.5MB)

• An aerial photo with buildings, roads, and wells all atop an elevation model: http://gis.kuscied.org/online/course_contents/mod8/3d/2/s2.wrl (4.8MB)





Chapter 3: Animation

Animation is the display of similar images in rapid succession. Animations are generally composed of ordered frames (still images) that are assigned a duration of view. Dibiase & McEachren (in Slocum, 1999) have argued that geographic animation is used for one of three purposes:

1. To emphasize change in a position or attribute of a phenomena. This group includes common time-series and fly-through animations.

2. To emphasize location of a phenomena. A "flashing point map" indicating the location of a thunderstorm would hallmark this category.

3. To emphasize an attribute of a phenomena by highlighting selected portions of it. In this type of animation, map symbols may be temporally displayed such that lesser value points appear first on a map, following by medium valued points, and finally greater valued points appear on the map.

Rationale for Using Animation Animation holds a unique place in instructional graphics, in that animated images are typically able to convey large amounts of information in a relatively short period of time. Animation has been used in the classroom with a variety of media and content, ranging from paper "flip-books" to video discs and computing applications.

Inherently, animations are able to leverage real time in conveying a phenomenon that is time-sensitive. This temporal element may provide a valuable advantage for observing some complex phenomena, particularly when coupled with maps. In fact animation is very similar to map making in that each leverage has a physical property to represent a value. In the case of map making, space on a map is used to depict physical space at some scale, extent, and perspective. Although educational research in animation is generally quite supportive of using animation for instructional purposes, it does have dissenters.

Creating Animations Animations can be created in a number ways, largely dependent upon the level of interactivity that the end user needs. For most purposes, a series of images looped continuously without user controls is suitable.

Using a GIS (such as ArcView or ArcGIS), a number of data layers can be created with each layer representing a distinct period of time. For example, a series of data layers may display the reported number of AIDS cases. Each data layer in the series represents the reported AIDS cases for a given year. It is imperative that the data classification (including color selection) be identical across all data layers. With a consistent legend throughout the animation, the animation sequence will likely lead viewers to correct conclusions.

After the data has been prepared, an image of each data layer (by year) is exported as an image file. Typically, it is helpful to name each image with a sequential file name (i.e. image1.jpg, image2.jpg, image3.jpg, etc).

Using a graphics editing program (like Macromedia Fireworks, Jasc Paint Shop, or Adobe Image Ready), all map images can be imported for the animation. Initially,

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images will need to be properly sequenced and then set to a specified time frame. A title, legend, and credits can also be added. The graphics application will then save the file sequence in a GIF or AVI (movie) format, suitable for exchange or publication across the web, email, or even Microsoft Office documents.

A simpler approach could be taken by using the Tracking Analyst extension of ArcView 3.x. Rather than exporting a series of frames from ArcView, the tracking analyst extension is especially designed for temporal data sets. After a tracking theme has been established using spatio-temporal data, the sequence can be animated from within ArcView GIS. The screen shot illustrates the interface controls and the general movement of a particular event. There may scripts available at ESRI for converting these sequences directly to MPEG for non-interactive, yet versatile, viewing.

ESRI's 3D Analyst will also allow for the creation of "fly-throughs". Unlike the previously mentioned animations, "fly-throughs" do not animate a specific sequence of time. A user may create a map and fly around it from a bird's eye view. In the example list below, an extensive 3D animation was created by graduate geography students at the University of Kansas. To create a simplified fly-through experience, 3D Analyst with ArcView 8.x can be used relatively easily, allowing for nearly instant movie creation of a flight path.

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Another method for creating an animation with basic user controls could consist of using Macromedia's Flash. While the Flash application does allow for interactivity not supported by animated GIFs and is still "Internet-friendly", it has not in the past lent itself easily to animated mapping. This is changing rapidly. The typical procedure for using Flash follows very similar lines to graphic applications (described above). The frames of the sequence must be exported from the GIS as map images, and then loaded into Flash. Titles, legends, credit, and north arrows can be overlaid atop the entire sequence. Flash allows for some user controls, such as stopping, starting, or progressing the animation without extensive programming or scripting. The image below depicts one frame of a Flash-based animated map with controls.

Examples of geographic animation

• Lawrence, KS: Volumetric Mapping and Animated Fly-through (in various file sizes and formats) This animation depicts a "fly-through" of Lawrence, KS approaching town from the north into downtown. Near the end, the fly-through stops to look at the county.

o Quicktime: Small File http://gis.kuscied.org/online/course_contents/mod8/animation/LawKS_QTlittle.mov

o Quicktime: Medium File http://gis.kuscied.org/online/course_contents/mod8/animation/LawKS_QTmed.mov

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o Microsoft Media Player: Small File http://gis.kuscied.org/online/course_contents/mod8/animation/LawKS_MS_intellistream.wmv

o Microsoft Media Player: Large/High-Quality File http://gis.kuscied.org/online/course_contents/mod8/animation/LawKS_MSbig.wmv

Thinking Question: What kind of animated geographic data could serve to support your present instructional practices? Be as specific as possible in your responses.





Chapter 4: Virtual reality and Immersive Technologies

At this point, you are well on your way to understanding the benefits of GIS as a two-dimensional mapping and data analyst tool. As chapter 3 touched on, hardware and software exist to explore our world, using GIS, three-dimensionally; however, much of this technology is not readily available to the everyday user. It is important to know what does exist, what you can do with your GIS, and what GISs of the future will look like.

We have all heard of virtual reality, if not in terms of scientific modeling, definitely in terms of video games and science fiction. For our purposes, virtual reality "refers to the use of computer-based systems for creating lifelike representations of the real world" (Slocum 1999). Visual realism and visual simulation are terms used synonymously with virtual reality to stress the importance of vision in representing reality.

Two major projects in the arena of virtual reality are being developed at the University of California at Los Angeles. Virtual Los Angeles aims to build a visually realistic model of the Los Angeles basin, and Rome Reborn attempts to recreate a three-dimensional representation of ancient Rome (Slocum 1999; Davis 1997; Bressi 1995). In digital models like these, a user has the options to explore the model by virtually flying through it, walking through it, or driving through it. Three-dimensional models are obviously not only useful representations of a location, like a map might be, but also enable a user to explore a location and model a real-life phenomenon through the representation. The following examples will begin to reveal what advancements have been made in virtual technology.

"The Wall": Collaborative Visualization Virtual reality projects are being developed at the University of Kansas in order to help visualize complex phenomenon and, in some cases, bridge the gap between scientific discovery and decision-making. Currently, various Research Projects in Interdisciplinary Collaborative Visualization (http://jade.designlab.ku.edu/~miller/CVR/) are using highly advanced technology to model certain phenomena. A wall-sized projection screen receives images from numerous projectors driven by a six-processor SGI Origin 2000 computer and three InfiniteReality2 graphics subsystems. The Collaborative Visualization Room, where this cutting edge technology is housed, is a multidisciplinary laboratory in which research projects are born, explored, and brought to life. Visualization is being used by many disciplines, including climatology, biology, and remote sensing, for education and research.

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An important aspect of three-dimensional digital modeling is predicting and measuring error and uncertainty in your model. Many of these advanced technologies allow for such an assessment in order to make a model not only personally useful, but useful for reliable and significant research and real-life decision-making.

GENESIS Earth Systems Model GENESIS is a project funded by the US Environmental Protection Agency. "The GENESIS Earth Systems Model consists of an atmospheric general circulation model coupled to multi-layer models of vegetation, soil, snow, sea ice, and general oceanic circulation" (see GENESIS Model Info at http://www.essc.psu.edu/genesis/geninfo.html). These layers, like in a GIS, allow the user to more fully explore, visualize, and understand real-world phenomena. In this case, GENESIS uses visualization models to help analyze global climate patterns and better communicate their findings. The products they produce are useful for both students and educators.

GENESIS visualization projects cover a wide range of topics on the global scale from modeling general circulation changes, to biospheric modeling, to modeling

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ocean currents. More images and example models are available on the GENESIS Earth Systems Model (http://www.essc.psu.edu/genesis/genesis.html) website.

Immersion Technology Along with modeling and visualizing, technologies are continually improving that allow you to use your body to experience virtual reality. Two of these are the Head Mounted Displays and Glove Input Devices. These technologies can be used for a range of activities from entertainment to medical research.

Head-Mounted Display P5 Glove

The P5 Glove (http://news.bbc.co.uk/1/hi/sci/tech/1768818.stm), developed by Essential Reality, (shown above) is the newest development in glove immersion technology. Although glove technology has been around for a while (for you who were children of the '80s or knew a few might remember the old Nintendo power glove), it has been generally awkward and coarse to use. The P5 Glove's lightness and utility make it suitable for future use in military, scientific and medical fields, even though it is currently marketed for utility in video games.

Researchers at the University of Buffalo are using similar technologies to revolutionize the medical field (http://www.hoise.com/vmw/00/articles/vmw/LV-VM-09-00-5.html) especially concerning medical examinations. The UB research team is constructing a computer based model of the soft tissue and organs of the human abdomen. The system they are developing will enable physicians to store information about what they feel during an examination. This data will then be available for future use by other physicians at other locations. Likewise, delicate medical procedures, such as brain surgery, can be practiced and perfected by new physicians by using similar models rather than mannequins that poorly mimic the responses of the human body. The system developed by UB runs on a Windows computer and allows physicians to explore three-dimensional images simulating surgery.

Immersive Computing Applications Applications such as TruFlite or TerraVision lend themselves well to creating virtual worlds from given data. Unlike some of the applications mentioned above, SRI TerraVision and the Digital Earth application are accessible to the public and can be accessed from computers with fast Internet connections. The application handles downloading, resolving, and creating 3D-like VRML models for users to create qualities approximating immersion into the environment (Leclerc et al., 2000; Reddy et al., 1998).

In general, these so-called virtual environments are gauged based on four characteristics: interactivity, immersion (or the sense of "being in"), detail of

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information, and the intelligence (or context-sensitivity) of objects (MacEachren, Kraak, Verbree, 1999). These characteristics describe an environment on a continuum, where immersive 3D technologies lie on one end and more abstract, non-immersives lie on the other end. Unfortunately, most of these immersive or near-immersive applications are beyond the technical scope of many public K-12 school environments.

Thinking Question: Is there a legitimate place for virtual reality and immersive technologies in K-12 education?

References:

Bressi, T. (1995) "The real thing? We're getting there." Planning 61, no. 7:16-20.

Davis, T.J., and Keller, C.P.(1997) "Modelling and visualizing muliple spatial uncertainties." Computers & Geosciences 23. no. 4:397-408.

Slocum, T.A. (1999) Thematic Catography and Visualization. Upper Saddle River, NJ: Prentice Hall.

