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Providing and Using Scientific Data Through NSDLCathy Manduca, Carleton College Ben Domenico, University Corporation for Atmospheric Research David Halle, University of California at Los Angeles Andrew Beveridge, Queens College
Susan Van Gundy, University Corporation for Atmospheric Research New technologies are facilitating unprecedented access to and techniques for interpreting complex datasets, as well as data sharing across disciplines and beyond traditional communities of scientific researchers. Authentic inquiry-driven experiences are being increasingly proven to have positive impact in K-12 and undergraduate education, stimulating a demand for data resources that are engaging and easy to use for non-technical audiences. This session will explore multiple aspects of providing and accessing data through NSDL including technical challenges, evidence from classroom use, new visualization tools, and discussion about how NSDL can best support the differing needs of researchers and educators. Notes - Providing and Using Scientific Data Through NSDLExamples of Data ProvidersMapping Application to look at change over time- racial change in New York City.
Photo Collections:
Unidata THREDDS: Making Distributed Datasets more available and usable in NSDL
Questions: Who is using this, what level, how any? Almost entirely at undergraduate level. Universities (80-90) have created on products, based on Unidata technologies, for K-12 environment. One of the main activities of THREDDS is working with DLESE to run workshops- one per year. We bring in technical people, data providers, and content developers and see how we can build modules that can be applicable to middle school environments. Have to be a Unidata school to use and access? No. Anyone who is in educational world can work with us. We have hardware grants available. No formal membership is necessary. How are end users at university level using data?Work by Cathy Manduca and David Mogk, out of CI sponsored workshop- "Using Data in Undergraduate Science Classrooms"- hard copy available
Repeating Themes-
Discussion: There are some circumstances were you want students to understand how to fill in holes in data, other times you dont. Teaching data literacy is important in higher education and K-12. There is also a need for highly annotated data sets where you know how the data collection took place. In K-12, there is a need to have data posted with caveats. At undergraduate level, use goes from just wanting a picture to wanting all of the information. Experts have own methods for evaluating data quality- expectations based on where data comes from- once you move out of the expert user, you dont see that ability to evaluate the data. Discussion:
No. Using Data study didn't focus on this. Issues come up in issues around tools. Dont want to have to use different tool for every data set. Want to use one tool. Discussion about how people want to find and combine data sets. Expect data to be combined so didnt really focus on this.
Earth Exploration Tool Book- K-12 experience, in testing stages
Interoperability Issues, Emerging Protocols Discussion TranscriptOpen GIS protocols for accessing data and creating metadata. How do you represent units and geographical location of data sets? Has to be there in some way that you agree on.Problem not the protocol, but the data. New York City has done fly-over to create new map/ GIS. Suddenly all census track maps are obsolete. Doesnt matter which standard they are in, they are wrong. Problem for GIS stuff is the data. There are mapping packages that allow you to translate the data. Protocol/ standard debate irrelevant. If data is not measured the same way, they arent going to line up. Problem neither. People arent used to learning from data. We dont understand how people learn from data. Data doesnt persuade students. Zero power to overcome the power of a sentence that students read in a textbook. Text carries more weight than data set for students. How do we help people learn form data? How to teach with data is the problem, not what standards to use. Ex: Abstract data into stories. Abstracting data into stories is problematic because then you are teaching through text, not teaching to read data. Have students create own stories out of data, dont tell them the stories. Practice reading charts and data until comfortable doing so. Practice is part, not alone More fundamental than this. Being able to deal with data period. Practice is very important. Dont do evaluation of data we want them to. With data they have to analyze. Easy to understand and repeat stories/ text. Analyzing data is a complicated notion. Bring expertise to graph when analyzing data. Need to teach students to bring that expertise or to find out what they dont know to be able to analyze. Problem isnt the data itself, but translating data to text. It's easier to respond to data questions using numbers than translating responses to text. Problems in translation form one mode of though to another. There is huge power in narrative, but perhaps first step has to be in students creating own narrative. Make own observations and drawing collections. Questions of how people learn in terms of meta-cognition. Importance of assessment in overall learning Way of assessing how students are interpreting data- going back and forth between numbers and text. Issue of made-up data in K-12 classroom: examples look like data, but are clearly made-up. Why is this so prevalent? Math community has a problem with this fake data. Have to go further than giving students fake data. Have to give students more authentic data so they can translate this to the real world. Make examples cleaned-up, real data Ability to understand a graph is one predictor of success in science courses in undergraduate. Long path between getting students from point of recognizing data to point of interpreting data. CommentsPlease enter any comments in the following format.
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