1. Kristin Gunckel
  2. Associate Professor of Science Education
  3. Comp Hydro:Integrating data computation and visualization to build model-based water literacy
  4. http://ibis-live1.nrel.colostate.edu/CompHydro/
  5. University of Arizona
  1. Alan Berkowitz
  2. http://www.caryinstitute.org/science-program/our-scientists/dr-alan-r-berkowitz
  3. Head of Education
  4. Comp Hydro:Integrating data computation and visualization to build model-based water literacy
  5. http://ibis-live1.nrel.colostate.edu/CompHydro/
  6. Cary Institute of Ecosystem Studies
  1. Randy Boone
  2. http://randallboone.org
  3. Professor
  4. Comp Hydro:Integrating data computation and visualization to build model-based water literacy
  5. http://ibis-live1.nrel.colostate.edu/CompHydro/
  6. Ecosystem Science and Sustainability, Colorado State University
  1. Bess Caplan
  2. Ecology Education Program Leader
  3. Comp Hydro:Integrating data computation and visualization to build model-based water literacy
  4. http://ibis-live1.nrel.colostate.edu/CompHydro/
  5. Cary Institute of Ecosystem Studies
  1. Beth Covitt
  2. Head of Science Education Research & Evaluation
  3. Comp Hydro:Integrating data computation and visualization to build model-based water literacy
  4. http://ibis-live1.nrel.colostate.edu/CompHydro/
  5. spectrUM Discovery Area, University of Montana
  1. John Moore
  2. Professor and Head
  3. Comp Hydro:Integrating data computation and visualization to build model-based water literacy
  4. http://ibis-live1.nrel.colostate.edu/CompHydro/
  5. Natural Resource Ecology Laboratory, Colorado State University
Public Discussion
  • Icon for: Kristin Gunckel

    Kristin Gunckel

    Lead Presenter
    May 13, 2018 | 05:50 p.m.

    Welcome to Comp Hydro. We are a design-based research project focused on integrating computational thinking into learning about water in environmental systems. We have four place-based curriculum units, each highlighting water issue relevant to the students we are working with. Our work has also included significant innovation to overcome computer infrastructure challenges in the schools. We are also currently using data from assessments we designed for these units to build learning progressions for computational thinking and interpreting data representations such as maps and cross-sections. We are happy to answer any questions you have about our instructional approach, activities, and Net Logo models (highlighted in this video), as well as our technical infrastructure and our learning progressions research.

     
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    Dan Moreno
  • Icon for: Alan Peterfreund

    Alan Peterfreund

    Facilitator
    May 14, 2018 | 11:34 a.m.

    Thanks Kristin for an interesting description of your project.  How are you collecting feedback on the utilization of the modules?  

     
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    Dan Moreno
  • Icon for: Kristin Gunckel

    Kristin Gunckel

    Lead Presenter
    May 14, 2018 | 12:30 p.m.

    Hi Alan,

    Thank you for your question. At each of our sites we are working in close contact with the teachers using the modules. For example, in three of our sites our project staff are able to visit classrooms to provide instructional support to the teachers. (Our fourth site is not able to visit as often because the teachers are spread across a large state with a mostly rural population). Although we are not collecting formal data during this process, these visits do help us learn how the teachers are using the modules and what challenges and successes they and their students are experiencing. In response, we have been able to make revisions to the modules, sometimes in real time, that make the lessons and activities work better for students and teachers.

    In addition, we conduct focus group interviews and one-on-one interview with the teachers. Our interviews ask the teachers about how they used the modules, what they found useful and helpful, how they modified the lessons and for what reasons, and how teaching the lessons influenced their teaching and their ideas about teaching computational thinking. We also have a lesson-by-lesson survey form that all teachers complete that documents which of the lessons in the modules they used, what modifications they made to the lessons, and which lessons they did not use and why. This survey gives us documentation on the implementation, helps us understand teachers' successes and challenges, and provides additional suggestions for revisions.

    Overall, we have learned that most of the teachers are finding the modules useful and supportive of student learning. most of the teachers are teaching all or most of the lessons in the modules. We also made several modifications based on teacher feedback. For example, last year we added new activities based on some teachers' suggestions. We also learned that teachers wanted additional supports, including powerpoint intros to lessons and formative assessment. We believe this is because for many teachers, teaching computational thinking is new and having additional resources available increases the likelihood that they will teach the modules. Overall, we are pleased with the teachers' uses of the modules and feel that they have genuinely embraced the purposes of the project and their role as collaborators in the design and implementation of the modules.

     

     
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    Dan Moreno
  • Icon for: Angie Kalthoff

    Angie Kalthoff

    Facilitator
    May 15, 2018 | 11:11 a.m.

    As an educator, I love that this project is based on real-world problems that students may be personally connected to or hear about in the news.

    I am wondering how students reflect on their learning throughout the 4 curriculum units?

    At the end of the project, how are students encourage to participate in helping their community? "We hope that students will then be able to use these practices to participate in community problem-solving around the water issues that affect them most."

     
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    Dan Moreno
  • Icon for: Kristin Gunckel

    Kristin Gunckel

    Lead Presenter
    May 15, 2018 | 12:17 p.m.

    Hi Angie,

    We appreciate your question because the real-world connections are important to us too.

    Students are reflecting on their learning about the contextualizing problem throughout the units. Lessons includes questions that ask students to think about how what they learned about water, maps, or computer models helps them understand a local problem. For example, in one of the units on groundwater contamination in Tucson, students see a map that shows massive wells installed to pump and treat the contamination from the aquifer. A common question students ask at the beginning of the unit is why the wells are far from the source of contamination. After a lesson on the shape of the groundwater table and the drivers of groundwater flow, students use what they learned about groundwater flow to explain the placement of the pump-and-treat wells at the leading edge of the contamination plume. The then reflect on how their understanding of the problem has changed.

    All of the units end with a problem-solving activity. For example, in the Maryland unit on flooding, teams of students work together to develop and present options to mitigate flooding in their own school yards. In the Montana unit on groundwater contamination, students use the computer models to develop ways to clean up the pollution. In Arizona, a small set of students also presented at a community event what they learned about the groundwater contamination in their neighborhoods.

    What we often hear from teachers and students in interviews we have conducted is that participation in the lessons has helped students better understand the water issues that face their communities. Students talk about how they were uninformed about groundwater contamination or flooding threats, but now they are better aware of the situation and more informed about the impacts on their own lives.

     
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    Dan Moreno
  • Icon for: Angie Kalthoff

    Angie Kalthoff

    Facilitator
    May 16, 2018 | 09:24 a.m.

    Thank you for your response.

    Since your project started in 2015, have you followed students who were part of the project at the beginning? Have any of the participants continued on to pursue a career in community problem-solving or a degree in higher ed due to their experience with these projects? 

     
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    Dan Moreno
  • Icon for: Bess Caplan

    Bess Caplan

    Co-Presenter
    May 16, 2018 | 09:31 a.m.

    Hi Angie.  I'm the facilitator of the project in Baltimore.  We don't follow student participants for these purposes.  However, one of the Baltimore students who participated in Comp Hydro during the 2017-2018 school year, approached me after the conclusion of the project and asked if I knew of any summer jobs in environmental science for high school students.  One of our partners in the City runs such a program and I connected this student with that program and he not only got the summer job but has continued to work with this organization through the current school year.  

     
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    Angie Kalthoff
  • Icon for: Karthik Ramani

    Karthik Ramani

    Facilitator
    May 15, 2018 | 11:03 p.m.

    Wow cool - nice way to learn about the environmental issues in the use cases, and basic principles of hydrology - exposing it via visualizations, understanding data representation and critique of results. Computational models form the heart of the learning process. Question: how much in depth the models for waterflow themselves are exposed to the students. Do they see or get a feel of the finite difference type of equations that are used for modeling? I can see they get a good feel for how to use the tool and question the results. Do they learn the assumptions of the model (in depth)? Overall nice explanation through the video that was very well constructed. 

     
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    Dan Moreno
  • Icon for: Kristin Gunckel

    Kristin Gunckel

    Lead Presenter
    May 15, 2018 | 11:40 p.m.

    Hi Karthik,

    The students do not interact directly with the equations used in the models. However, we do use the models to teach some of the principles of computational modeling. For example, by comparing models with different numbers of data points, they learn about relationships between data and uncertainty and also learn how computer models infer between data points. They also learn that more data results in more accuracy in the models, but also requires more computational work. In addition, they learn how data are rasterized, and how modelers identify parameters based on principles of hydrology to write the "rules" for moving water through a model. We think that these ideas lay a foundation for later working with equations and analyzing assumptions.

     
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    Dan Moreno
  • Icon for: Karthik Ramani

    Karthik Ramani

    Facilitator
    May 17, 2018 | 01:09 p.m.

    Got it. So this is the early experiences that the learner can become more curious, ask questions, and then prepare them for later deep dives. Are they asking questions that suggest that this is preparing them for later deeper dives into some of the areas they are exposed and interact with such models. It will be good to tell them what is happening behind (in simpler terms) without scaring them, and that is the basis for being able to do predictions and simulations - gaining deeper understanding of even climate change and other phenomena.

     
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    Dan Moreno
  • Icon for: Alan Berkowitz

    Alan Berkowitz

    Co-Presenter
    May 17, 2018 | 02:35 p.m.

    Greetings, Karthik.

    We actually have a nice range of implementation contexts in the Comp Hydro project. In Baltimore, some of the classes are in computer science, and in these cases students are applying what they already know about programming to interpret and critique the Net Logo models they work with in Comp Hydro. Other Baltimore classrooms,  however, are engaged in computational thinking via 'be the model' simulations like the one shown in the cover photo for our video. In Tuscon, for instance, earth science students each with their own chromebooks are interacting with the models via a web interface we've created for this purpose. These students are gaining the kinds of preparatory experience you are describing, building their awareness of the basic affordances of the models and some of their requirements and limitations. In the Montana case, students are interacting with scientists' and their sophisticated models more directly and thereby seeing how algorithms, parameterization, descretization and boundary conditions play out in models being used to address the real world issues they're learning about. We're in the midst of interviews with students to help us understand what they are taking away from this broad range of types of engagement with the modeling which, along with our written assessment, should give us a rich set of answers to your questions ... and, of course, a new set of questions.

     
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    Dan Moreno
  • Further posting is closed as the showcase has ended.