About Nearby Data

Continuation and expansion of a model for incorporating relevant data technologies into classroom curriculum, with the education expertise of West Liberty University and technology expertise of the CREATE Lab.  

The Nearby Data Project is a partnership between the CREATE Lab of the Robotics Institute at Carnegie Mellon University and the Center for Arts & Education in the College of Education and Human Performance at West Liberty University. The project provides students hands-on, real-world exploration of cutting edge data mapping tools, on a local, national and international scale. The Nearby Data Model encourages students to not only understand big data systems, but also notice and wonder about how their data is being used and how data is being used to represent them.  Now in its fourth year, the project reinforces the value of building lasting relationships and provides spaces for technology discussions. Offering teachers regular discussion points and ways to think about technology empowers them to take on the challenge of integration and understanding.

 

The Nearby Data Project combines technology innovations from the CREATE Lab and existing public source data visualization platforms with data visualization activities and classroom implementation support from West Liberty University. In addition, these technologies and resources enable teachers and students to explore their world while learning basic map reading concepts and deeply exploring data sets.

 

One of the resources used in the Nearby Data Project is EarthTime, a tool developed by the CREATE Lab. Earthtime enables teachers and students to explore the data visualizations being used by experts at places like the World Economic Forum and in the book ‘Terra Incognita.”

Project team led session at West Liberty University Campus October 21st. Educators from the tri-state area gathered in The Center for Arts and Education to share best practices and data literacy insights.

Paul Dille , Ryan Hoffman of CREATE Lab 

Lou Karas, The Center for Arts and Education

Nearby Data is:






The prelude to Nearby Data, Nearby Nature, was designed to get students exploring invisible aspects of the environment from their own classrooms, challenging the notion that field work is required for a genuine learning experience. Students measured a variety of environmental factors from classroom air quality to ground cover outside of their school.  Nearby Data builds on this by allowing students to examine how big data is visualized, from their own classrooms, and incorporate data visualizations into their current curriculum. 

Like Nearby Nature, it is adaptable to any classroom setting with an internet connection. Students will not only learn core concepts but also learn to critically examine how data is being represented and why it is represented that way. Students and classrooms will have the opportunity to manipulate data visualizations and visualize their own data. Advanced classrooms will work directly with CREATE Lab software developers to adapt the EarthTime data visualizations to their classroom setting by visualizing new datasets and exploring new ways to represent data.

Educators working with Earth Time, Terra Incognita, and other resources made available through The Center for Arts and Education in the College of Education & Human Performance at West Liberty University.

Nearby Data partners in WV, OH and PA can anticipate:

The Fluency Project

https://www.fluencyproject.org/

Constructivist learning concepts, adopted from the Fluency Project.


For example: learning how to look for census demographic data trends on a map also teaches students about census data collection and classification. 


For example: Defining terms like scales / keys / compasses and exploring  different ways to visualize data


We will design activities that to be done as a group to encourage  “Parking lot” questions and observations


We learn in relation to what we know -  Where do the observations come from?

Students will observe data visualizations and question where their observations come from

For example: personal experience / something they read / social media / etc