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.
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:
Providing teachers tools and tutorials for implementing data visualization exploration in their classrooms
Meaningfully integrating EarthTime and other mapping tools into curriculum
Offering local (or online if necessary) professional development and support through CREATE Lab and West Liberty University
Aligning professional development content and materials with state standards and assessment requirements
Fostering an environment where students and teachers generate unanswered questions about the data visualizations
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:
Learning new technologies and applying them across curriculum in the classroom
Performing genuine inquiry around data visualizations and learning to manipulate open source data tools with real world application
Further engaging students with hands‐on and relevant lesson plans
Engaging students in constructivist learning principles. This is important because the way students interpret an explore data reveals their own bias and ingrained learning as much as it shows wider trends
Teachers designing and implementing project‐based learning, developmentally appropriate ways, using data collected and/or compiled by students
Using other existing CREATE tools
Developing a more accessible model of EarthTime, with fewer data layers, for younger students
Exploring the intersection of low tech vs high tech through digital polaroid cameras, Canva and other introductory tools
Building foundation with younger students so that this type of work can be continued through high school and beyond
The Fluency Project
https://www.fluencyproject.org/
Constructivist learning concepts, adopted from the Fluency Project.
Learning consists both of constructing meaning and constructing systems of meaning.
For example: learning how to look for census demographic data trends on a map also teaches students about census data collection and classification.
Learning language: Nearby Data will create a glossary of terms used in data visualization
For example: Defining terms like scales / keys / compasses and exploring different ways to visualize data
Make learning a social experience
We will design activities that to be done as a group to encourage “Parking lot” questions and observations
Learning is contextual
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