In NGSS Appendix D: All Standards, All Students, building community connections was recognized as an effective strategy for science education. Students are more interested and engaged in their learning when it is centered around topics that have relevance to their lives.
Therefore, integrating local examples and phenomena into instruction is a natural way to maximize student engagement. There are many ways to integrate such examples into your instruction (e.g., articles, videos).
We see value in diving into local data to leverage the learning you want to happen in your classrooms. We define local data as data from a student's city, county, state, or region. Because “local data” is often more about relevance and meaningfulness than about a physical boundary, it can pertain to different geographic scales. This data can be collected by students, citizen science projects, or through a curated Tuva dataset.
Tuva works to support uses of local data in classrooms throughout the country in a variety of ways.
Widespread and Unique Phenomena
Local data can be leveraged for deeper learning of widespread issues, phenomena, and/or processes by using data from where you live and what you are more familiar with. Rather than looking at data from across the country or from a different country (i.e., a place your students may have no experience with), use data from your schoolyard (or town, county, state, etc.) to investigate issues, phenomena, and/or processes.
It makes sense for students in Arizona to study the concept of abiotic and biotic relationships by exploring data from desert ecosystems. Similarly, it would likely be more meaningful for Michigan students to study abiotic and biotic relationships using data from northern hardwood forests or from Great Lake ecosystems than the desert ecosystems. Once students leverage their understanding go the surrounding area to gain a sense of place, they can use data to see how it compares with other places.
Another way you can leverage your students learning by using local data is when a phenomenon is unique to a specific location, in other words it is a localized phenomenon, and thus to investigate that phenomenon you need data from that area to understand it. Rather than just reading about it, dive into the data to explore it! Additionally, you can use local data to more deeply teach the process of science as your students collect their own data to explore their questions. Use Tuva’s search filter to find "local" data for your students using key words or names for the places, issues, and phenomena that your students care about or you want to bring into your teaching.
Geographic Scales of Local Data
As stated earlier, “local data” is often more about relevance and meaningfulness than about a physical boundary, it means that “local” data can pertain to different geographic scales. So, for example:
1. When studying about the growth, development, and reproduction of organisms:
- You could look at how the behaviors of cardinals affect their probability of successful reproduction, so local would be the area around the schoolyard (and maybe beyond) that the cardinals use.
- Also, you could look at how the behaviors of moose in your state affect their probability of successful reproduction, so local would be the area of the state that the moose use.
2. When exploring weather and climate:
- You could look at how weather in a certain location changes over time (e.g., your school), so local would be the area around the schoolyard determined by where your data collection instruments are setup.
- Also, you could look at how air masses flow from regions of high pressure to low pressure and how that causes the weather in a certain location to change over time then local is the broader region around your school that air masses move through.
- Additionally, studying “local data” at different geographic scales provides a great opportunity for students to articulate their understanding of the crosscutting concept of scale.
Explore the many sources of local data
Local data comes from many different sources, both primary that you collect yourself and secondary that others collect but you use. At Tuva, we support and draw from the following sources for our datasets that can be used to leverage your learning objectives:
- Your students collect their own data.
- Citizen science projects around the world collect data for a wide range of issues, phenomena, and processes. If any exist in your area, consider using the data and potentially getting your kids involved as citizen scientists themselves.
- Scientists at various town, county, state, and federal agencies collect data related to their agency’s mission. These data are paid for by taxpayers dollars so they are available to access. Many are already online, but you can also email an agency and request data you cannot find online.
- Scientists at universities and research institutions collect a wide range of data for their research, which they publish in peer-reviewed science journals, and often make available through online databases. If you learn of published research that you would like to find data for, you can email a scientist to find out how to obtain a dataset. Often scientists are excited to share their data for educational purposes, but know that there can be time lags due to research and publication schedules.
Finding and collecting data can be time consuming which is why we provide an ever growing number of curated datasets from these sources for you to use more easily! If there are data or issues that we don’t yet have a dataset for, let us know and we will work to find it for you.
- Achieve, Inc. "Using Phenomena in NGSS-Designed Lessons and Units." 2016. Retrieved from: https://www.nextgenscience.org/sites/default/files/Using%20Phenomena%20in%20NGSS.pdf
- NGSS Lead States. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press, 2013.
- National Research Council. A Framework for K–12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. Washington, DC: The National Academies Press, 2012. https://doi.org/10.17226/13165