By: Kristin Hunter-Thomson
Where did this come from?
The California, Oregon, Washington, and Nevada Science Teachers Associations published in October, 2017 a great report titled “Priority Features of NGSS-Aligned Instructional Materials: Recommendations for Publishers, Reviewers and Educators” (see announcement here). There is a host of great information included within this report, especially the paragraph about thinking about data in the 21st century and NGSS status of science education.
What are good ways to integrate data into NGSS science instruction?
Let’s look dive deeper into a couple of their suggestions:
- “provide students with multiple supported opportunities to work with raw data” – Working with raw data, rather than only processed/cleaned-up data helps students (1) understand that it takes a lot of data to understand a pattern, (2) data collection is not one-and-done, and (3) not all data has the same importance (aka just because you have collected it does not mean you will need it to communicate your findings). Supporting students to work with the raw data in ways that support their growth of skills to “identify and explain connections and support claims” from the data to their question/problem is a key data literacy skill that needs to be facilitated and fostered over time.
- “data should be collected first hand by students” – Absolutely! Collecting data yourself is the best way to really understand your data, what its limitations are, and what it could mean. Also, understanding how to collect data of high quality is an important data literacy skill. While I fully encourage and support learner-generated data, do not forgo the use of professionally-collected data as well…but we aware that it comes with its own sets of advantages and challenges (upcoming posts to follow).
- “analyzing data to make decisions about which data are useful and could be used for identification of patterns, relationships, trends and anomalies” – Understanding how to analyze the data as well as what data to analyze are both key aspects of data literacy for our students, especially given the ever increasing amount of data in our lives. Too often we make the decisions for our students about what data is useful to look at, given constraints of time and skills. But to prepare our students to be successful on their own working with data, we will need to scaffold the skill of making these choices.
- “mak[e] decisions about how to represent data (visualization tools such as tables, graphs, or diagrams)” – Putting students in the driver seat, sometimes, about which is the best way to look at their data teaches them an invaluable skill of how the visualization we use is tied to the question we are asking and the kind of data we have. As students are given the tools to make these choices (see Data Literacy Series: Create & Iterate Data Visualizations Resources) and the opportunities to practice making them, they will become more proficient in exploring their data and explaining their findings from the data.
- “routinely engag[e] in sense-making, collaborating, and revising their thinking when working with data” – Working with data is an iterative process in science, and one that involves a lot of trial and error, reprocessing, talking with colleagues, editing, and starting from scratch. The timeframe that scientists put in to working with data, is not feasible to incorporate into our K-12 classrooms every time that students work with data. However, if they are never provided the opportunity to make sense, collaborate, and revise their sense making then do not learn how vital this is in the process of science. [If you want to explore this more, check out “Helping Students Make Sense of the World Using Next Generation Science & Engineering Practices” by Schwarz, Passmore, & Reiser (2017).
Interested in thinking about this more?
Check out the “Turning Data into Evidence: Where Science Standards & Data Meet” article put out by Tuva last month (.pdf file here). For each SEP they talk through how data are used and integrated into the practice.