Welcome to our video about Streams of Data. This project will develop and test an approach that demonstrates how fourth grade students, supported by scaffolded learning experiences, can begin developing the analytical thinking skills necessary to make meaning from complex, large-scale, professionally collected data. The work will focus on analytical thinking as a subset of data literacy, specifically understanding the nature of data, asking and answering questions using data, and communicating with and about data.

We've been challenged to keep the project on track during the COVID-19 pandemic. Our video conveys some of our reflections on those challenges and how, with support from our partnering school systems, we've been able to keep the research going.

We'd like to hear about how others have navigated similar challenges with school-based research. We'd also like to hear from others doing work with K-5 students in data literacy and associated skills, especially with professionally-collected real-world data.

Thanks for watching! 

I so appreciate your description of that sense of how emergent the reality was for trying to do research in classrooms ... that sense of "maybe in another month." And I love the solution of the Awesome Aquifer Kit! I'm also impressed with how you used video in order to still see what kids were doing and to conduct interviews. Wonderful creative thinking! I'm curious what you learned in the last year, or what methods you turned to, regarding the conduct of classroom-based research that you might retain going forward?

Thanks for sharing this awesome project. I know that the focus of this video was more on the adjustments that your team had to make to your data collection process because of the pandemic, and there are a lot of lessons to be learned in that alone. However, I am interested in hearing more about how you decide which data sets to use as you develop your protocols. Are there certain types of data that seem to be more easily adaptable to the type of connections you want students to make?

I love how you were able to adapt to the world being turned on its head! My project ended up being more an "accidental" solution to lockdown. 

I'd love to hear more about what tools you used to pull off this transition.

Hi Dionne!

Thank you for your question. The process of developing curricula using authentic data is as much an art as it is a science. In this case, we began with a set analytical thinking outcomes that we hoped to support through the scaffolded learning experiences we were testing. These include:

Understanding the nature of data and their limitations:

• Connecting data to real-world events or objects, where data representations are abstractions of those events or objects;
• Considering data sources and limitations;
• Shifting from viewing data as case values to data as an aggregate;

Asking and answering questions using data:

• Mathematizing real-world problems and situations;
• Making inferences and articulating levels of certainty;

Communicating with and about data:

• Constructing narratives to explain patterns observed in data sets;
• Engaging in argument with evidence from data.

We knew that we were interested in using data relating to rainfall and stream flow, in part because these are phenomena which seemed relatable to students at this age (grades 3-5). But the process of developing specific storylines and identifying datasets was iterative and organic. In the first phase of the project, we tested several different representations of these types of data including maps, histograms, and dot plots, to get a feel for the way students derived meaning from each of them.

For the second phase, which we are in now, we have experimented with a variety of different possible narratives, and evaluated each of them according to their potential to address the analytical thinking outcomes listed above. Once we settled on a topic that seemed to support our analytical thinking goals (periodic flooding of the Embarrass River Bridge in Sainte Marie, Illinois), we had to identify the specific datasets we would use, as well as the most effective ways to present them to the students. This has been an ongoing process, and as we test them with students, we continually modify and refine our approach based on the things we learn.

In general, we try to have the students work with datasets that are large enough and complex enough so there is not a single “correct” interpretation, or a definitive “right” or “wrong” answer. Instead, we try to support students in finding patterns or relationships within the data, making deductions from those observations, and using their findings as evidence to support their reasoning. This isn’t always easy, and we are hoping that this project will provide some insights about what kinds of facilitation practices work best for students at this age!

Data literacy is an essential skill for decision making in careers and everyday life. It is exciting to see an emphasis on this type of critical thinking at the elementary level.  Thanks to everyone at Oceans of Data for this important work.

Hi Dionne,

In response to your question about datasets, Randy covered a lot of the curriculum design side well. I'll add that the project was really designed to explore ways that professionally collected can be used with young students. There is a lot of professionally collected data available online. We focused on geoscience data because both partnering organizations--EDC and AGI--have an interest there, which led us to the focus on hydrology. For example, the USGS website "USGS Current Water Data for the Nation" allowed us to navigate to hydrographs for rivers near our partner schools. We modified them a bit, primarily to take out perceptual noise, and shared them with students. We also downloaded precipitation data and constructed our own graphs from those data. Those are just a couple of examples. In making our adjustments, we tried to work within a framework of the way data are represented at the grade levels we are working, but with real-world data. It was interesting that we were even asked, "Are those real data?" as we interviewed children. It is no surprise that they are interested in working with data and information that relates to their own communities.  

Fascinating work! Thank you for describing the ways you adapted once the pandemic hit and how you continued to engage learners remotely. I am curious to learn more about the role of the teachers in your program. In what ways did you collaborate with teachers and did you collect any data from them?

Comments: As mentioned in the comments above, this work is fascinating! I'm also very much uplifted by your presentations of research as messy in that it takes place in authentic contexts that aren't "sterile" sort to speak. And yet that hasn't hindered your ability to do something great that has positive impacts on students. Congrats!

Questions: While I'm eager to read more about your findings in the future, I resonate with Sabrina De Los Santos' comments and questions, and I'm also curious about the same.

Hi Sabrina and Remy,

The question of teacher involvement is important to us. We recognize that we need to make use of both the formal knowledge base of teaching and the teachers' knowledge of their students. At first we intended to work directly with the teachers at the different phases of the research, working in partnership as we developed activities/protocols, and providing results of those phases both for their input and professional development. COVID changed that. We weren't able to meet with the teachers as intended and, especially at the beginning of the pandemic, there was not much appetite among the teachers for extra time online--the demands on them were already pretty intense. I'll skip over some of the "this led to that" scenarios and say that just as we found ways to interact with the students, we also found ways to interact with teachers and get their input about our protocols. This was facilitated by the fact that to move our work with the children to virtual modes, our protocols ended up being built on Google Slides that were screen-shared with the children through whatever platform their school system was using. Using Google Slides allowed us to share the slides with teachers before using them with children--Science, Mathematics, and English/Language Arts--and get their input. They offered great insights on a range of things, from types of data representations the students might be familiar with (or not) to key terms and pedagogical nuances (e.g., "notice" has a specific meaning among some of the teachers and students--as in the sense of open-ended observations--"What do you notice about the graph?") While as we finalize the protocols we don't always stay "in-bounds" in terms of curriculum and instructional standards, the teachers' input allows us to be intentional about the times we're pushing the children outside of their likely academic experiences in order to explore what thinking they're capable of. As summer approaches, discussions of results sharing and professional development are re-emerging.

Great project and inspiringly frank sharing of challenges faced by circumstances and passion and effort to address. 

I love the student-centered approach your model takes to teaching data literacy. I work with the Passion-Driven Statistics model where we take the same approach. We believe that students from diverse educational, social and economic backgrounds deserve not only a seat in a classroom, but also a welcoming place at the table. Our goal is to increase the number and diversity of students exposed to meaningful and empowering data analysis experiences and to inspire the pursuit of advanced data-driven experiences and opportunities for everyone! 

We have used this in higher education and with high school students in their course and as a summer boot camp. We have an upcoming workshop with middle school STEM teachers this summer that will implement next year in the classroom. We are excited to see our model being used by middle school students to complete project-based research using real data, asking original questions, and communicating methods and results using the language of statistics.

I see in the video the students are exposed to data, do they work with a software program to analyze their data?

Hi Kristen. I'm impressed with the description of your work and its motivations. I'll take a look at your video to learn more. 

As for the software for use in analysis, that is not part of our design. It would be interesting to consider what grade 4 students might be able to do in that regard. Do you have experience at that level? I'll bring this up with the math teachers when I talk to them next.

Best wishes,

Ed