How can we help all students navigate our data-rich world?
By Leticia Perez and Karen Lionberger | May 3, 2023
It’s a strong possibility that by the time this blog comes across your screen today, you will have received multiple alerts based on a collection of personal data about you. Maybe it was your watch or phone telling you how much screen time you had this week, or how many steps you have taken towards your daily goal, or maybe a targeted advertisement that popped up on your social media. Sound familiar?
We live in a world that is fundamentally engulfed in an expansive ocean of available data. Yet, it can still feel surprising to be the recipient of this type of data analysis.
There were 5 exabytes (one billion gigabytes) of information created between the dawn of civilization through 2003, but that much information is now created every two days.
– Eric Schmidt (Executive Chair, Google)
Data can provide insights, reveal hidden patterns, and shape the way we see the world and approach problem solving. Some argue that big data analytics may lead to furthering current social inequities and bias, while others contend that purposeful, social justice-oriented use of data science can actually help solve those very same issues. Regardless of what side of this debate you land on, one thing is clear — if we want students to navigate the world around them more effectively, their need to develop competencies and confidence with data has never been greater.
The need for building confidence with interpreting and using data reaches beyond simply meeting our instructional goals for math or science students. Becoming more fluent with data allows all youth opportunities to become more discerning community members. This translates into becoming adults who have confidence in their ability to interpret and use data in a myriad of important ways in their day-to-day lives — asking more effective questions, making more informed decisions, and more effectively communicating essential findings and trends to others. These types of skills are important to all of us, not just those who are seeking STEM degrees and careers. If we truly do want to work towards a world where data science is used as a social justice-oriented pursuit, then both data science and fluency must focus on equitable opportunities for participation from Black, Latino/Latina, and lower income students — those who have historically been excluded from this type of learning.
Data science’s emergence as a distinct field has prompted educators across the spectrum to tackle questions such as: What do community members need to make sense of a world awash in data? How and when do students develop these skills? Should we integrate data science into other subjects across grades K–12 or should we treat it as a stand-alone content area? We have learned from those involved in the recent pushes for increasing computer science fluency, like CSforALL, that mapping a pathway forward for this type of change is a complex task. First and foremost, we must answer the fundamental question of what is data fluency and in what competencies do students need to develop confidence with using data?
In this blog post we are sharing work from the first stage of our NSF project, Boosting Data Science Teaching and Learning in STEM1. This project brings together WestEd’s proven Making Sense of SCIENCE (MSS) teacher professional learning model, along with the Concord Consortium’s Data Visualization tool, CODAP. We first propose some ways to define data fluency in the field of education and consider effective practices for engaging all students with data in the classroom.
How do we define data fluency?
Based on our review of the research, we define data fluency as the ability and confidence to actively make sense of and use data. It extends beyond possessing discrete knowledge and skills to knowing when, how, and why to use data to explore topics of interest and for a specific purpose. Learners develop this fluency by exploring data, getting data, asking questions, along with designing solutions, interpreting data, and communicating with data.
Data fluency is something that is developed over time and through authentic investigations with data across a large variety of contexts. Whether we are using a sensor to track our blood sugar, planning a trip around optimal weather conditions, or learning a new visualization tool like CODAP, as students or educators, we are all growing and learning how to use and incorporate data in new ways. That is to say, as a project, we don’t see data fluency as a destination for everyone to reach, but a lifelong journey that transforms across our contexts.
Through active engagement with data, we learn to:
Hold broad notions of what counts as data;
Understand how data are collected, produced, and used;
Use digital tools to create, modify, and interpret representations and visualizations of complex data; and
Ask critical questions about data that arise from personal, contextual, and historical experiences.
How do we support data fluency in the classroom?
Our definition of data fluency helps us to consider what learning with data looks like within our math and science classrooms. The Next Generation Science Standards (NGSS) Science and Engineering Practices and Common Core Standards of Mathematical Practices helped emphasize the need for students to have more opportunities to engage with data in meaningful ways. This should result in all students having experiences where they analyze and interpret data as well as collect, transform, create, and critique not only the data themselves but its implications. Ali Gubary, a life science high school teacher in the Central Valley of California, who is collaborating with us on this research, elaborates on the need to move away from lower-level, textbook-driven questions about narrow slices of textbook-provided data that require very little sense-making and critical thinking from students. Instead, she proposes that educators use more robust datasets that invite students to consider how the data were generated, pose authentic questions, and evaluate the adequacy of those data for those purposes:
The shift of NGSS needs to also apply to the data that we look at. We have to move away from the idea that, “This data has been given to you so you can answer these sets of questions” to “Here’s this data, tell me what information can you get from it. What information is still missing?” This allows them to really analyze the data so students can build critical thinking skills.
Through our review of the literature2 and prior MSS work, we developed the Entryways to Data model as a tool for educators which describes different effective pathways for incorporating data into learning. To further develop data fluency, learners need multiple opportunities to engage with data and a variety of approaches to make sense of and communicate about data. Importantly, we want educators to recognize that supporting data fluency can begin at any entryway. Data scientists use many different pathways when exploring data, and their pathway may not always be the same or follow a fixed sequence.
What are principles of data-rich instruction?
Ensuring that all students have access to data-rich instruction will require us as educators, decision-makers, and leaders to make strategic choices within our spheres of influence. To help us balance our choices, we have found it imperative to be explicit about our beliefs and principles that ensure all students have the opportunity to equitably pursue data fluency.
Data-Rich Instruction Principles
Data-rich learning is accessible to all learners.
Data-rich learning is learner-driven.
Data-rich learning uses complex datasets.
Data-rich learning encourages healthy skepticism.
Data-rich learning strengthens content knowledge.
Data-rich learning utilizes the power of digital tools.
Data-rich learning draws on learners’ personal, historical, and cultural contexts.
For the next stage of our project, we are using a co-design process with educators to develop our professional learning. These teachers are engaging their math and science students in data-rich experiences, documenting evidence of student sensemaking, and collecting instructional artifacts. This process helps ensure that teachers’ voices and experiences are centered in the design of our professional learning.
Leticia Perez is a Data Science and Professional Learning Specialist in the Science and Engineering Department at WestEd. As a former high school science teacher, her interests center around respectful and impactful science and data science professional learning. Her more recent work includes designing professional learning for K–12 educators that incorporates data science and computational thinking into classrooms.
Karen Lionberger is the Associate Director for Making Sense of SCIENCE in the Science and Engineering Department at WestEd. Her work focuses on developing equity-driven curriculum, professional learning, and assessments that promote deeper teacher and student engagement and self-confidence in STEM. The dedication to this work is rooted in her experiences as a high school science teacher.
1 This material is based upon work supported by the National Science Foundation under Grant No. 2101049. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.