To address challenges such as climate change, pollution, and health problems locally and globally, we need justice-centered solutions. It’s key to emphasize justice because many of these challenges disproportionately affect minoritized groups. We believe one underemphasized lever for promoting justice-centered solutions is K–12 STEM education. Just as the STEM disciplines—science, technology, engineering, and math—play a central role in creating solutions to pressing societal challenges, STEM education must play a key role in guiding students to explore such challenges and design solutions.
In our view (as researchers studying ways to make STEM more relevant and within reach for multilingual learners), exploring how societal challenges harm vulnerable groups gives educators an opportunity to strengthen students’ knowledge and cultivate the assets students bring to STEM learning. Imagine how it might transform STEM education if, while learning computer science or engineering, students saw clearly how that learning could help them work toward a more just society.
We’ve developed a conceptual framework to be used for what we call justice-centered STEM education with all students, especially multilingual learners (Lee & Campbell, 2020). The framework brings together recent advances in STEM disciplines to address the COVID-19 pandemic and emerging research in STEM education that centers justice. Based on the framework, we have developed prototype lessons addressing COVID-19. Currently, the two of us are engaged in a multiyear research and development project with funding from the National Science Foundation (NSF 2300118). We hope that by unpacking the framework and showing how one middle school teacher used it to teach our lessons on COVID-19, we can illustrate the potential of justice-centered STEM education. We (along with our colleagues) especially see potential to draw in students who have often faced barriers to pursuing STEM learning by giving them knowledge and self-confidence in STEM. We hope this article can serve as a call to action for STEM educators—those teaching any of the STEM-related subjects (science, engineering, mathematics, data science, computer science, integrated STEM programs, etc.) across diverse instructional settings—to work toward centering justice.
What Is Justice-Centered STEM Education?
Our framework focuses on STEM education broadly because trying to solve societal challenges requires drawing on knowledge and practices from all STEM subjects (including data science and computer science, key areas K–12 students need to learn about). This focus on justice-centered solutions is a fundamental shift from the approach of engaging students with STEM in ways that center the knowledge of STEM disciplines without discussing how that knowledge relates to societal problems. Solutions that focus narrowly on “following the science” are necessary but not sufficient to meet challenges like climate change.
Justice-centered STEM education could be especially powerful for K–12 students from minoritized groups, who haven’t traditionally seen their lives, families, or communities reflected in STEM subjects. As students explore and propose solutions for societal challenges that disproportionately affect minoritized groups, it’s important that both teachers and students center the experiences, knowledge, and voices of these groups. One such group is the fast-growing population of students who speak a language other than English at home. More than 1 in 10 students are classified as “English learners” by their schools, and 1 in 5 students report speaking a language other than English at home (National Center for Education Statistics, 2023). We refer to these students, collectively, as multilingual learners (MLs) to emphasize the assets they bring rather than their perceived deficit in English.
Justice-centered STEM education is grounded in an asset-based view of all students, especially MLs (Lee, 2021). It aims to transform STEM education by expanding beyond teaching canonical knowledge of STEM and beyond privileged ways of expressing that knowledge, so that all students can draw on an array of resources for making meaning and demonstrating their understanding. Justice-centered STEM education strives for full, equitable participation of all students, which means engaging in STEM education and using STEM learning to positively impact society.
The complexity of a challenge like COVID-19 gives teachers an opportunity to let students try out the type of work STEM professionals often do.
Four Principles in Our Framework
The framework we developed incorporates four interrelated principles for the kind of actions teachers could take to empower students to address societal challenges by leveraging knowledge and practices in STEM subjects.
1. Guide students to ask questions about societal challenges that disproportionately affect minoritized groups (Lee & Grapin, 2022). This is a fundamental shift from anchoring science learning in “sanitized” phenomena and problems that are disconnected from societal challenges (e.g., “What happens to our garbage?”) to grounding learning in real-life problems (e.g., “How are landfills affecting communities?”). Teachers who honor this principle highlight how challenges grounded in local contexts connect to students’ knowledge about their homes and communities, and how students’ personal experiences can provide insights. This is powerful with students whose communities often face justice-related challenges—true of many MLs.
2. Guide students to explain societal challenges by integrating their everyday experiences and knowledge with key disciplinary practices in data science. Key practices here are collecting, analyzing, and interpreting data; looking for patterns in data; and constructing explanations for the patterns. Students also consider the personal, cultural, and sociopolitical layers of how data is produced and used (Lee, Wilkerson, & Lanouette, 2021), including whose interests are served by the data that gets collected or doesn’t.
When a group of students doing this kind of data interpretation includes MLs, teachers can highlight such students’ transnational experiences and knowledge as assets. Although the majority of MLs were born in the United States (National Center for Education Statistics, 2023), many continue to maintain ties to two or more countries through travel, virtual communication, or cultural practices. Because of these connections, they can often bring insights to an exploration of particular data and what that data reflects. For instance, a student may share about initiatives to reduce plastic pollution in their country of origin.
3. Give students ways to explain societal challenges by engaging in computational modeling. Computational modeling is one application of disciplinary practices in computer science. Computational modeling synergizes computational concepts and tools with the practice of developing models. For example, students can develop computational models to explain a societal challenge, such as plastic pollution and its impacts. They use the model to program interactions of individual parts in a system (such as people using plastic in a community) and then observe the resulting behavior of the system as a whole (how plastic pollution accumulates in the community).
When MLs do this kind of computational modeling, teachers can highlight the various resources MLs often use for making meaning as assets (National Academies of Sciences, Engineering, and Medicine, 2018). As MLs interact with computational models, they construct and communicate meaning by using different modalities (e.g., computer codes or graphs), registers (e.g., programming register), and languages (e.g., English and Spanish) (Grapin et al., 2022). By pointing out how computational modeling involves knowing and being flexible with various communication systems, teachers can help MLs gain a more positive view of their ways of making meaning, including their ability to speak several languages. Some students might begin to see language flexibility as an asset in STEM-connected work.
4. Help students design solutions to societal challenges by engaging in engineering to address underlying causes for inequities. By inequities, we mean disproportionate effects that societal challenges have on marginalized groups. Students from a group or community marginalized in some way would engineer and put into action solutions that recognize cultural practices of that community (i.e., cultural practices that would make a solution more likely to succeed).
Our Framework in Action: The Case of the COVID-19 Pandemic
The resources our team has developed (see “Related Resources” at the end of this article) to help K–12 teachers carry out justice-centered STEM education embody these four principles. These resources include the series of lessons and curated data sets we created in connection with the National Science Teaching Association. As one of the most pressing societal challenges of our century, the COVID-19 pandemic directly affected the lives of all K–12 students. While the worst of the pandemic is over, the complexity of that challenge gives teachers an opportunity to let students try out the type of work STEM professionals do to end a pandemic and weave justice into solutions that students design. Throughout COVID-19, STEM professionals used data science and computer science to understand the virus and slow its spread and disastrous effects. They analyzed local and global data, explained patterns about the spread of COVID-19 in the data, and developed and tested computational models that helped policymakers design solutions to slow the spread.
Justice-centered STEM education is grounded in an asset-based view of all students, especially multilingual learners.
In spring 2022, we collaborated with a 6th grade science teacher in the New York City Public Schools to pilot these resources with her three classes (Grapin, Dudek, & Lee, 2023). Our work with this teacher (who we’ll call Ms. Jamison) and her students reflected our four principles. Regarding our first principle, students answered the driving question in our lessons, “How can we make informed decisions to keep ourselves and our communities safe during the COVID-19 pandemic?” Some multilingual learners in the classes related cultural practices that can be characteristic of their culture to issues that affect different communities disproportionately (e.g., how multigenerational living connects to the larger impacts of COVID-19 on Latinx/Hispanic communities). In addition, they shared their knowledge of the countries where they or their families were from to explain why COVID-19 outcomes varied among countries (such as countries having differences in access to healthcare).
For the second principle (using data science and integrating what one learns from patterns in data with personal experiences and knowledge), students examined COVID-19 cases and deaths globally, by nation, and by local community using data from the Johns Hopkins University Coronavirus Resource Center and the Global Change Data Lab in collaboration with the University of Oxford. They also analyzed CDC data by racial and ethnic group. Through such data, students could clearly see the injustices the pandemic exposed, with cases and deaths disproportionately affecting minoritized groups.
For the third principle, students developed computational models using StarLogo Nova, a computer programming environment. These models helped the students explain the patterns of COVID-19 spread and understand underlying causes for the overrepresentation of racial and ethnic minority groups in COVID-19 cases, such as the fact that minority groups are disproportionately represented among frontline workers and among people living in crowded conditions—both of which made it harder for people to follow CDC guidelines.
For the final principle, students used their computational models to design and iteratively test systemic solutions, such as improving transit systems for frontline workers and increasing access to affordable housing. For students who were MLs, leveraging their rich repertoire of meaning-making resources enabled them to communicate their explanations and solutions. For example, one group of students developed a presentation for the local city council to argue for increasing protections and pay for frontline workers in their local community. In the presentation, the students, many of whom were multilingual learners, used a strategic combination of graphs, code blocks, and written language. They used multiple languages, including English and Spanish, which helped them reach communities served by their designs.
STEM Learning for Life
This group of students’ work is only one example of how justice-centered STEM education could advance learning and get students enthused about STEM. We believe teachers can use our framework and open resources to engage students in tackling many challenges that disproportionately affect minoritized groups, such as climate change and affordable housing. Through this kind of justice-centered STEM education, we believe all students, particularly those from groups that often are left out of STEM learning, can have the tools to better understand and solve societal problems. They can use STEM learning to make informed decisions, take responsible actions, and design solutions that work for local and global communities who grapple with formidable challenges.
Related Resources
The open education resources that we and our colleagues have developed are available to any educator here:
Tracking COVID-19 in the United States (https://www.nsta.org/playlist/tracking-covid-19-united-states)
Understanding COVID-19 Disparities Using Computational Modeling (https://www.nsta.org/playlist/understanding-covid-19-disparities-using-computational-modeling)
The open education resources that we and our colleagues have developed are available to any educator here:
Tracking COVID-19 in the United States (https://www.nsta.org/playlist/tracking-covid-19-united-states)
Understanding COVID-19 Disparities Using Computational Modeling (https://www.nsta.org/playlist/understanding-covid-19-disparities-using-computational-modeling)
