Okhee Lee is a professor of childhood education in NYU Steinhardt’s Department of Teaching and Learning. She leads a collaborative research project between NYU and Stanford University to develop instructional materials aligned with the Next Generation Science Standards (NGSS) that promotes science learning and language learning of elementary students including English learners (ELs). She is also leading collaborative research with MIT and Vanderbilt University to integrate computational thinking and modeling in NGSS-aligned instructional materials. We spoke to her how children, including ELs learn science and use computational thinking.
Here’s a big question: How do children learn science?
Our understanding of what counts as science and how children learn science has evolved over the years. Traditional views focus on science as a discrete body of knowledge and on individual learners’ mastery of scientific facts. In contrast, contemporary views emphasize that children make sense of phenomena and design solutions to problems as scientists and engineers do in their work. Also, we have a better understanding that children are capable of engaging in science and engineering practices in collaboration with peers and under the guidance of a teacher. Students ask questions, develop and use models, plan and carry out investigations, analyze and interpret data, use mathematics and computational thinking, construct explanations, argue from evidence, and evaluate and communicate information. By engaging in these practices, students come to understand core ideas in science disciplines as well as concepts that cut across science disciplines, such as patterns and systems. Because this approach to science learning involves using and applying knowledge for a particular purpose, it has been referred to as knowledge-in-use.
In contemporary views of science learning, explaining phenomena or designing solutions to problems gives a purpose to “doing” science and engineering, which positions students as agents of their own learning. In our work with fifth grade English learners (Els), we use local phenomena rooted in everyday experience and everyday language in students’ homes and communities. Selecting local phenomena is especially important for students who have not experienced science as real or relevant to their lives or families and do not view science or engineering to have any bearing on their careers. For these students, selection of phenomena could serve either to provide access to science by relating science to their lives or to exacerbate marginalization by alienating them further from science. Thus, local phenomena promote ELs’ access to science and inclusion in the science classroom. In one of our instructional units, fifth grade students enter the classroom and see in the center of the room a mound of garbage collected from their school cafeteria including their own lunch garbage. The everyday phenomenon of garbage becomes phenomenal. For several weeks, the students learn about structure and properties of matter by figuring out what happens to their garbage.
How do English learners learn science?
Our understanding of what is language and how children learn language has been evolving. Traditional views focus on individual learners’ acquisition of vocabulary (lexicon) and grammar (syntax). In contrast, contemporary views emphasize that language learning occurs not as a precursor but as a product of using language in social interaction. Engaging in science and engineering practices inherently involves using language for purposeful communication. For instance, students use language when they develop models, construct explanations, and argue from evidence. As students use language to “do” science, they learn science and language simultaneously. Because this approach to language learning involves using language for a particular purpose, it has been referred to as language-in-use.
In recent years, we have witnessed parallel shifts in science learning and language learning that are mutually supportive. In science classrooms that emphasize collaboration and community, ELs participate meaningfully using less-than-perfect English. While investigating a phenomenon relevant to their lives, ELs draw on cultural and linguistic resources from their homes and communities. As they “do” science, they use everyday language to communicate their ideas. As their science understanding becomes more sophisticated, they use more specialized language. They also use multiple modalities, including drawings, diagrams, tables, graphs, charts, and text. In fact, in learning science, visual representations are as important as textual forms.
What is the role of technology in learning science?
Technology is defined in different ways. One way I am currently thinking about technology is computational thinking or “an approach to solving problems, designing systems, and understanding behavior that draws on concepts fundamental to computing” (Wing, 2006). While there is growing agreement on the importance of computational thinking, there is a lack of consensus on what constitutes computational thinking. In addition, the education system is not prepared to respond to the emerging force of computational thinking, especially when it comes to meeting the learning needs of all students, including ELs. As computational thinking has become part of STEM, all students should have access to equitable learning opportunities starting from the early grades. ELs could benefit from instruction in computational thinking, but they tend to be excluded from such educational initiatives and innovations, thus perpetuating and perhaps even widening the opportunity-to-learn gap. At present, little is known about the affordances and challenges that computational thinking may present to ELs. Also, instructional materials to enable teachers of STEM subjects to support ELs in computational thinking are virtually non-existent.
Our work is situated at the intersection of new ways of learning science, new ways of learning language, and new ways of using technology with all students, including ELs, in fifth grade. Our research team members come from multiple disciplines, including Lorena Llosa, associate professor, and Alison Haas, Scott Grapin, and Marcelle Goggins, who are graduate students in the Department of Teaching and Learning. We also work with colleagues across institutions, including Stanford University, MIT, and Vanderbilt University. I am excited to chart this new line of research into the future.