Computing in Precollege Science, Engineering, and Mathematics Education

Abstract

Computing is essential to disciplinary practices and discourses of science, engineering, and mathematics. In each of these broad disciplinary areas, technology creates new ways of making sense of the world and designing solutions to problems. Computation and computational thinking are synergistic with ways of knowing in mathematics and in science, a relationship known as reflexivity, first proposed by Harel and Papert. In precollege educational contexts (e.g., K-12 schooling), learners’ production of computational artifacts is deeply complementary to learning and participating in science, mathematics, and engineering, rather than an isolated set of competencies. In K-12 contexts of teaching and learning, students’ data practices, scientific modeling, and modeling with mathematics are primary forms through which computing mediates the epistemic work of science, mathematics, and engineering. Related literature in this area has contributed to scholarship concerning students’ development of computational literacies––the multiple literacies involved in the use and creation of computational tools and computer languages to support participation in particular communities. Computational thinking is a term used to describe analytic approaches to posing problems and solving them that are based on principles and practices in computer science. Computational thinking is frequently discussed as a key target for learning. However, reflexivity refocuses computational thinking on the synergistic nature between learning computing and the epistemic (knowledge-making) work of STEM disciplines. This refocusing is useful for building an understanding of computing in relation to how students generate and work with data in STEM disciplines and how they participate in scientific modeling and modeling in mathematics, and contributes to generative computational abstractions for learning and teaching in STEM domains. A heterogeneous vision of computational literacies within STEM education is essential for the advancement of a more just and more equitable STEM education for all students. Generative computational abstractions must engage learners’ personal and phenomenological recontextualizations of the problems that they are making sense of. A democratic vision of computing in STEM education also entails that teacher education must advance a more heterogeneous vision of computing for knowledge-making aims. Teachers’ ability to facilitate authentic learning experiences in which computing is positioned as reflexive, humane, and used authentically in service of learning goals in STEM domains is of central importance to learners’ understanding of the relationship of computing with STEM fields.