Abstract:

While the U.S. has engaged in significant efforts to grow computer science (CS) education in K-12, state-based decision-making has made approaches to computer science vary widely from stand-alone CS courses to the integration of computational thinking into disciplinary learning. Ongoing attention on equity points CS program developers towards compulsory education as a means to reach all students and a home for the development of foundational competencies.

This session shares progress of Science+C (Computing), a National Science Foundation project funded to create and institutionalize Computational Biology, Computational Chemistry and Computational Physics courses for schools to adopt as replacements for traditional science offerings. Science+C courses connect students’ experiences in school with the skills and knowledge used in the contemporary scientific enterprise; help students explore scientific phenomena as they develop skills to use, decode, and modify computer models and analyze data; and better understand both scientific processes and how computational methods and tools have changed the nature of scientific inquiry.

Rather than asking students to learn to build models as would occur in computer science classes, the 10 curriculum units focus on having students work with pre-built models that explicitly connect between disciplinary concepts and processes and code. The learner’s task becomes understanding and assessing models made by others. This new conception of decoding is a multi-step process that explicitly connects core disciplinary ideas with their instantiation in code. Students inspect code in models and match it to disciplinary concepts. They read code for understanding of process and cause/effect relationships within the models they are exploring. Decoding becomes the foundation for assessing the validity of models, and for making modifications to models (to correct errors, increase validity, or answer new questions).

This session highlights curriculum examples and shares lessons learned from the ten modules per courses piloted. The ten modules include 6 focusing on modeling and simulation, and 4 focusing on artificial intelligence through data. A quasi-experimental research study is evaluating the impact of the intervention on student computational thinking and science outcomes, and changes in teacher skills and capacities. Results will inform how we prepare students to use computational approaches to solve real-world problems in science and other fields, and through scientific exploration made possible by computational approaches, how Science+C improves or enhances student understanding of science.

Keywords:

Computer Science, Computational Science, Computational Thinking, CT-integrated STEM, Modeling and Simulation, Computer Science, Workforce of the Future.