To be proficient in science, students need to know, use, and interpret scientific explanations of the natural world. They must understand interrelations among central scientific concepts and use them to build and critique scientific arguments. This strand includes the things that are usually categorized as content, but it focuses on concepts and the links between them rather than on discrete facts. It also includes the ability to use this knowledge.
For example, rather than memorizing a definition of natural selection, a child who demonstrates proficiency with scientific explanations would be able to apply the concept in novel scenarios. Upon first encountering a species, the child could hypothesize about how naturally occurring variation led to the organism’s suitability to its environment.
Part of this strand involves learning the facts, concepts, principles, laws, theories, and models of science. As the National Science Education Standards state: “Understanding science requires that an individual integrate a complex structure of many types of knowledge, including the ideas of science, relationship between ideas, reasons for these relationships, ways to use the ideas to explain and predict other natural phenomena, and ways to apply them to many events.”2
Evidence is at the heart of scientific practice. Proficiency in science entails generating and evaluating evidence as part of building and refining models and explanations of the natural world. This strand includes things that might typically be thought of as “process,” but it shifts the notion to emphasize the theory and model-building aspects of science.
Strand 2 encompasses the knowledge and skills needed to build and refine models and explanations, design and analyze investigations, and construct and defend arguments with evidence. For example, this strand includes recognizing when there is insufficient evidence to draw a conclusion and determining what kind of additional data are needed.
This strand also involves mastering the conceptual, mathematical, physical, and computational tools that need to be applied in constructing and evaluating knowledge claims. Thus, it includes a wide range of practices involved in designing and carrying out a scientific investigation. These include asking questions, deciding what to measure, developing measures, collecting data from the measures,