Focusing on Essential Knowledge and Skills

A coherent system recruits, prepares, develops, and retains strong teachers and leaders, leading to an effective teacher for every student and principal for every school. MORE

 

Read the papers that informed The Opportunity Equation report recommendations.

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Common standards, linked with rigorous assessments, set the bar for all students—from struggling to advanced—to master academically rigorous content and succeed in the global economy. MORE

 

Michele Cahill responds to probing questions about why stronger math and science education is crucial for all American students. MORE

 

Connecting to Your Work

How can you mobilize to help focus teaching and learning on essential math and science knowledge and skills? Read recommended actions from The Opportunity Equation report. MORE
 

Five Areas of Core Science Knowledge: What Do We Mean by ‘STEM-Capable?'

Jason Zimba
Bennington College
2009

Prepared for the Carnegie-IAS Commission on Mathematics and Science Education

In his advice to the Commission, physicist and educator Jason Zimba described five areas of core science knowledge that all students should have an opportunity to learn and 15 fundamental science practices that all students should have a chance to develop.

Core Science Knowledge: What All Students Should Learn

1. Where we are in the universe. The earth, moon, sun, and planets; asteroids and comets; the sun and nearby stars; the Milky Way galaxy and notable objects within it; the Local Group of galaxies; boundaries of the known universe. The round, revolving, spinning earth; seasons. Thickness of the atmosphere; mantle and core of the earth. Heights of mountains; depths of seas. Rivers, lakes, and oceans; erosion; precipitation and the water cycle; weather and climate.

2. How we came to be. The calendar and units of time. The age of the universe. The age of the earth, sun, and solar system. The future of the sun. The oldest known rocks; the oldest known fossils. What is still unknown about the origins of life on earth. Questions about the origins of life that are and are not answerable by science. The fossil record and the historical fact of evolution. Periodic mass extinctions. The Cambrian Explosion. The Kingdoms and taxa; Linnean classification; phylogenetic trees. The evolutionary path leading from the seas to Homo sapiens. Ecosystems and energy webs; the role of the sun; the interaction of life with its physical environment. Photosynthesis, respiration, and carbon cycles.

3. The organizing principles of contemporary science. The atomic picture of matter, the Periodic Table, and chemical reactions. Kinds of interactions: electrical, magnetic, gravitational, and nuclear forces. Energy conservation and transformation. Light, sound, and heat. Disorder, waste, and inefficiency. The organization of life: biomolecules, cells, tissues, organs, and organisms. Natural selection. Genes and heritability; DNA replication, transcription, and mutation. Universal behaviors of complex systems.

4. Human health and well-being. Nutrition. Sleep. Exercise. Major systems of the body (digestive, circulatory, nervous, immune). Infectious diseases; pathogens; what life was like before antibiotics; antibiotic resistance. Risk factors, relative risk, and risky behaviors and decisions. Chronic diseases and healthy lifestyle. Mental illness. Science of animal and human reproduction.  Sexually transmitted diseases. The needs of infants. Life planning. Growing old. Death.

5. What science and technology can do today. Nanotechnology, genetic engineering, medical therapies, weapons, buildings and structures, computing, transportation, communication, robotics, energy production and conservation, climate change mitigation.

Fundamental Science Practices: What Students Should Be Able to Do

1. Making and using mathematical models. Mathematizing a situation; solving the resulting math problem; interpreting the answer. At its simplest, doing ‘word problems’ and applying relevant formulas. Justifying the use of the formulas. Making simplifying assumptions in order to make progress on a hard problem; noting them as such; identifying the limitations of the model. Checking answers using order of magnitude estimation and dimensional analysis. Using substantial models that must be analyzed on a computer (e.g., using a spreadsheet to simulate the population dynamics of a predator-prey relationship; using stochastic models). Using mathematical and computer models to predict phenomena. Improving models to make better predictions.

Using network diagrams or other techniques to visualize complex situations with many factors, causes, or agents.

2. Connecting domains of knowledge. Seeing biological functions as chemical phenomena. Seeing chemical reactions as electrical phenomena. Seeing large issues through multiple lenses, such as viewing climate change through the lenses of chemistry, ecology, and policy. Relating phenomena across timescales, such as identifying evolutionary timescales with geological timescales and situating metabolic timescales within growth timescales.

3. Approaching complex problems. Using network diagrams or other techniques to visualize complex situations with many factors, causes, or agents. Organizing the factors/causes/agents into a hierarchy of importance; estimating orders of magnitude. Modeling the most important factors. The perturbative approach to problems; power and limitations. Separating out timescales and multiple timescales in a problem or phenomenon. Using notions of surface area to volume ratio and taking advantage of the smallness of boundaries.

4 Learning to look. Keeping lab notebooks, field notebooks, and observing journals. Making detailed observations. Sketching what one sees. Writing short, vivid descriptions. Being specific: e.g., saying “elm tree” instead of “tree.” Using optical instruments such as binoculars, telescopes, magnifying glasses, microscopes, and pinhole cameras. Reliably estimating distances, sizes of objects, and numbers of individuals. Building a vocabulary of vivid descriptive words—not only the colors of the rainbow and light or dark shades of these, but colors such as turquoise, buff, teal, magenta, rust, crimson, and salmon; modifiers such as iridescent, translucent, matte, glossy, and milky; textures such as pebbled, knurled, polished.

5. Designing and conducting experiments. Formulating testable hypotheses, designing controlled experiments that can reject them. Conducting studies of varied design (case/control, observational/correlational, longitudinal/cohort, randomized clinical trial, ethnographic/qualitative). Designing studies with an eye toward statistical power.  Making precise measurements using specialized instruments (thermometers, micrometers, light meters, etc.); identifying major and minor sources of systematic error; quantifying precision and accuracy. Building and refining apparatus to maximize results. Keeping dated notebooks and cataloguing them over multiple years. Collecting data on a computer. Inventorying computer files and documenting computer code.

6. Presenting data for a purpose. Designing data displays that make the desired implications clear. Using data displays to bolster an argument. Preparing data to support difficult decisions with serious consequences. Writing accurately about data.

7. Crafting, critiquing, and debating causal explanations. Explaining phenomena mechanistically and causally-such as why summer is warmer than winter, why the worms in the terrarium died, how soap gets things clean, why a ball tossed in the air comes back down, why the credit system collapsed in 2008 and so on. Mechanistic causal explanations need not reflect an expert’s understanding of the domain, its theories, or its content. Indeed, students should often formulate mechanistic causal explanations for unfamiliar and unexplained phenomena-just as practicing scientists and problem solvers must often do.

8. Thinking with your hands; thinking on your feet. Not sitting back in one’s chair, but standing at the lab bench with materials in hand, trying things out. Holding conversations at the board with chalk in hand, quantifying statements, and sketching objects and functions. Making order of magnitude estimates. Doing all this without being cued to do so.

Responding forcefully, precisely, and civilly to the criticisms of others; thinking carefully before doing so.

9. Writing up results. Describing an experiment or model clearly enough so that it can be replicated by others; reporting findings objectively; supporting one’s conclusions with summary statistics and effective data displays; anticipating the questions and objections of skeptical readers; explaining why the project was worth doing in the first place; relating the results to prior art and acknowledging the contributions of others to one’s work.

10. Criticizing, defending, and conceding. Giving precise criticisms of others’ scientific efforts, in a civil tone; thinking carefully before doing so. Responding forcefully, precisely, and civilly to the criticisms of others; thinking carefully before doing so. Writing retractions when experimental results or conclusions are found to be flawed. Endorsing the work of others when it is judged superior to one’s own.

11. Modifying beliefs based on new evidence. Beliefs based on faith (religious beliefs) contrasted with beliefs based on evidence (scientific*/secular beliefs). Rules governing scientific/secular beliefs. Deciding which of two or more hypotheses the evidence favors. Continually updating beliefs based on new information. Reasoning probabilistically. Living comfortably with uncertainty. Scientific truth, scientific consensus, and the nature of scientific progress.