Concord Consortium Blog

The basic concepts of evolutionary theory are contained in the National Science Education Standards (National Research Council, Washington, DC, 1966) as well as those of the various states. For example, in the table below we show the alignment between the "big ideas" of evolution and the science standards for three states: Massachusetts, Missouri, and Texas. The “learning progressions” in the second column are adapted from the Atlas of Scientific Literacy (American Association for the Advancement of Science, 2007), while the quotes in the third column are from the Massachusetts Science Framework; we also index in that column the corresponding standards from the Missouri “Show-Me” Standards and the Texas Essential Knowledge and Skills Standards.

Beginner Level

Big Idea	Learning Progression	MA Science Framework
Basic needs of organisms	Plants and animals need air and water; plants also need light and nutrients; animals also need food and shelter.	“Identify the ways in which an organism’s habitat provides for its basic needs.” See also MO Science K-4: VII.B.2, TEKS Grade 4:5.A&B
Organisms and their environment	For any particular environment, some kinds of organisms survive well, some less well, and some cannot survive at all.	“Identify the structures in plants and animals that enable them to survive in an environment.” See also MO Science K-4: VII.A.2, TEKS Grade 4:8.A
Interspecific differences	Plants and animals have different life cycles that include being born, developing into adults, reproducing, and dying.	“Classify plants/animals according the physical characteristics that they share.” See also MO Science K-4: VII.C.1
Basic needs of species	Groups of organisms can survive even though every individual in the group eventually dies.	“Give examples of how changes in the environment have caused some organisms to die.” See also TEKS Grade 4:8.B
Interactions between species	Organisms with similar needs compete with each one another for resources.	“Investigate how invasive species out-compete native ones.” See also MO Science K-4: VII.A.2, TEKS Grade 4:8.B
Intra-specific differences	Individuals of the same species may differ.	“Observe differences between organisms.” See also MO Science K-4: VII.E.2, TEKS Grade 4:8.A
Heritability of traits	Offspring are usually very much, but not exactly, like their parents.	“Differentiate between inherited and other characteristics.” See also MO Science K-4: VII.D.2, TEKS Grade 4:8.C

Intermediate Level

Big Idea	Learning Progression	State Learning Standards
Basic needs of species	For a species to survive, the individual organisms in it must reproduce fast enough to replace the ones that die out.	“Describe how organisms meet their needs by using behaviors in response to stimuli received from the environment.” See also MO Science 5-8: VII.C.1&2, TEKS Grade 5:5.A
Interactions between species	Every animal species depends on another species, plant or animal, for food.	“Give examples of how organisms can cause changes in their environment to ensure their survival.” See also MO Science 5-8: VII.E.2, TEKS Grade 5:5.B
Intra-specific differences	Differences between individuals in a species may give some an advantage in surviving and reproducing.	“Give examples of how inherited characteristics may change over time as adaptations to changes in the environment that enables organisms to survive.” See also MO Science 5-8: VII.E.1&3, TEKS Grade 5:10.B
Heritability of traits	Some traits of organisms are inherited from their parents; others are learned or acquired.	“Recognize that every organism requires a set of instructions that specifies its traits.” See also TEKS Grade 5:10.A

Advanced Level

Big Idea	Learning Progression	State Learning Standards
Heritability of traits	Heritable characteristics can affect the likelihood that an organism will survive and reproduce	“Relate the extinction of species to a mismatch of adaptation and the environment.” See also MO Science 5-8: VII.E.4, TEKS Grade 7.10.C
Genetics	Heritable traits are transmitted from parents to offspring via different forms of genes, called alleles.	“Recognize that hereditary information is contained in genes located in the chromosomes of each cell.” See also TEKS Grade 7.10.C
Survival of fittest individuals in an ecosystem	Offspring of advantaged individuals are more likely than others to survive and reproduce, increasing the proportion of organisms that have advantageous traits.	“Recognize that biological evolution accounts for the diversity of species developed through gradual processes over many generations.” MO Science 5-8: VII.E.4, TEKS Grade 7.10.B

The word "model" means a lot of different things to different people. A model airplane looks like a real airplane, only smaller; a paper airplane flies like a real airplane, only not as far or as fast. Both are models, neither is the kind of model I have in mind.

For the purposes of this discussion I'm defining a model as a description of a phenomenon in terms of things that can't be seen, felt, or heard, but that explain what's going on. Models may involve things that are too small to be seen, or too big; processes that take place too slowly or too fast. The plate tectonics model, for instance, informs us that the Himalayas are being formed, even as we speak, by the earth crumpling like a car fender, as India crashes (rather slowly, to be sure) into Asia.

Science is all about models of this kind, and an important goal of science education -- and of the Concord Consortium -- is to give students some examples of models and show them how to use those models to make predictions, to guide experimentation, and generally to make sense out of their own and other people's observations and experiments. Scientific models are constantly subject to revision as new experiments are performed, new data collected, and new interpretations advanced to explain existing data. It is important, therefore, that we teach our students about this process as well, giving them the sense that science is perpetually a work in progress, rather than a set of unchanging "facts."

Take, for example, the theory of evolution, a model that systematizes the description of an enormous body of data in terms of three simple propositions:

1. that organisms inherit many of their physical traits from their parents,
2. that differences in those traits can result in differences in organisms' ability to survive and procreate, and
3. that new traits can arise through random variation.

Note that the model does not depend at all on understanding the processes by which these things happen. In fact, when Darwin proposed his model for evolution in 1859, only the first two could be demonstrated at all, and only the second could be explained. Mendel's model of genetic inheritance  would not be published for another seven years (only to be ignored and rediscovered in 1900) and the discovery of mutations, which give rise to the random variation required by proposition three, was half a century away.

Genetics is only one example - evolution depends critically on models drawn from many other sciences. Geological models of the age of fossil-bearing rocks, critical to giving evolution theorists enough time for the hypothesized processes to take place, bear directly on the feasibility of the evolution model. So do models of mutation rates in different organisms, which themselves depend on models of the environment in which those organisms lived a long time ago. And the burgeoning new science of genomics, by comparing DNA sequences across present-day species, gives us the ability to trace the evolution of those species with a precision that Darwin could never have imagined.

All this interdependency makes evolution a particularly fragile model, as its opponents often point out. The discovery of a single errant fossil^,* like the human footprint supposedly found amid dinosaur tracks, could in principle bring the entire elaborate edifice crashing down. But its very fragility confers on the evolution model its extraordinary power. Evolution is fragile because it is so broadly applicable. In providing a model for how the multitude of species arose on this planet, it feeds into and constrains dozens of other models. It is powerful, in other words, for precisely the same reason that it is fragile.

 

^*For an in-depth discussion of this issue try http://paleo.cc/paluxy.htm.

ATE (Advanced Technological Education, one of the NSF education programs) is different!

For one thing, their audience is different: two-year community colleges and secondary “vocational” schools. A far cry from the Caltechs and MITs that NSF normally hobnobs with.

Still, as I found at my their meeting last week, ATE is far from “NSF Lite.” For one thing, in contrast to the rest of the science ed programs, ATE’s goal is not to prepare their students for the next run of the academic ladder – for the most part, the graduates from ATE-funded programs, go straight into the high tech job market, where they will work as technicians, lab assistants, or network administrators. This means that the program is driven, for better or worse, by external market forces, and is correspondingly insulated from some of the kookier pendulum swings of educational policy.

This leads, among other things, to a refreshing concern with bringing course materials up to date. While the “academic” educators tweak a curriculum that considers Mendel’s Laws (1866) synonymous with genetics, and relegates Relativity (1905) to the chapter on “modern” physics that never gets covered, ATE is busy funding initiatives on hybrid cars, nanotechnology, renewable energy sources, and biotechnology!

Our CAPA project, of course, is about none of those things – we are the only project in the portfolio, in fact, that deals with assessment, rather than content. But there again, I’m finding the ATE community ahead of the curve. Performance assessment (inferring kids’ understanding by their manipulations of models, rather than their answers to questions) has been something of a hard sell to the more academic programs of NSF (to say nothing of the Department of Education!) In contrast, the community colleges and technical high schools recognize that not everyone “tests well” on multiple-choice items – their clientele, in fact, comprises a disproportionate number of intuitive problem-solvers who are “good with their hands” but score poorly on tasks requiring abstractions and the extensive use of language. I talked to a lot of people at the meeting, and when I explained why I was there everyone “got it.” I collected a lot of business cards.

It was a peculiar feeling to attend a PI meeting where the only familiar faces, aside from NSF folks, belonged to my co-PI John Chamberlain of CORD, and Bob Tinker and Amy Pallant, who were there representing the Molit project (you should have seen Amy’s hotel suite – ask her about it sometime!), But was a more exciting meeting than the ones I’m used to, and I’m already looking forward to next year’s.