1. Questions, Claims, and Evidence presents a new approach to science teaching that engages students
fully by linking literacy and inquiry. With it you’ll replace the lab reports of traditional science teaching with
the writing of scientists searching for answers. And in the process, you and your students may well
discover that you enjoy and learn from science time more than ever.
Step by step Questions, Claims, and Evidence immerses students in scientific inquiry and writing. It
transforms experiments from following directions and making notes into chances to pose and answer
questions that interest students. Its approach helps you:
increase students’ interest in science by showing students how to ask good questions and
design their own experiments to answer them
improve their analysis skills by giving them tools to make and support scientific claims
boost their science writing by offering meaningful opportunities to argue for, reflect on, and
summarize their findings.
But Questions, Claims, and Evidence doesn’t only support student learning. It improves your science
teaching by:
broadening your professional knowledge with the latest research and theory
providing self-evaluation tools for monitoring your performance
answering frequently asked questions about the Questions, Claims, and Evidence approach.
Try something new that will motivate your students and improve their writing abilities. Read Questions,
Claims, and Evidence, and don’t be surprised if your students agree with this fifth grader’s sentiment: “I
love the way that we do science now because I learn more and I get to do more. I actually feel like I am
smart.”
Step by step Questions, Claims, and Evidence immerses
students in scientific inquiry and writing. It transforms
experiments from following directions and making notes into
chances to pose and answer questions that interest students.
Its approach helps you:
• increase students’ interest in science by showing students
how to ask good questions and design their own
experiments to answer them
• improve their analysis skills by giving them tools to make
and support scientific claims
• boost their science writing by offering meaningful
opportunities to argue for, reflect on, and summarize
their findings.
But Questions, Claims, and Evidence doesn’t
only support student learning. It improves your science
teaching by:
• broadening your professional knowledge with the
latest research and theory
• providing self-evaluation tools for monitoring your
performance answering frequently asked questions
about the Questions, Claims, and Evidence
approach.
Try something new that will motivate your students
and improve their writing abilities. Read Questions,
Claims, and Evidence, and don’t be surprised if your
students agree with this fifth grader’s sentiment: “I love
the way that we do science now because I learn more
and I get to do more. I actually feel like I am smart.”
It seems only appropriate when writing a book about science writing that we

2. should use the very template that we advocate for science inquiries to express our
gratitude to the many people who made this book possible. It all started with a
question.
1. Beginning ideas: We had a question: “How does the Science Writing Heuristic
approach work in the elementary classroom?” To answer this question, we had
the help of many school districts, teachers, students, and administrators who
joined us in this inquiry, asked their own questions about science and literacy,
and pushed us every day to think deeply about teaching and learning.
2. Tests: The test was to examine the use of the SWH approach with classroom
teachers in preschool through sixth grade. This work would not have been
possible without the support of a Math-Science Partnership grant and the
State of Iowa who supported the teachers and researchers to engage in this
investigation.
3. Observations:We observed, interviewed, videotaped, analyzed, and took notes.
We had dialogue and examined our data, which lead to new observations with
an amazing research team including Murat Gunel, RecaiAkkus, Sara Nelson,
Sarah Trosper, Kyle Rasmussen, Elham Mohammad, Ryan Kelly, Ahmad AlKofahi, Bill Crandall, and Jay Staker.Over the years we have had numerous undergraduate
students who have provided support to this project—managing
data, scoring writing samples, transcribing, and analyzing: Micale Coon, Jessica
Drey, Alicia Johnson, Kevin Jolly, Katie Raymon, Lisa Ryherd, Katherine
Schnoor, Sara Ann Smith, and Ashley Titman. In addition, many preservice
teachers participated in this project by providing an audience for SWH classrooms
by reading and responding to penpalletters.Your thoughtful response
over the years has made writing purposeful for children. Finally, a special thankyou
to the many freshman honors mentees who chose to participate in the project
as beginning researchers; your insight has been invaluable.
4. Claims: We made claims based on the evidence. Having the opportunity to
“gopublic”with your claims and thinking is a key part of the learning process.
Daily,we share our thinking with our colleagues, students, teachers, and friends
at Loess Hills Area Education Agency 13, Iowa State University, and the
Acknowledgments
x■AC KNOWL E D GMENT S
University of Iowa.We thank you for your continued support of our questions as
teachers, researchers, and writers.
5. Evidence: Once the evidence was gathered, we reflected upon our understanding
by writing. The results were overwhelming—when teachers are willing to reexamine
their beliefs about teaching and learning and give the process a go, students
and teachers are successful. Here we must thank the support of sixty
teachers across the United States who read the first draft of this book,“had a go”
in their own classrooms, and gave us extensive feedback to bring this revised draft
to you. The field testing of the first draft was supported through a Teacher Professional
Continuum grant (No. ESI—0537035) through the National Science Foundation.
An advisory board has also provided thoughtful response and feedback on
our efforts including Donna Alvermann, Sharon Dowd-Jasa,Todd Goodson, Kathy
McKee,Wendy Saul, and Larry Yore.We thank you for your wisdom and continued
“nudging”as we grow in our own understanding of teaching and learning, science
and literacy.
6. Reading: We asked the experts—of course, the teachers and the students whose
stories you will read in this book—but a special thank-you to Jan Westrum and
Allyson Forney who read very early drafts of this book and provided insightful
feedback about audience and style.
7. Reflection: Finally, reflection—in reflecting on what has made this project possible,
we must thank our program assistants, Tracie Miller and Allison Donaldson.Your
attention to detail, pep talks, humor, and ability to multitask has made this book an
intriguing endeavor as you both reminded us daily of the important work we were
doing. Also, a special thank-you to the Heinemann Team and Robin Najar for seeing
the value in this project and providing ongoing questions to fuel the writing

3. (and future investigations!).
And, with extreme gratitude and pride, we thank our families who create spaces
and time for us to practice what we teach and continually encourage us to have a go
http://undsci.berkeley.edu/teaching/educational_research.php
Formulation of a question: The question can refer to the explanation of a specific
observation, as in "Why is the sky blue?", but can also be open-ended, as in "How can I
design a drug to cure this particular disease?" This stage also involves looking up and
evaluating evidence from previous experiments, personal scientific observations or
assertions, and/or the work of other scientists. If the answer is already known, a different
question that builds on the previous evidence can be posed. When applying the scientific
method to scientific research, determining a good question can be very difficult and
affects the final outcome of the investigation.[19]
Hypothesis: Anhypothesis is a conjecture, based on knowledge obtained while
formulating the question, that may explain the observed behavior of a part of our
universe. The hypothesis might be very specific, e.g., Einstein's equivalence principle or
Francis Crick's "DNA makes RNA makes protein",[20] or it might be broad, e.g., unknown
species of life dwell in the unexplored depths of the oceans. A statistical hypothesis is a
conjecture about some population. For example, the population might be people with a
particular disease. The conjecture might be that a new drug will cure the disease in some
of those people. Terms commonly associated with statistical hypotheses are null
hypothesis and alternative hypothesis. A null hypothesis is the conjecture that the
statistical hypothesis is false, e.g., that the new drug does nothing and that any cures are
due to chance effects. Researchers normally want to show that the null hypothesis is
false. The alternative hypothesis is the desired outcome, e.g., that the drug does better
than chance. A final point: a scientific hypothesis must be falsifiable, meaning that one
can identify a possible outcome of an experiment that conflicts with predictions deduced
from the hypothesis; otherwise, it cannot be meaningfully tested.
Prediction: This step involves determining the logical consequences of the hypothesis.
One or more predictions are then selected for further testing. The less likely that the
prediction would be correct simply by coincidence, the stronger evidence it would be if
the prediction were fulfilled; evidence is also stronger if the answer to the prediction is
not already known, due to the effects of hindsight bias (see also postdiction). Ideally, the
prediction must also distinguish the hypothesis from likely alternatives; if two hypotheses
make the same prediction, observing the prediction to be correct is not evidence for either
one over the other. (These statements about the relative strength of evidence can be
mathematically derived using Bayes' Theorem.)
Testing: This is an investigation of whether the real world behaves as predicted by the
hypothesis. Scientists (and other people) test hypotheses by conducting experiments. The
purpose of an experiment is to determine whether observations of the real world agree
with or conflict with the predictions derived from an hypothesis. If they agree, confidence
in the hypothesis increases; otherwise, it decreases. Agreement does not assure that the
hypothesis is true; future experiments may reveal problems. Karl Popper advised
scientists to try to falsify hypotheses, i.e., to search for and test those experiments that
seem most doubtful. Large numbers of successful confirmations are not convincing if
they arise from experiments that avoid risk.[21] Experiments should be designed to
minimize possible errors, especially through the use of appropriate scientific controls. For
example, tests of medical treatments are commonly run as double-blind tests. Test
personnel, who might unwittingly reveal to test subjects which samples are the desired

4. test drugs and which are placebos, are kept ignorant of which are which. Such hints can
bias the responses of the test subjects. Furthermore, failure of an experiment does not
necessarily mean the hypothesis is false. Experiments always depend on several
hypotheses, e.g., that the test equipment is working properly, and a failure may be a
failure of one of the auxiliary hypotheses. (See the Duhem-Quine thesis.) Experiments
can be conducted in a college lab, on a kitchen table, at CERN's Large Hadron Collider,
at the bottom of an ocean, on Mars (using one of the working rovers), and so on.
Astronomers do experiments, searching for planets around distant stars. Finally, most
individual experiments address highly specific topics for reasons of practicality. As a
result, evidence about broader topics is usually accumulated gradually.
Analysis: This involves determining what the results of the experiment show and
deciding on the next actions to take. The predictions of the hypothesis are compared to
those of the null hypothesis, to determine which is better able to explain the data. In cases
where an experiment is repeated many times, a statistical analysis such as a chi-squared
test may be required. If the evidence has falsified the hypothesis, a new hypothesis is
required; if the experiment supports the hypothesis but the evidence is not strong enough
for high confidence, other predictions from the hypothesis must be tested. Once a
hypothesis is strongly supported by evidence, a new question can be asked to provide
further insight on the same topic. Evidence from other scientists and experience are
frequently incorporated at any stage in the process. Depending on the complexity of the
experiment, many iterations may be required to gather sufficient evidence to answer a
question with confidence, or to build up many answers to highly specific questions in
order to answer a single broader question.
This model underlies the scientific revolution.[22] One thousand years ago, Alhazen demonstrated
the importance of forming questions and subsequently testing them,[23] an approach which was
advocated by Galileo in 1638 with the publication of Two New Sciences.[24] The current method
is based on a hypothetico-deductive model[25] formulated in the 20th century, although it has
undergone significant revision since first proposed (for a more formal discussion, see below).
GREAT PBLS EARTH SCIENCE
http://www.k5geosource.org/
http://school.discoveryeducation.com/lessonplans/k-5.html
http://www.docfizzix.com/products/science-lessons/#
Good Infor
http://web.nmsu.edu/~susanbro/educ451/docs/inquiry_based_science_classroom.pdf
Water Cycle
http://ga.water.usgs.gov/edu/watercycle2ndgrade.html
https://www.teachervision.com/ecological-environment/printable/32539.html