For more information contact:
TABLE OF CONTENTS
Science Section:
1. The Relationship Between Density and Buoyancy
2. The Dire Wolf Project
3. Growing Plants In An Area With Very Limited Light
4. Traveling Air Pollution That Causes Acid Rain
5. Planting In Sand and Soil
Math Section:
1. The Distributive Property of Multiplication
2. Using Samples to Predict
3. The Associative Property of Addition and Multiplication
Social Studies Section:
1. Tasting Unfamiliar and Healthy Food
2. Survey of Females and Males Working With Computers
SCIENCE SECTION
TITLE: The Relationships Between Density and Buoyancy
STUDENT RESEARCHERS: Abra Murray
SCHOOL: Mandeville Middle School
Mandeville, Louisiana
GRADE: 6
TEACHER: John I. Swang, Ph.D.
I. STATEMENT OF PURPOSE AND HYPOTHESIS:
I would like to do a scientific research project to determine the
relationships between density and buoyancy. My hypothesis states
that the density of an object and a liquid will affect the
object's buoyancy.
II. METHODOLOGY:
First, I wrote my statement of purpose. Then I researched my
topic and wrote a review of the literature. Next, I developed my
hypothesis. Then I wrote my methodology to test the hypothesis.
Next, I wrote a list of materials and developed an observation and
data collection form. Then I began my experimentation. I found
the densities of four different objects and liquids. In the first
two trials, I used an object less dense than the liquid and in the
last two trials I used an object more dense than the liquid. Then
I put the object less dense than the liquid in the liquid and
recorded whether or not the object sunk. Then I put an object
more dense than the liquid in the liquid and recorded whether or
not the object sunk. I repeated this procedure for the other
objects and liquids. Next, I wrote my analysis of data. Then I
wrote my summary, conclusion, and application. Finally, I sent my
abstract to the Journal of Student Research for publication.
III. ANALYSIS OF DATA:
For the first trial, I used butter which has a density of .865
grams per cubic cm. and milk which has a density of 1.0315 grams
per cubic cm.. When I placed the butter in the milk it floated.
For the second trial, I used cardboard which has a density of .69
grams per cubic cm. and water which has a density of 1 gram per
cubic cm.. When I placed the cardboard in the water it floated.
For the third trial, I used paper which has a density of 1.1 grams
per cubic cm. and olive oil which has a density of .918 grams per
cubic cm.. When I placed the paper in the olive oil it sunk. For
the fourth trial, I used a small brick which has a density of 2.2
grams per cubic cm. and methyl (rubbing alcohol) which has a
density of .81 grams per cubic cm.. When I placed the brick in
the methyl it sunk.
IV. SUMMARY AND CONCLUSION:
All the objects denser than the liquid sunk and all the objects
less dense than the liquid floated. Therefore, I accept my
hypothesis which stated that the density of an object and liquid
will affect the object's density.
V. APPLICATION:
My data indicates, that if I want something to be able to float I
will have to make sure that the object I want to float is less
dense than the liquid I want it to float in.
Title: The Dire Wolf Project
Student Researchers: Jennifer Bailey, Mike Bryant, Megan Carey,
Kristin Kutz, Kyla Marchand, Ryan Martel,
Christopher Mello, Nils Pilotte
School: North Elementary School
Somerset, MA
Grade: 5
Teacher: Bruce J. Herman
I. STATEMENT OF PURPOSE AND HYPOTHESIS:
We want to find out how big the dire wolf was (height and weight)
because there are no records that we could find. We thought this
was an excellent challenge since the dire wolf is extinct.
II. METHODOLOGY:
First, we divided ourselves into two groups of four. Then we each
took turns measuring the Dire Wolf's skull size. We had to decide
what part of the skull to measure and do it all the same way each
time we measured the skull. Next, we measured the skull sizes of
our own domesticated dogs at home. We also measured the shoulder
height of our dogs. We hoped that this information would help us
predict the size of the Dire Wolf by using proportions. After that
we made one big bar graph using the measurements from all of our
animals. With this information we were able to make a hypothesis
of the size the Dire Wolf. This information was shared with two
other elementary school with the use of e-mail. We decided that
our prediction of the size of the Dire Wolf was accurate according
to the results of the other two schools. Finally, we made a
composite cut of the Dire Wolf and hung it up in our school.
III. ANALYSIS OF DATA:
We found out that the Dire Wolf's weight was about 110 pounds. The
Dire Wolf's height was about 72 centimeters. We determined these
sizes by plotting the information on a graph of our other dogs.
IV. SUMMARY AND CONCLUSION:
We found out that the more animals (pet dogs) we had on our graph
the more accurately we could predict the size of a Dire Wolf. We
used the results from the three schools to prove that our
predictions were accurate.
V. APPLICATION:
This process can be used to measure and predict the size of many
prehistoric animals by using a model of a body part for
measurement. This information may be useful to anyone interested
in comparing the sizes of existing animals and extinct animals.
Title: Growing Plants in an Area With Very Limited Light
Student Researchers: Ed-Co Fifth Graders
School: Edgewood-Colesburg Elementary
Colesburg, Iowa
Grade: 5
Teacher: Kayla Ramsey
I. Statement of Purpose and Hypothesis:
What we wanted to do was put plants in a special place in our
school that has very little sunlight. Our hypothesis was that
some plants would grow well with very little light and others
would not.
II. Methodology:
We planted 32 different kinds of seeds in the same soil. We put
them all in cups. We started them all growing in the classroom
with quite a bit of light. Then we put them in the place in our
school with very little light where we wanted them to grow. We
left the plants in this area for four weeks.
III. Analysis of Data:
All of the 32 plants we grew did well in the classroom where we
started growing them. After growing them and taking care of them
in the area of our school with very little light for four weeks,
most of the plants had stopped growing, grew very slowly, or died.
There were just three main kinds of plants that seemed to survive
quite well in this area with very little light.
IV. Summary and Conclusion:
We found that most of the 32 kinds of plants that we planted did
not thrive or survive in very limited sunlight. The three kinds
of plants that did fairly well in the area of our school with very
little sunlight were Begonias, Sweet Peas, and Morning Glories.
V. Application:
We will inform our principal that we found that Begonias, Sweet
Peas, and Morning Glories were the type of plants that will grow
successfully in the special area of our school with very little
light.
TITLE: Traveling Air Pollution That Causes Acid Rain
STUDENT RESEARCHER: Peter Tinti
SCHOOL: North Stratfield School
Fairfield, Connecticut
GRADE: 4
Teacher: Mr. Carbone
I. STATEMENT OF PURPOSE AND HYPOTHESIS:
I want to know if our state is down wind from states that emit
high amounts of air pollution that cause acid rain. I think the
states up wind from us produce high amounts of air pollution that
cause acid rain.
II. METHODOLOGY:
I will look a the jet stream for three weeks to see where we get
our air. Then I will examine those states in Connecticut's path
to find out what kinds of industries they have. This will
determine if the state is a high air pollution producer or a low
air pollution producer.
III. ANALYSIS OF DATA:
After looking at the jet stream for three weeks I found some
patterns. The wind swoops down in the mid-west. Then it goes up
New England. These were the following states that the wind came
from: New York, New Jersey, West Virginia, Ohio, and Pennsylvania.
>From my research, I found that each one of these states except
Pennsylvania produce high amounts of air pollution that produce
acid rain.
IV. SUMMARY AND CONCLUSION:
Connecticut has high amounts of air pollution that causes acid
rain coming toward us. I accept my hypothesis.
V. APPLICATION TO LIFE:
I can apply this to life because I know that Connecticut is not a
good place for farming and growing plants because of acid rain.
Title: Planting In Sand and Soil
Student Researchers: Brindi Lott, Sarah Drew, Jim Pellegrino,
Elizabeth Allen, Jenny Sinn, Elecia Valenti,
Scooter Blake, Bobby Ault, Ryan Sievers,
Nick Rossy
School: Enfield Elementary,
Ithaca, New York
Grade: 2
Teachers: Maria Leahy and Daisy Sweet
I. Statement of Purpose and Hypothesis:
We wanted to find out if popcorn seeds would grow better in sand
or soil. We predicted the popcorn seed would not grow in sand.
We predicted that the popcorn seeds would grow in the soil.
II. Methodology:
We planted 4 popcorn seeds in each pot, one with sand and one with
soil. We put in the same amount of sand and soil. We watered
them each day. We put them next to each other in the sunlight.
We observed them and measured them. We recorded our observations
in our science journals.
III. Analysis of Data:
After six days, we did our first observation. We noticed that the
sand had three plants and the tallest plant was 3 cm. In the
soil, there were four plants and the tallest plant in the soil was
4 cm. After 18 days, the sand had one plant and it was 17 cm.
Some of the plant's leaves were brown. In the soil, there were 4
plants and the tallest was 34 cm. The plants in the soil were
dark green and the plant in the sand was light green.
IV. Summary and Conclusion:
We learned that it is better to plant in soil than sand. Our
prediction that popcorn seeds would grow in soil was true. Our
prediction that popcorn seeds would not grow in sand was false.
One plant in the sand is still alive, but it has begun to die
since the leaves are brown. We think that if we waited more days,
our prediction about plants dying in the sand would be true.
V. Application:
We will tell others to grow plants in soil, not in sand.
MATH SECTION
TITLE: The Distributive Property of Multiplication
STUDENT RESEARCHER: Abra Murray
SCHOOL: Mandeville Middle School
Mandeville, Louisiana
GRADE: 6
TEACHER: John I. Swang, Ph.D.
I. STATEMENT OF PURPOSE AND HYPOTHESIS:
I would like to do a scientific research project to determine
whether the distributive property is correct. The distributive
property of multiplication is a rule that states that multiplying
a sum by a number is the same as multiplying each addend by the
number and adding the products:
(AxB)+(AxC) = A(B+C)
My hypothesis states that distributive property of multiplication
will be correct.
II. METHODOLOGY:
First, I wrote my statement of purpose. Then I researched my
topic and wrote a review of the literature. Next, I developed my
hypothesis. Then I wrote my methodology. Then I wrote a list of
materials. Next, I developed an observation and data collection
form. Then I began my experimentation. I took the mathematical
statement (AxB)+(AxC) and calculated the answer. Then I
simplified the statement using the distributive property of
multiplication and got A(B+C). I repeated this procedure for six
problems. Then I recorded my data. Next, I wrote my analysis of
data and accepted or rejected my hypothesis. Then I wrote my
summary, conclusion, and application. Finally, I sent my abstract
to the Journal of Student Research for publication.
III. ANALYSIS OF DATA:
The first problem stated (3+2)6 =. I found an answer of 30 by
multiplying the sum of 3+2 or 5 by 6 and an answer of 30 by
multiplying 6 times 3 and 6 times 2 and adding the products. The
second problem stated (17+41)4 =. I found an answer of 232 by
multiplying the sum of 17+41 or 58 by 4 and an answer of 232 by
multiplying 4 times 17 and 4 times 41 and adding the products.
The third problem stated (9+18)2 =. I found an answer of 54 by
multiplying the sum of 9+18 or 27 by 2 and an answer of 54 by
multiplying 2 by 9 and 2 by 18 and adding the products.
IV. SUMMARY AND CONCLUSION:
I got the same answer working the problems out using (A+B)C= as I
did working the problem out using (AxC)+(BxC)=. Therefore, I
accept my hypothesis which stated that the distributive property
of multiplication would be correct.
V. APPLICATION:
My data indicates that, a person could solve a problem by using
(AxC)+(BxC)= or (A+B)C=. They could work the problem out the way
it was easiest for them and still get the correct answer.
TITLE: Using Samples To Predict
STUDENT RESEARCHER: Michael Clark
SCHOOL: Mandeville Middle School
Mandeville, Louisiana
GRADE: 6
TEACHER: John I. Swang, Ph.D.
I. STATEMENT OF PURPOSE AND HYPOTHESIS:
I would like to do a scientific research project about using
samples from a large population to make predictions about the
population. My hypothesis states that I can use samples to
predict how many beans are in a one pound sack of beans.
II. METHODOLOGY:
First, I wrote my statement of purpose and reviewed my literature.
Then I developed my hypothesis and wrote my methodology. Next, I
bought one bag of uncooked beans. Next, I made an observation and
data collection sheet. Then I opened up the bag and counted out
fifty beans and marked them with a black marker. I placed them
back into the bag and shook them up. Then I picked out a sample
of 50 beans and counted out all of the marked beans. I then
multiplied the number of total marked beans I put in the sack by
the number of beans I took in my sample. Then I divided that
number by the number of marked beans I found in the sample. The
formula I used is written below. N is equal to the total number
of beans in the bag.
Number of marked beans in sample Total number of marked beans
--------------------------------- = ----------------------------
Total number of beans in the sample N
I repeated this two more times. Then I counted the number of
beans by hand that were in the sack to see if it was equal to the
number of beans I estimated by sampling. Then I wrote my analysis
of data. Then I wrote my summary and conclusion and accepted or
rejected my hypothesis.
III. ANALYSIS OF DATA:
On my first trial, I sampled 50 beans from the bag of beans. There
were four marked beans in my sample. 625 was my predicted total
number of beans. I counted a total of 813 beans in the bag. The
difference between my sampling estimate and the total number was
188 beans. On my second trial I sampled fifty beans from the bag.
There were five marked beans in my sample. 500 beans was my
predicted total number of beans. The difference between my
sampling estimate and the total number of marked beans was 313
beans. My third trial was the same as the first.
IV. SUMMARY AND CONCLUSION:
There were large differences between the acutual number of beans
in the bag and that which my samples predicted. I reject my
hypothesis which stated that I could use sampling to predict the
number of beans in a bag. I believe that this large difference
was due to the fact that I did not have a large enough sample to
accurately predict the total number of beans in the bag.
V. APPLICATION:
>From my research, I can tell scientists to make sure that when
they use samples to predict that they have a large enough number
in their sample to predict accurately.
TITLE: The Associative Property of Addition and Multiplication.
STUDENT RESEARCHER: Allison Walter
SCHOOL: Mandeville Middle School
Mandeville, Louisiana
GRADE: 6
TEACHER: John I. Swang, Ph.D.
I. STATEMENT OF PURPOSE AND HYPOTHESIS:
I would like to do a scientific research project on the
associative property of addition and multiplication. The
associative property states that when adding or multipling three
or more numbers, the grouping of the addends or factors can be
changed and the sum or product will be the same. The associative
property can be expressed as:
(A+B)+C = A+(B+C) and (AxB)xC = Ax(BxC)
My hypothesis states that the associative property of addition and
multiplication is correct.
II. METHODOLOGY:
First, I wrote my statement of purpose. Then I wrote my review of
the literature. Next, I wrote my hypothesis. Then I wrote my
methodology. Next, I made my list of materials. Then I made my
data collection form. Then I took the mathematical statement
(AxB)C = A(BxC) and (A+B)+C = A+(B+C) and substituted my values to
make six different problems, three addition and three
multiplication. Then I calculated the answer using sets for the
multiplication and filled out my data collection sheet
accordingly. Next, I wrote my analysis of data and made my charts
and graphs. Then I wrote my summary and conclusions and accepted
or rejected my hypothesis. Finally, I wrote my application and
applied my findings to the real world.
III. ANALYSIS OF DATA:
The three problems of addition had the same outcomes for the two
algorithms. The three multiplication problems had the same
outcome for the two algorithms and the sets.
IV. SUMMARY AND CONCLUSION:
Since the outcomes were the same for the addition and
multiplication algorithms, the associative property is correct.
Therefore, I accept my hypothesis which stated that the
associative property of addition and multiplication will be
correct.
V. APPLICATION:
According to my data, the associative property works. So now I
can use the property in my math work.
SOCIAL STUDIES SECTION
TITLE: Tasting Unfamiliar and Healthy Food
STUDENT AUTHOR: Katherine Lande
GRADE: 4
SCHOOL: WindyCreek Homeschool
706 Sussex Road
Wynnewood PA, 19096-2414
TEACHER: Mrs. Nancy Lande
I. STATEMENT OF PURPOSE AND HYPOTHESIS
I wanted to find out how much of a strange, but healthy food
someone would want to try it they had never seen or tasted it
before. My hypothesis stated that more than 50% of the people I
sampled would take only one or two pieces of an unfamiliar food.
II. METHODOLOGY
First, I researched what healthy foods are. I wrote a statement
of purpose and my hypothesis. I determined that I needed at least
15 people for my sample and then I developed a tally sheet that
would show how many pieces of food people picked. Then I went to
a health food store and picked out a food that I thought most
people were not familiar with. I picked out snack sized puffed
rice cakes with herb and garlic seasoning. I put one piece of it
in on a napkin, two pieces on another napkin, three pieces on
another, and four pieces on the last napkin. I set out a card in
front of each napkin with the number of pieces written on it. I
put the napkins on a table with a sign saying that it was a
healthy food and to take a sample. I sampled both children and
adults at a science fair. As each person took a sample, I circled
the number of pieces that they picked. Afterward I gave them a
paper with the name and ingredients of the snack and then I asked
them to write why they chose the number of pieces of food they
picked. I scored the questionnaires and analyzed the data. At
the end, I wrote a summary and conclusion and checked my work for
publication.
III. ANALYSIS OF DATA
Of the 15 people that I sampled, I found that 7 of the people
picked either 1 or 2 pieces to try. This is 47% of the sample.
Four people tried 1 piece of the unfamiliar food, three people
tried two pieces, six people tried three pieces, and two people
tried four pieces.
IV. SUMMARY AND CONCLUSION
Since 47% of the people chose 1 or 2 pieces, I rejected my
hypothesis. People took the napkins with more pieces than I
thought. When I asked them why they chose that number, most of
them said it was because they were hungry, even though they didn't
know what the food was. Some of the people who picked only 1 or 2
pieces said they would have picked 4 pieces, but they were afraid
they would look like a pig so they didn't. Most of the people
really did like the new snack. This study tells me that people
are willing to take a bigger portion of an unfamiliar "healthy"
food than I had thought.
V. APPLICATION:
I hope my study will encourage marketing studies to consider
people's willingness to try new, healthy snacks. I was surprised
and pleased to see that people were willing to take a risk on
something new and this might help people to try making a better
world.
TITLE: Survey of Females and Males Working With Computers
STUDENT RESEARCHER: Jackie Sanchez & Jason Riley
SCHOOL: Las Vegas, Nevada
Chaparral High School
GRADE: 12
TEACHER: Mr. Wood
I. STATEMENT OF PURPOSE AND HYPOTHESIS:
Over the last two months, we conducted an e-mail survey in order
to investigate the ratio of females to males involved in working
with computers and their motivations for doing so. Our hypothesis
stated that more males would be working with computers than
females.
II. METHODOLOGY:
We developed a survey requesting information about the location of
the responding schools, the ages and sex of students using
computers, and various questions about their background. The
survey was sent out on the telecomputing networks on April 7 and
we received 12 responses.
III. ANALYSIS OF DATA:
In some age groups, there were more males and in others more
females. Combined there were eight hundred and ninety-eight
students in the survey. This total was divided in five age
groups.
Actual Numbers of Students Using Computers
Ages Males Females Total
0 - 9 ( 39) ( 35) ( 73)
10 - 13 (119) (112) (231)
14 - 16 (201) (221) (422)
17 - 19 ( 72) ( 69) (141)
20+ ( 18) ( 12) ( 30)
-----------------------------------
Total (449) (449) (898)
There were exactly the same number of females as there are of
males using the computer in the survey. A large number of
students reported their parents had no contact with computers.
The parents who do use computers, mostly use them for
spreadsheets, payroll, and to type memos. Most of the students
involved with computers were mainly interested with the
practicality of computers. A very small percentage planed to
major in a computer related subject. Many of the students got
involved with computers as a school requirement and now enjoy the
benefits.
IV. SUMMARY AND CONCLUSION:
We did not receive enough answers to be able to call the survey
accurate. However, in the schools that did reply, the number of
males and females was exactly the same. Conclusions cannot be
drawn from this result, although it would seem that females are
playing an important role in the telecommunications and computer
world and are as active in it as the males. Therefore, we
tentatively reject our hypothesis which stated that more males
would be working with computers than females.
© 1994 John I. Swang, Ph.D.