Post on 29-Apr-2023
Course: 5210 Administrative Instructor: Dr. Crossley Assignment: Multiple Intelligences
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The Testing Score Gap
Issue Provides a Need for
Administration to Include Multiple Intelligence Teaching Methods in
the Classroom
By
John Johnson
Course: 5210 Administrative Instructor: Dr. Crossley Assignment: Multiple Intelligences
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Abstract
This proposal will focus on the the need for school administrators to
include teaching to multiple intelligence in the classroom due to
educational achievement gaps between European Whites (majority) and
African Americans (minority) in American Education, which has long
been a social and political issue. The evolution of education in
American history is well documented, as is the government’s
intervention for finding solutions to the testing gap issue. The
period from 1950 through the 60’s becomes problematic to judge student
achievement due to the lack of statistics and appropriate measures,
but there are statistical evidence and measures from 1971 through
2010, which continue to raise the issue of achievement gaps (Ed. U.
D., 2010). This study examines the information reported by the US
Department of Education, as it relates to historical testing scores
and educational resources made available to our education system, the
Organization for Economic Co-operation and Development (OECD) to the
application and the possible connection of Multiple Intelligence with
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education, and the vast opinions of the scholars and scientists across
the world as to the basis for this type of intervention. This study is
to determine whether Multiple Intelligence is an option for educators
and learners for improved learning, and if so, is there a basis for
the application of Multiple Intelligence in our educational
institutions.
Introduction
To reflect on the history of education in the United States is to
reflect on the history of inequalities injected into the U.S.
education system for which minorities had to endure. The historical
issues, such as: The Literacy Gap, The Elementary School Attendance
Gap, and finally, The High School Completion Gap, all concurrently
precluded the testing scores gap issue (Anderson, 2003). It is well
documented that the achievement gaps mentioned were and is directly
attributed to educational inequalities, which stemmed from racism and
the hierarchal norms that were prevalent during these times. It is
also argued that ethnic minority pupils face multiple sources of
inequalities, of a social (redistributive) and cultural (recognition)
nature for which is linked as a basis of achievement gaps in our
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education system (Anderson,2003),(Verhoeven,2011), (Bhopal & Preston,
2011). Since the mid-50’s, and as recently as 2009, with the Race to
the Top Program, the government has enacted 45 different legislations
(Education, ED.Gov, 2011), including the landmark Civil Rights Act of
1964. These laws presumably gave equal access to an education, but not
necessarily access to an equal education. The many laws enacted, as
well as, adoptions of different nutritional and intervention programs
were to help narrow the testing scores gap, yet, they proven to
reflect little or no statistical change in the testing scores gap. The
reported measures and the literature reviewed raise the question, is
there a basis for adding Multiple Intelligence (M.I.) teaching methods
to the classroom?
Testing Scores Gap and the Possible Solutions
Between 1971 and 2010, there are significant statistics which reflect
a disparity in testing score gaps between the majority and the
minority. The US Department of Education and The Institution of
Educational Science (IES) have posted its annual performance report
for 2010 (Ed., 2011). The report shows minimal increase in average
test scores in math and reading overall for students at all levels of
education. Over recent years, education policy has included the NCLB
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Act of 2001 and 2004, which refer educators to offer students the
highest quality of education and learning, yet, the 2010 IES
performance report indicates that students’ test scores of minority
students continue to lag behind those students who are considered the
majority. There is no single answer as to why there is a disparity in
the test scores. Classification of the issues that may be attributed
to this disparity include but is not limited to the student’s
socioeconomic environment (nutrition, healthcare, family income,
family structure), child neglect or abuse, and educational resources
(educational materials, educational funds, and teacher qualifications
or the teacher to student ratio). The government over the years has
introduced many programs in an attempt to address most, if not all of
the classifications which may be attributed to any learning disorders
or educational disadvantages for minority students (Education, ED.Gov,
2011), which has become the prelude to the application of Multiple
Intelligence teaching methods in our education system.
What is Multiple Intelligence?
In order to fully understand the concept we must understand the
individual who brought this theory to light. The man is Howard
Gardner. Howard Gardner is the John H. and Elisabeth A. Hobbs
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Professor of Cognition and Education at the Harvard Graduate School of
Education. Gardner also holds positions as Adjunct Professor of
Psychology at Harvard University and Senior Director of Harvard
Project Zero. In 2008 he was selected by Foreign Policy and Prospect
magazines as one of 100 most influential public intellectuals in the
world. Multiple Intelligences refers to the concept developed by
Howard Gardner that challenges the traditional view of intelligence
and explains the presence of nine different Multiple Intelligences
(Joyce A. McClellan, 2008). One way of addressing individual
differences among adult learners is to identify the Multiple
Intelligences of the learner. There have been studies conducted
regarding Multiple Intelligence since before the Eighteenth Century.
As a matter of disclosure, Eric Chudler writes “the word "brain"
appears on an ancient paper-like document called the Edwin Smith
Surgical Papyrus”. The “Papyrus” document was written around the year
1700 BC, but is based on texts dated around 3000 BC (Chudler, 2011).
Ansari noted “Although attempts to connect Multiple Intelligence, the
brain, and education have been scarce, there has been recently a wave
of enthusiasm for making greater links between learning sciences”
(Ansari, 2008). The theory of Multiple Intelligence may influence
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research in mathematics education by (a) contributing to our
understanding of atypical numerical and mathematical development, (b)
paving the way for setting up behavioral experiments and (c)
generating findings about learning and instruction that cannot be
uncovered by behavioral research alone (De Smedt, Ansari, Grabner,
Hannula, Schneider, & Verschaffel, 2010). There are indications that
although research of Howard Gardner’s “Multiple Intelligence Theory”
hasn’t proven very much about reading instruction, the research has
proven fascinating to many educators nonetheless, and increasingly it
has been making appearances, often trivial but occasionally profound,
in the literature related to literacy instruction and development
(Hruby, 2011).
Review of Literature
As I immersed into the depth of my study, the history of inequalities
in our education system and the idea of merging M.I. theory and
education, only raised more questions. The literature reviewed,
brought out the intrinsic feeling to dive deeper into contextualizing
the testing score gap and all possible solutions. I embraced the ideas
of enhancing my understanding in the physical abilities of the brain
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and the difficulty of applying M.I. teaching methods to classroom
instructions. The many offering views from experts in this area will
show why merging Multiple Intelligence teaching into our education
system offers administrators, teachers, families, and the students,
insights to cognitive learning. Malcolm Knowles (1970) conceptualized
andragogy as “the art and science of helping adults learn” (p. 38).
The learning styles as it relates to M.I. in education will further
our knowledge in this area. The critics and concerns of M.I. in
education brings openness to this study and bring to light untested
issues, and finally, how socioeconomic conditions give concern to
possible links to poor test scores. The literature focused on all of
the areas mentioned and proved to be very profound to say the least.
It allowed me to critically think about this study and to what impact,
if any, can teaching in the areas of Multiple Intelligence have on our
ability to teach, learn, and test better.
Current and Previous Recommended and Attempted Solutions
The legislation and recommendations to help solve the testing scores
gap issue, such as the 45 laws mentioned, proved thus far to be
insignificant. The current trend is to believe that charter schools
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can make a difference (Lake & Hernandez, 2011), since 1985, there has
been an influx of charter schools (KELLY, 2001), (Brouillette, 2008).
Can we count on all of them to synchronize into one successful model
and are we prepared to end traditional public schools as they are
today. Others recommend that every school should have equal resources
(Park & Kyei, 2011), yet, research indicates the federal government
distributes equal funding to our states (based on the student
population) for educational needs and resources (Education,
ed.gov/about, 2011). The most recent program is the 4 Billion dollar
Race to the Top program (Ed. U. D., 2011). This program will provide
funding for 48 of the fifty states whose budget ranges from 20 Million
dollars to 700 Million dollars, depending on the number of students
within its respective State (Duncan, 2010). At the time of this study
and through the second of three phases, only 12 of the 48 states have
received funding from this program (Ed. U. D., 2011).
Educators and policymakers have begun to realize that the essential
task of closing achievement gaps will require new kinds of
accountability systems, and tests (Yaffe, Coley, & Pliskin, 2009), but
such a drastic and expensive educational reform will not guarantee
success (Haynes, 2011)and although there has been new legislation
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which spreads accountability, there remains no significant change to
the testing scores gap (Education, ED.Gov, 2011). Although new laws
and greater resources may account for statistical change in testing
scores gap, we must look at other alternatives which may help in
making significant long term change to this issue. The brain learns
every day, but how does it process the information?
The Plasticity of the Brain Allows for Greater Learning
The underlying assumption of the studies of brain and behavioral
plasticity is that if behavior changes, there must be some change in
organization or properties of the neural circuitry that produces the
behavior. Conversely, if neural networks are changed by experience,
there must be some corresponding change in the functions mediated by
those networks (Martensen, 2006). For the investigator interested in
understanding the factors that can change brain circuits, and
ultimately behavior, a major challenge is to find and to quantify the
changes. Neuroimaging (Spatial)of adolescents shows us that the
adolescent brain is far from mature, and undergoes extensive
structural changes well past puberty (OECD, 2008). The idea
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characterized by being able to see an image or situation and quickly
assess areas that could be changed to transform or improve the
appearance (Joyce A. McClellan, 2008). Due to plasticity, we are
genetically poised to make neuronal connections, which is the basis of
all cognitive breakthroughs. The brain’s acquisition of reading
(Linguistic)is an example of this: to read, the brain must build new
connections among circuits designed thousands of years ago for older
visual, auditory, linguistic, and cognitive operations (Williham,
2008). To understand language, the student must have verbal/
Lingusitic Intllegence, the Retrieval, Automaticity, Vocabulary,
Engagement with Language, and Orthography (RAVE-O) program represents
our evolving knowledge from the cognitive neurosciences, linguistics,
and education, and its integration with best classroom practices
(Wolf, et al., 2009). Knowing how the brain is processing information
may lead to better testing scores.
Multiple Intelligence Theory Application to Education
Research has shown that there is a significant relationship between
the brain and stress, “there is a neuroscientifically definable
optimal level of stress arousal in children against which various
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curricula and teaching and learning activities can be examined”
(Blair, 2010). The knowledge of how the brain changes with new
experiences (Bryan Kolb, 2011), raises question regarding the
application of Multiple Intelligence teachings in the classroom,
namely, when is it applicable to do so. In Northern Ireland, the
education system is approaching an unprecedented curricular reform
(which was being phased in from September 2007) whose rationale is
essentially based on cognitive neuroscience (Howard-Jones, 2008).
Researched information has touched on possible criterion for adding
methods of teaching multiple intelligence to the classroom (Williham,
2008). First, there are times when two well-developed behavioral
theories make very similar predictions, making them difficult to
separate with behavioral data. But at the neural level, it might be
possible to make different predictions. Second, Multiple Intelligence
data can show us that there is diversity where there appeared to be
unity, or unity where one might suspect diversity. That is, we might
discover that what seemed like a single type of behavior (e.g.,
“learning”) is actually supported by nine anatomically distinct brain
systems, which indicates (but doesn’t prove) that what we thought was
a single function is in fact nine different functions, operating in
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different ways. Third, Multiple Intelligence data might prove useful
for the diagnosis of some learning disabilities. Dyslexic readers
show patterns of brain activity on electroencephalograms that differ
from those of average readers. Early diagnosis would allow early
intervention, which could be an enormous advance (Williham, 2008)
(Goswami*, 2004). Although the neuroscientific research on numeracy
is still in its infancy, the field has already made significant
progress in the past decade. It shows that even very simple numerical
operations are distributed in different parts of the brain and require
the co-ordination of multiple structures (OECD, 2008).
The Theory of Multiple Intelligence can offer more structure to Educational Practices
Persuasion one way or the other as to what M.I. can offer more to
educational practices than that of the status quo, must be conclusive
in order for me to feel comfortable with my conclusion on this topic.
It is argued here that to speak to the classroom, neuroscience has to
shout across two gaps (Perkins, 2009). The different levels of
explanation along with the epistemological contrast between
explanation theories and action theories, roughly the contrast between
basic science on the one hand and engineering science and craft on the
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other (Perkins, 2009), can and has created a sense of utter confusion.
What is clear is the determination that learning is not studied at the
cell level (Goswami*, 2004), but neuroscientists and scholars alike
certainly can assert that what is going on in education has to reflect
the processes of the brain. Accordingly, understanding the brain
better may not have simple, but powerful information on how we can
manage learning and teaching (Perkins, 2009). Most educators believe
that in order to teach reading and math well, there should be a set of
guidelines or principles (Perkins, 2009). We should want to know the
best way to get the maximum results and use it as a learning tool.
Take into consideration our brain and numbers, as the brain relates to
numeracy, genetically-defined brain structures alone cannot support
mathematics as they need to be coordinated with those supplementary
neural circuits not specifically destined for this task but shaped by
experience to do so. Hence, the important role of education, whether
in school, at home, or in play; and hence, the valuable role the
theory of Multiple Intelligence can play in helping address this
educational challenge (OECD, 2008). Multiple Intelligence may also
offer methods for the early identification of special needs, and
enable assessment of the delivery of education for special needs
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(Goswami*, 2004). Used creatively, M.I. methods have the potential to
deliver important information relevant to the design and delivery of
educational curricula as well as the quality of teaching itself
(Goswami*, 2004). Cognitive neuroscience and Multiple Intelligence in
education has varying opinions (Bruer, 1997; Byrnes & Fox, 1998;
Geary, 1998; Geake & Cooper, 2003; Mayer, 1998; Schunk, 1998;
Stanovich, 1998), but in general the consensus is moving away from
early views that Multiple Intelligence is irrelevant because it only
confirms what was already known. The eventual answer will probably be
that it is very valuable indeed (Goswami*, 2004).
Learning Styles at it Relates to Neuroscience Multiple Intelligence in Education
Multiple Intelligences is specifically defined, investigates the
processes by which the brain learns and remembers from the molecular
and cellular levels right through to brain systems (e.g., the system
of neural areas and pathways underpinning our ability to speak and
comprehend language) (Goswami*, 2004). The perfect storm for poor
testing as research on children's counterproductive test behavior
supports a three-factor model for behaviors, which are attributed to
socioeconomic behavior: inattentiveness, avoidance, and uncooperative
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mood (Oakland & Harris, 2009). Based on this research that students’
ability to test requires not only a level of knowledge, but a high
level of attention, engagement, and a cooperative mood to be
successful when tested, leads one to wonder, where does Multiple
Intelligence fit into this equation. One of the most common models is
the VAK classification where learners are tested to discover whether
they are visual, auditory or kinesthetic learners. Thompson and
Maguire (2001) outline the different learning styles and suggest
strategies to promote greater learning (Purdy, 2008): visual learners,
it is claimed, learn better through seeing pictures, diagrams, moving
images and color, and are encouraged to use pictures, mind maps or
different color pens to help the brain remember better; auditory
learners learn by storing sounds in their brains and are encouraged to
listen to music while learning, repeat their work out loud in funny
voices and make up raps about their work; kinesthetic learners learn
by movement or touch and should do things practically, walk around
while reading, do brain gym exercises or squeeze a sponge or stress
release ball while working (Purdy, 2008). Existential is the
appreciation of spirituality and understanding questions about life.
This intelligence relates to exploring human existence in the universe
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(Gardner, 1999, p. 115). The OECD indicates that the mere
representation of numbers involves a complex circuit that brings
together sense of magnitude, and visual and verbal representations.
Logical-Mathematical Intelligence calls on other more complex and
distributed networks, varying according to the operation in question.
Subtraction is critically dependent on the inferior parietal circuit,
while addition and multiplication engage yet others. Research on
advanced mathematics is currently sparse, but it seems that it calls
on at least partially distinct circuitry. Understanding the underlying
developmental pathways to mathematics from a brain perspective can
help shape the design of teaching strategies (OECD, 2008). This focus
on learning and memory can be at a variety of levels. Interpersonal
intelligence is the proficiency of an individual in perceiving the
moods, aims, motivations, and emotions of others (Gardner, 1983, pp.
237-276). Intrapersonal is having a positive self-concept and life
direction which is intrinsically grounded, the competency in knowing
oneself and acting to modify oneself based on that knowledge. Music is
the ability to appreciate, distinguish, compose, and perform in
various musical forms. Finally we have the Naturalistic intelligence
which is the ability to appreciate, categorize, classify, explain, and
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connect to things encountered in nature (Gardner, 1999, p. 115).
Understanding cell signaling and synaptic mechanisms (one brain cell
connects to another via a synapse) is important for understanding
learning (Goswami*, 2004). Successful learning is not just dependent
on the information alone but a combination of the curriculum and the
teacher, the context provided by the classroom and the family, and the
context of the school and the wider community (Goswami*, 2004).
The Critics and Concerns of Multiple Intelligence in Education
There are harsh criticisms and concerns which raises other questions
regarding the application of multiple intelligence practices in
education. To many M.I. application to education is not highly
regarded. In the article “Neuroscience and Education: How Best to
Filter out the Neurononsense from Our Classrooms?” (Purdy, 2008),
Purdy states criticism of the role of the different intelligences
connected to the M.I. theory in education is not new. Purdy goes on to
say, “Bruer, deemed ‘the most outspoken critic of a premature
application of brain research to education’ (Blakemore and Frith 2005,
9) famously argued (Bruer 1997, 5) that the ‘neuroscience and
education argument may be rhetorically appealing, but scientifically,
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it’s a bridge too far’”. Critics urge caution in attempting to make
direct links between classroom learning and Multiple Intelligence
Theory. Purdy goes on to reference (Bruer, 2004), it is said that what
neuroscience and some of the Multiple Intelligences so far has had to
offer education is distinctly meager (Bruer, 2004).
Multiple Intelligence Theory in Education: Socioeconomics conditions and Test Scores
Twenty percent of public elementary schools and nine percent of public
secondary schools in the United States are considered high-poverty
schools and these schools educate approximately 6 million elementary
school students and 1 million secondary school students. Test scores
in schools has forever been linked to economics, in “Neuroscience
Perspectives on Disparities in School Readiness and Cognitive
Achievement”, socioeconomic status--already linked with how well
children do on skills tests generally--is particularly closely linked
with how well they perform on tasks involving these crucial
neurocognitive systems (Noble, Tottenham, & Casey, 2005). The
education system’s pursuit of a happy median between economics and
education is limitless when searching for additional ingredients to
the recipe for academic achievement. The National Science Education
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Standards recommend that science be taught using inquiry-based
approaches (Foy, Feldman, Lin, Mahoney, & Sjoblom, 2006). The
Organization for Economic Co-operation and Development (OECD, 2008)
indicated by conditioning our minds and bodies correctly, it is
possible to take advantage of the brain’s potential for change and to
facilitate the learning process which include all intelligences, not
just the traditional ones. The students in the middle schools are
said to be the ideal candidates for Multiple Intelligence research in
the field of education. The preteen years of the middle grades
represent a time of incredible physical and mental development
(Parlier & Demetrikopoulos, 2004). Recently, accumulating empirical
evidence supporting the validity and reliability of the hypothesis
preference indicator, which is named Multiple Intelligences Survey
(MIS), exists for identifying Multiple Intelligence preferences of
adult learners (McClellan, 2006). Howard Gardner first introduced
Multiple Intelligences over 20 years ago. Gardner’s theory provides a
theoretical foundation for recognizing different abilities and
talents. This theory acknowledges that while all students may not be
verbally or mathematically gifted, students may have an expertise in
other areas.
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Conclusion
This study will show the US government has intervention in our
education system (NCLB Act, Section 1001. (2), (3)) (Ed., 2010) has
rewarded limited success. The assertion that in order to determine the
output of Multiple Intelligence teaching in the classroom, we must
understand the brain, how education and learning can be advanced with
the addition of M.I. in the education system, and when to apply M.I.
in the classroom is well understood. School administrators must
recognize that Multiple Intelligence Theory becomes a tool for
cognitive learning and seems to offer various possibilities to
education, including the early diagnosis of special educational needs,
the monitoring and comparison of the effects of different kinds of
educational input on learning, and an increased understanding of
individual differences in learning as well as the best ways to suit
input to learner. Although there are concerns and skeptics (Bruer,
2004), (Goswami*, 2004), (OECD, 2008), (Purdy, 2008), there is a
consensus among these same skeptics that relates to the positive
effects which teaching to different intelligences will continue to
make in the development of learning when merged with the classroom.
The information on past and current academic status of minorities (Ed.
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U. D., 2010) and the numerous different legislation and programs
offered to combat the underlying issues has proven to be significantly
ineffective (Education, ED.Gov, 2011), and along with the opinions of
experts and scholars, the indication is that there is a basis for
applying the Multiple Intelligence Theory to education.
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Type of Design
Qualitative study
Survey
Operational Definition and Terms
Socioeconomic
o A family's socioeconomic status is based on family income,
parental education level, parental occupation, and social
status in the community
Child Abuse
o physical, sexual, emotional mistreatment, or neglect of
children
Child Neglect
o Neglect is failing to provide adequate food, clothing,
shelter, supervision or medical care.
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Teacher to student ratio
o The number of teachers in a school with respect to the
number of students who attend the institution. High student-
teacher ratio is often cited for criticizing proportionately
underfunded schools or school systems
Majority – White European Americans and Jewish Americans
Minority – All races other than that of White European Americans
Multiple Intelligence Theory (M.I.) - Gardner’s (1983) theory
conceptualized intelligence as consisting of several distinct
intelligences rather than a singular cognitive capacity.
Neuroscience
o The scientific study of the nervous system
o Cognitive Development
o Field of study in neuroscience and psychology focusing on a
child's development in terms of information processing
Plasticity
o The ability for the brain to learn as it experiences new
events
Institution of Educational Science (IES)
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o Established under the Education Sciences Reform Act of 2002,
IES operates with the counsel and oversight of the National
Board for Education Sciences.
US Department of Education
o The U.S. Department of Education is the agency of the
federal government that establishes policy for, administers,
and coordinates most federal assistance to education.
NCLB
o No Child Left Behind Act 2001
Learning Disorders
o Learning disorders affect how a person understands,
remembers and responds to new information.
Activity
o Reduced brain activity (non-learning, cognitive stimulation)
Cognitive stimulation
o Process to which the brain is stimulated from learning and
new experiences
Low to Mid-Level test scores
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o Scores that are considered below the mean or within the mean
Basis of the study
Standardized Test
o Any test in which the same test is given in the same manner
to all students in the education system is a standardized
test.
High-Poverty Schools
o High-poverty schools are those where 76–100 percent of
students are eligible for FRPL
o The school poverty measure used throughout is the percentage
of a school's enrollment that is eligible for free or
reduced-price lunch (FRPL) through the National School Lunch
Program (NSLP).
Low-Poverty Schools
o Low-poverty schools are those where 0–25 percent of students
are eligible for FRPL.
Assumptions, Delimitations, and Limitations
Assumptions
All students can test better by merely studying more.
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Participants will be Parents, Teachers, and Scholars who
otherwise understand the issues of High Poverty Middle
Schools.
Delimitations
Survey sampling of 55,000 Parents, Teachers, and
Scholars associated with High Poverty Middle schools
worldwide.
Limitations
Teaching is very important to successful learning. It
is notable, however, that neuroscience does not study
teaching (Goswami*, 2004).
Validity of Criticisms
History, Demand Characteristics, and Avis effects
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Appendix C
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Outcomes for Students Who Attend High-Poverty Schools
High-poverty School Student Historical
Average Performance on NAEP Assessments1
On average, students from high-poverty schools did not perform as well
on National Assessment of Educational Progress (NAEP) reading,
mathematics, music, and art assessments as students from low-poverty
schools.
Reading
Reading on each NAEP assessment given between 1998 and 2009, average
reading scores for 4th- and 8th-grade Students from high-poverty
schools were lower than the scores for students from low-poverty
schools (see tables A-10-1 and A-10-2). In 2009, the average NAEP
reading score (on a 0–500 point scale) for 4th-grade students from
high-poverty schools was 202, while the average score for 4th-graders
from low-poverty schools was 237. The average score for 4th-graders
from high-poverty schools increased between 1998 and 2009, from 187 to
202, while the score for 4th-graders from low-poverty schools
increased from 231 to 237. The reading achievement gap between low-
1 All Statistical information from National Center for Educational Statistics 2010
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and high-poverty 4th-grade students decreased from 44 points in 1998
to 35 points in 2009. The percentages of 4th-grade students from high-
poverty schools performing at or above the Basic, at or above the
Proficient, and at the Advanced reading achievement levels were lower
than the respective percentages of students from low-poverty schools
(see table A-10-3). In 2009, about 45 percent of 4th-graders from
high-poverty schools performed at or above Basic, compared with 83
percent of 4th-graders from low-poverty schools. Similarly, 14 percent
of 4th-graders from high-poverty schools performed at or above
Proficient, compared to 50 percent of 4th-graders at low-poverty
schools. In 2009, the average NAEP reading score (on a 0–500 point
scale) for 8th-grade students from high-poverty schools was 243, while
the average for 8th-graders from low-poverty schools was 277. Between
1998 and 2009, scores for 8th-graders from low-poverty schools
increased 4 points, from 273 to 277, while there was no measurable
change in the scores of 8th-graders from high-poverty schools. The
reading achievement gap between low- and high-poverty 8th-grade
students was 34 points in 2009. The percentages of 8th-grade students
from high-poverty schools performing at or above the Basic, at or
above the Proficient, and at the advanced achievement levels were
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lower than the respective percentages of 8th-grade students from low-
poverty schools. In 2009, about 53 percent of 8th-graders from high-
poverty schools performed at or above Basic, compared with 87 percent
of 8th-graders from low-poverty schools. Similarly, 12 percent of 8th-
graders at high-poverty schools scored at or above Proficient,
compared with 47 percent of 8th-graders at low-poverty schools.
Mathematics
Mathematics on each NAEP assessment given between 2000 and 2009,
average mathematics scores for 4th- and 8th-grade students from high-
poverty schools were lower than the scores for students from low-
poverty schools (see tables A-12-1 and A-12-2). In 2009, the average
NAEP mathematics score (on a 0–500 point scale) for 4th-grade students
from high-poverty schools was 223, while the average score for 4th-
graders from low-poverty schools was 254. The average score for 4th-
graders from high poverty schools increased 18 points between 2000 and
2009, from 205 to 223, while the score for 4th-graders from low-
poverty schools increased 14 points, from 239 to 254. The mathematics
achievement gap between low- and high-poverty 4th-grade students was
31 points in 2009. The percentages of 4th-grade students from high-
poverty schools performing at or above the Basic, at or above the
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Proficient, and at the Advanced mathematics achievement levels were
lower than the respective percentages of 4th-grade students from low-
poverty schools (see table A-12-3). In 2009, about 64 percent of 4th-
graders from high-poverty schools performed at or above Basic, 17
percent performed at or above Proficient, and 1 percent performed at
Advanced. In contrast, about 93 percent of 4th-graders from low-
poverty schools performed at or above Basic, 60 percent performed at
or above Proficient, and 12 percent performed at Advanced. In 2009,
the average NAEP mathematics score (on a 0–500 point scale) for 8th-
grade students from high poverty schools was 260, while the average
for 8th-graders from low-poverty schools was 298. Between 2000 and
2009, scores for 8th-graders from high-poverty schools increased 14
points, from 246 to 260. During that period, scores for 8th-graders
from low-poverty schools increased 11 points, from 287 to 298. The
mathematics achievement gap between low- and high-poverty 8th-grade
students was 38 points in 2009. The percentages of 8th-grade students
from high-poverty schools performing at or above the Basic, at or
above the Proficient, and at the advanced achievement levels were
lower than the respective percentages of 8th-grade students from low-
poverty schools. In 2009, about 49 percent of 8th-graders from high-
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poverty schools performed at or above Basic, 13 percent performed at
or above Proficient, and 1 percent performed at Advanced. In contrast,
about 87 percent of 8th-graders from low-poverty schools performed at
or above Basic, 50 percent performed at or above Proficient, and 15
percent performed at Advanced.