The Testing Score Gap Issue Provides a Need for Administrators to Include Teaching Multiple...

Course: 5210 Administrative Instructor: Dr. Crossley Assignment: Multiple Intelligences

1

The Testing Score Gap

Issue Provides a Need for

Administration to Include Multiple Intelligence Teaching Methods in

the Classroom

By

John Johnson


2

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


3

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


4

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


5

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


6

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


7

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


8

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


9

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


10

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


11

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


12

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


13

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


14

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


15

(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


16

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


17

(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


18

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,


19

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


20

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.


21

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.


22

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.


23

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.


24

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)


25

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


26

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.


27

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

References

Anderson, J. (2003). A Historical Context for Understanding the Testing

Score Gap . 1-21.

Ansari, D. (2008). The Brain Goes to School: Strengthening the Education-

Neuroscience Connection. Education Canada, v48 n4 p6-10 Fall 2008. 5 pp.., 6-10.


28

Aud, S. (2010). The Condition of Education. Washington, DC: US Department of

Education.

Bhopal, K. E., & Preston, J. E. (2011). Intersectionality and Race in Education.

Routledge Research in Education. Florence, KY: Routledge, Taylor & Francis

Group. 228 pp.

Blair, C. (2010). Going down to the Crossroads: Neuroendocrinology,

Developmental Psychobiology, and Prospects for Research at the

Intersection of Neuroscience and Education. Mind, Brain, and Education, v4 n4

p182-187 Dec 2010. 6 pp.., 182-187.

Bobby D. Rampey, G. S. (2009). The Nation's Report Card: Long-Term Trend 2008.

Washington, DC: NCES 2009479.

Brouillette, L. (2008). Charter Schools, Lessons in School Reform. In L.

Brouillette, Charter Schools, Lessons in School Reform (p. 220). Mahwah, New

Jersey: Taylor & Frances, e- Library 2008.

Bruer. (2004).

Bryan Kolb, R. G. (2011). Brain Plasticity and Behavior. 64.

Chudler, E. H. (2011, March 27). Neuroscience for Kids. Retrieved March 28, 2011,

from Neuroscience for Kids:

http://faculty.washington.edu/chudler/papy.html

De Smedt, B., Ansari, D., Grabner, R. H., Hannula, M. M., Schneider, M., &

Verschaffel, L. (2010). Cognitive Neuroscience Meets Mathematics

Education. Educational Research Review, v5 n1 p97-105 2010. 9 pp.., 97-105.


29

Duncan, A. (2010, January 10). http://www.ed.gov/blog/2010/01/race-to-the-top.

Retrieved November 23, 2011, from http://www.ed.gov/:

http://www.ed.gov/blog/2010/01/race-to-the-top-%E2%80%93integrity-and-

transparency-drive-the-process/

Ed., U. D. (2010). ED.gov. Retrieved April 7th, 2011, from US Department of

Education: http://www2.ed.gov/policy/elsec/leg/esea02/pg1.html#sec101

Ed., U. D. (2011). http://www2.ed.gov/programs/racetothetop. Retrieved November 23,

2011, from http://www2.ed.gov:

http://www2.ed.gov/programs/racetothetop/index.html

Education, U. D. (2011). ED.Gov. Retrieved 2011, from ED.Gov:

http://www2.ed.gov/policy/gen/leg/edpicks.jhtml?src=ln

Education, U. D. (2011). ed.gov/about. Retrieved November 2011 5th, 2011, from

ed.gov: http://ed.gov/about/overview/budget/statetables/index.html

Foy, J. G., Feldman, M., Lin, E., Mahoney, M., & Sjoblom, C. (2006).

Neuroscience Workshops for Fifth-Grade School Children by

Undergraduate Students: A University-School Partnership. CBE - Life Sciences

Education, v5 n2 p128-136 Sum 2006. 9 pp.., 128-136.

Goswami*, U. (2004). Neuroscience and Education. British Journal of Educational

Psychology (2004), 74, 1–14, 74, 1-14.

Haynes, M. (2011). The Federal Role in Confronting the Crisis in Adolescent

Literacy. Education Digest: Essential Readings Condensed for Quick Review, v76 n8 p10-15

Apr 2011. 6 pp., 10-15.


30

Howard-Jones, P. (2008). Education and neuroscience: evidence, theory and

practical application. Educational Research, 50(2):119-201, 119-201.

Hruby, G. G. (2011). Minding the Brain. Journal of Adolescent & Adult Literacy, v54 n5

p316-321 Feb 2011. 6 pp., 316-321.

Joyce A. McClellan, G. J. (2008). Identifying the Multiple Intelligences of

Your Students. Journal of Adult Education, Volume 37, Number 1, , 13-37.

Kaufmann, L. (2008). Dyscalculia: Neuroscience and Education. Educational

Research, v50 n2 p163-175 Jun 2008. 13 pp.., 163-175.

KELLY, K. (2001). The Harvard Education Letter. Retrieved 2011, from hepg.org:

http://www.hepg.org/hel/article/147

Lake, R., & Hernandez, A. (2011). Eliminating the Achievement Gap: A White

Paper on How Charter Schools Can Help District Leaders. Center on

Reinventing Public Education, University of Washington., 10.

Martensen, R. L. (2006). The Brain Takes Shape: An Early History. J Hist Med

Allied Sci (April 2006) , 247.

Noble, K. G., Tottenham, N., & Casey, B. J. (2005). Neuroscience

Perspectives on Disparities in School Readiness and Cognitive

Achievement. Future of Children, v15 n1 p71-89 Spr 2005. 19 pp.., 71-89.

Oakland, T., & Harris, J. G. (2009). Impact of Test-Taking Behaviors on

Full-Scale IQ Scores from the Wechsler Intelligence Scale for

Children-IV Spanish Edition. Journal of Psychoeducational Assessment, v27 n5 p366-

373 2009. 8 pp.., 366 - 373.


31

OECD. (2008). Understanding the Brain:. OECD/CERI International Conference. Paris,

France: CERI.

Park, H., & Kyei, P. (2011). Literacy Gaps by Educational Attainment: A

Cross-National Analysis. Social Forces, v89 n3 p879-904 Mar 2011. 26 pp., 26.

Parlier, D., & Demetrikopoulos, M. K. (2004). A Touch of Neuroscience.

Science Scope, v28 n2 p48-50 Oct 2004. 3 pp.., 48-50.

Perkins, D. (2009). On Grandmother Neurons and Grandfathers Clock. Mind, Brain,

and Education, v3 n3 p170-175 Sep 2009, 170-175.

Purdy, N. (2008). Neuroscience and Education: How Best to Filter out the

Neurononsense from Our Classrooms? Irish Educational Studies, v27 n3., 197-208.

Verhoeven, M. (2011). Multiple Embedded Inequalities and Cultural Diversity

in Educational Systems: A Theoretical and Empirical Exploration.

European Educational Research Journal, v10 n2 p189-203 2011. 15 pp, 189 - 203.

Williham, D. (2008). When and How NeuroscienceApplies to Education. Phi Delta

Kappan, 421-423.

Wolf, M., Barzillai, M., Gottwald, S., Miller, L., Spencer, K., Norton, E.,

et al. (2009, June). The RAVE-O Intervention: Connecting Neuroscience

to the Classroom. Mind, Brain, and Education, v3 n2 p84-93 Jun 2009. 10 pp, 10.

Yaffe, D., Coley, R. J., & Pliskin, R. E. (2009). Addressing Achievement

Gaps: Educational Testing in America: State Assessments, Achievement

Gaps, National Policy and Innovations. ETS Policy Notes. Volume 17,

Number 1, Winter 2009. Educational Testing Service, 12 pp.


32

Appendix C


33

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


34

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


35

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


36

Proficient, and at the Advanced mathematics achievement levels were


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


poverty schools. In 2009, about 49 percent of 8th-graders from high-


37

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.

The Testing Score Gap Issue Provides a Need for Administrators to Include Teaching Multiple...

Documents

Transcript of The Testing Score Gap Issue Provides a Need for Administrators to Include Teaching Multiple...