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Improving Instructional Practices, Policies, and Student Outcomes for Early Childhood Language and Literacy Through Data-Driven Decision Making

Dominic F. Gullo

Published online: 2 March 2013

� Springer Science+Business Media New York 2013

Abstract Since the passage of No Child Left Behind,

data-driven decision making has become one of the central

foci in schools in their attempt to attain and maintain

adequate levels of student academic performance. The

importance of early childhood education is well established

with language and literacy proficiency in the early years

being viewed as a leading indicator in children’s educa-

tional development. It provides schools with the initial

signs of progress towards academic achievement. In this

article, a conceptual framework for improving instructional

practice and student outcomes in early childhood language

and literacy through data-driven decision making was

described. Four questions served as the structure around

which the conceptual framework was built. These ques-

tions include (1) Why do data need to be collected? (2)

What kinds of data need to be collected? (3) How are the

data collected? (4) How are the data used for making

decisions? Responses to these questions serve as tenets for

guiding the decision making process.

Keywords Literacy � Language � Data-driven decision

making � Assessment

Introduction

The importance placed on early language and literacy is

evident among educators, families, and policy makers

alike. Language and literacy development starts early in

life and is influenced by multiple developmental domains.

In addition, early language and literacy development has

been found to be highly correlated with later school

achievement (Strickland and Riley-Ayers 2006). Language

and literacy proficiency in the early years is seen as a

leading indicator in a child’s educational development by

providing schools with the initial signs of progress towards

academic achievement. Leading indicators in education are

important in that they are viewed as avenues through which

student outcomes are improved and achievement gaps are

reduced. When early language and literacy are viewed as

leading indicators in education, they can be used to assist

educational decision making in a number of ways (Foley

et al. 2008).

First, early language and literacy proficiency can be

used to see the direction in which educational efforts are

going. This can be at the programmatic level, the classroom

level, or the individual child level. Second, once it becomes

evident in which direction an educational effort is headed,

corrective actions can be taken as soon as possible if

needed. Finally, early language and literacy proficiency

data can be used to take actions in planning for intervention

or curriculum reform (Musen 2010). The National Institute

for Literacy (2009, p. 3) explains it well: ‘‘Learning to read

and write opens doors to progress and prosperity across a

lifetime’’.

Contextually speaking, the importance and significance

of the early years of schooling is well established.

Throughout the continuum of children’s schooling, the

skills and knowledge needed to succeed in each subsequent

This paper was based on an invited address presented by the author at

the U.S. Department of Education National Comprehensive Literacy

Institute, July 30—August, 1, 2012, Anaheim CA.

D. F. Gullo (&)

School of Education, Drexel University, 3141 Chestnut Street,

Philadelphia, PA 19104, USA

e-mail: dfg28@drexel.edu

123

Early Childhood Educ J (2013) 41:413–421

DOI 10.1007/s10643-013-0581-x

year are built upon the knowledge and skills acquired in

previous years. Children who lag behind in the early grades

find it more and more difficult to close the gaps that may

result between themselves and other children as they pro-

gress through school.

By the time children are in third grade they are expected

to have the fundamental skills and knowledge required to

be proficient readers (Musen 2010). No longer are children

being taught how to read; rather, they are now expected to

use written language to learn other material in other cur-

riculum areas such as social studies, science and mathe-

matics. By the time children enter fourth grade, there is a

fundamental shift in how the role of reading in the cur-

riculum is viewed. Children shift from ‘‘learning to read’’

to ‘‘reading to learn’’ (Musen 2010, p. 2). This shift is

difficult for children who have not mastered the funda-

mental language and literacy knowledge and skills that are

requisite for successfully accomplishing this change in

fundamental focus.

As previously stated, early language and literacy are

highly associated with later academic success. The results

of one study found that children who lag behind in reading

in third grade are still struggling academically by ninth

grade (Fletcher and Lyon 1998). It was also found that third

grade reading scores can predict, with reasonable measure,

high school graduation (Slavin et al. 1994). According to

Musen (2010), ‘‘early reading skills, therefore, affect not

only graduation rates, but also economic prospects for

students and communities,’’ and as such, ‘‘literacy has

emerged as a key to success in twenty-first century

America’’ (p. 2).

With early literacy being such a powerful indicator of

later school and personal success, it is no wonder that there

is such a fervent emphasis on literacy instruction and

achievement across the grades, but particularly in the early

grades. As a result of this emphasis, efforts to assess and

improve language and literacy curriculum and instruction

through data-driven decision making have become a major

focus in early education. These efforts, along with the

assessment mandates implemented with the passage of the

No Child Left Behind Act (NCLB), have led to the need for

a better understanding of the data-driven decision making

process and its impact in early childhood education.

What is Data-Driven Decision Making?

Since the passage of the (NCLB) in 2001, data-driven

decision making has become the central focus of most

schools in their attempts to attain and maintain specified

levels of student academic competence. During this era of

high-stakes accountability in education, never has there

been a greater need for accurately understanding student,

teacher, and school data. While there is no shortage of data,

there is definitely a challenge in being able to appropriately

interpret and use the data for the purpose of improving

pupil and teacher performance and outcomes.

The ideas behind data-driven decision making are not

new and were originally modeled after business and

industry practices that successfully used data for organi-

zational and product improvement (Marsh et al. 2006).

NCLB’s implementation of standards-based accountability

resulted in increased opportunities and incentives for

making educational decisions based on the use of data.

There was a push for the analysis of new types of data as

well as increased pressure to use these data to improve

students’ test scores (Massell 2001). Under the regulations

of NCLB, states were required to use accountability sys-

tems based on test results that reflected particular criteria

with regard to grade level and the subjects tested. It was

also mandated that the test scores be reported in both

aggregated and disaggregated forms and that schools and

districts were held accountable for the improvement of

student academic performance.

The mere presence of raw data does not ensure that it

will be used to make informed decisions. That is, raw data

alone do not equal information. Once data are collected, in

order to be used for curricular decision making, they must

be organized and amalgamated with an understanding of

the context in which they were collected and will be used.

It should also be noted that if the data that are collected are

not of high quality, these data may lead to misinformation

or result in inferences that are not valid (Marsh et al. 2006).

Schools and districts often struggle with NCLB’s mandates

because the data they are mandated to use are often stored

in forms that are not accessible and are difficult to

manipulate or interpret (Wayman 2005).

According to Marsh et al. (2006), the decisions that are

made using these data often fall into two categories. The

first category of decision making refers to using data to

inform, identify, or clarify. For example, with regards to

early language and literacy, data might be used to identify

language development or emergent literacy program goals

or, conversely, may be used to inform decisions regarding

the content of language and literacy professional develop-

ment opportunities needed for teachers. In the second

category of decision making, data are used to take some

action. Taking action might involve decision making with

regard to curriculum changes or the reallocation of

resources.

Data-driven decision making is closely related to stan-

dards-based accountability. Standards-based accountability

has been the driving force behind the development of

educational policy in the United States (Hamilton et al.

2012) since the enactment of NCLB. Standards-based

414 Early Childhood Educ J (2013) 41:413–421

123

accountability generally includes attainable benchmarks

that specify what children in school are expected to know

and what skills they should be able to demonstrate. It also

includes measures of attainment of these benchmarks as

well as a set of consequences for schools or classrooms

based on these data.

Taken together, the significance of early language and

literacy as a leading indicator in education, along with the

prevalence of data-driven decision making (and standards-

based accountability), there is a urgent need to be able to

identify and use appropriate information for the purpose of

improving student performance in language and literacy in

early childhood education. In this article, a conceptual

framework for improving instructional practice and student

outcomes in early childhood education language and lit-

eracy through data-driven decision making will be exam-

ined. Four questions will serve as the structure around

which this conceptual framework will be built:

1. Why do data need to be collected?

2. What kinds of data should be collected?

3. How are data collected?

4. How are data used for making decisions?

Why Do Data Need to be Collected?

By collecting data, a number of benefits can be realized.

These benefits will not only improve student performance,

but also can lead to improved teacher effectiveness and

program quality (Sagebrush Corporation 2004). The data in

this process should be collected with the expectation of

building a more thorough, complete, and accurate reflec-

tion of children’s performance in school (Rankin and

Ricchiuti 2007). As will be seen, data-driven decision

making can be a powerful tool for revealing needed

change, and for questioning long-held assumptions, as well

as for facilitating communication with and among students,

families and other colleagues. It should become evident,

that while the focus of this paper is data-driven decision

making with regard to early language and literacy devel-

opment and learning, the data-driven decision making

conceptual framework presented here can be applied across

the other curriculum content areas as well.

When responding to the question, why do data need to

be collected, it should be acknowledged that data represent

and can be equated with information—information about

children’s academic performance; information about tea-

cher effectiveness; information about program efficacy.

Through the process of collecting information, a number of

important educational objectives can be achieved. They

primarily include, but are not limited to the following

outcomes.

Narrowing the Gaps in Academic Performance Among

Students

Gaps in academic performance among students or between

and among schools or classrooms can be due to the uneven

distribution of resources coupled with the uneven distri-

bution of students of different ability levels that may be

concentrated in particular schools or classrooms. The data

that are collected will provide quantifiable evidence of the

existence of either of these two situations. Appropriate

resources can be allocated to those schools or classrooms

that are over-populated with lower achieving students or

conversely, students can be reallocated so that more of a

balance of students reflecting different ability levels is

represented within or across schools and classrooms.

Improving Teacher Effectiveness Through Targeted

Professional Development

Through the collection of data (information), the quality of

language and literacy instruction can be enhanced and

improved by targeting teachers’ specific professional

development needs. Through the careful, thoughtful, and

purposeful analysis of student performance it should

become evident which instructional strategies are most

effective, and for which students. It should also become

well-defined where and when there are mismatches

between curriculum content or instructional strategies and

children’s differing levels of development or different

learning styles. These mismatches may interfere with

children’s language and literacy instructional needs being

met and/or attained. Through this process of collecting and

carefully analyzing data, it should become apparent where

and what type of additional professional development is

necessary.

Improving Program Quality Through Proactive

Decision Making

By collecting targeted types of data, program administra-

tors can gain insights into curriculum design and devel-

opment. These data can also provide an understanding of

the root causes of problems or potential problems. This

then provides an avenue through which administrators,

curriculum developers, or teachers can solve problems

holistically, rather than only dealing with the symptomatic

elements of the identified problems. Data provide infor-

mation about what works and what is in need of

improvement. Therefore, best practices can be shared

among classes, school, and districts. Finally, data provide

information about student performance with regard to

attainment of knowledge and skills or rate of progression

through the instructional sequence. This information can be

Early Childhood Educ J (2013) 41:413–421 415

123

used to identify student or class strengths and limitations;

as such, they can become a mechanism for motivating

students.

Communicating Effectively with Education

Stakeholders

The data that are collected can provide information that can

be used with various stakeholders in early education.

Rather than responding to questions in a defensive manner,

factual information gleaned from the data that are collected

can provide a more comprehensive and targeted response.

In addition, the information that collected can also be used

to provide useful information to families. The anticipated

and desired outcome of this is increasing families’

involvement by helping them understand the educational

process in general and, more specifically, how their child

performs within this process.

What Kinds of Data Should be Collected?

Generally speaking, first and foremost, data should be

collected that are both purposeful and systematic. As such,

the data should be tied directly to learning standards and

curriculum goals; tied directly to the needs of individual

programs or students; be the kinds of data that best inform

decision making and help identify patterns of outcomes;

and be the kinds of data that can best be able to help design

strategies that enhance student learning.

Secondly, the data that are collected should come from a

variety of sources and be of different types that include but

are not limited to:

• Demographic data

• Student performance data

• Attitudinal data

• Perception data

• School and classroom process data

• Observational data

By collecting multiple types of data in systematic ways,

information from these data can be used in a variety of

ways; to answer a variety of questions; and to respond to a

variety of early childhood language and literacy needs.

Finally, with regard to early language and literacy, data

should be collected that measure the multiple facets of

language and literacy development that exist among chil-

dren. These data should be representative of speaking and

listening skills as well as reading and writing knowledge

and skills. Specifically, while we know that early literacy

predictors of later reading and school success include oral

language, alphabetic code, and print knowledge (Strickland

and Riley-Ayers 2006), other areas related to literacy

knowledge and skills that should be assessed include:

• Comprehension—language and reading

• Knowledge—background and linguistic

• Structure—phonology, syntax, and semantics

• Decoding—lexical, cipher, and phonemic

• Concepts about print

As was alluded to earlier, there is no shortage of data.

The challenge that exists is in being able to access the data,

and once the data are accessed, ensuring that the appro-

priate types of data are collected and in a format that is

easy to use and understand as well as suitable for

addressing the educational questions being posed. Most

data that are used in educational decision making are stored

in multiple locations and in multiple formats. Oftentimes

these data provide the user with discreet, compartmental-

ized types of information, making it difficult to see patterns

across the different kinds of data that exist. In order for the

data-driven decision making process to be effectively

implemented, the range and assortment of data that exist

must be readily available to both administrators as well as

teachers (Rankin and Ricchiuti 2007).

Due to the high-stakes nature that is frequently associ-

ated with data-driven decision making and federal man-

dates, student test scores represent the most common types

of data that are collected and used. Specifically, state

achievement test scores are used most often in a systematic

way (Marsh et al. 2006). Unfortunately, test results are

often available too late to be effective or useful in making

curriculum, teaching, or school decisions or adjustments

for the current school year during which the test was given.

To make standardized test scores more meaningful for

decision making, an approach that is suggested is imple-

menting a value-added model (VAM: McCaffrey et al.

2003). VAM controls for students’ prior achievement by

estimating the relative impact of schools and/or teachers in

contributing to the achievement growth in students. An

added feature of VAM is that it purports to distinguish

between the effects of school factors from non-school

factors on student learning. Non-school factors include

such things as family background or socioeconomic status.

Although tests of student progress (formative assess-

ments) are more useful and provide more relevant and

frequent information than do end-of-the-year tests (sum-

mative assessments), many administrators and teachers rely

on information that come from other sources. Sources other

than the formative and summative assessments mentioned

above are particularly effective for providing more con-

tinuous information about student progress. These may

include such things as teacher-made classroom assess-

ments, daily assignments, or homework. The type of stu-

dent information that is closely integrated with on-going

416 Early Childhood Educ J (2013) 41:413–421

123

classroom instruction and includes reflective feedback has

been found to be a powerful tool for instructional decision

making (Arter and Stiggins 2005; Boston 2002).

Another issue that affects the kinds of data that can

be collected is data availability. Teachers often do not

have access to the data that they need or want in order

to make the kinds of adjustments to the curriculum or to

their teaching that might be indicated through a sys-

tematic analysis of the data. Oftentimes, teachers do not

have access to the data that they can use to improve

their instruction because districts and schools restrict the

use of the data in order to focus on accountability

concerns and for ensuring that the curriculum and

instruction are aligned with mandated state assessments

(Means et al. 2009). When teachers do have access to

the data systems, they find that they are not user

friendly, may contain limited data, and they lack the

instructional tools that teachers need to make informed

decisions based on the data that is provided to them.

When student data systems are available to teachers, the

kinds of data most frequently available are student

attendance data and student grades.

Classroom teachers with access to student data systems

still are confronted with barriers in attaining the kinds of

data they need and want. While teachers may have access

to the data systems, they lack the knowledge, skills and

training required to use data queries to extract the pertinent

data from these systems. They are also hampered by the

fact that they have limited utility of the kinds of informa-

tion that is available to them in making decisions on what

and how to teach (Means et al. 2009). Classroom teachers,

therefore, are often at the mercy of an administrator and/or

others who have full access to and utility of these data

systems. The effect is that teachers are left to comply with

the decisions that are made by other individuals that reside

outside the classroom environment and by those with no

direct contact with children.

How are Data Collected?

Both formal and informal methods of data collection can

and should be used (Gullo 2005). Formal data collection is

typified by standardized assessments that allow the per-

formance of one student to be compared to that of another

student or to groups of students with similar characteristics

such as age or grade level. Formal methods of assessment

include collecting data from developmental or academic

screening tests, achievement tests, readiness tests, diag-

nostic assessments, or teacher-made tests.

Informal data collection is typified by data that are not

used to compare students one to the other, and may include

such measures as performance assessments, academic or

developmental checklists, or anecdotal and running

records. Data from these types of assessments are often

used to measure individual pupil progress or improvement.

Since NCLB, formal assessment procedures tend to be

over emphasized as a means for collecting data that drive

decision making in schools. Too often, the only data that

are collected are data from formal or standardized assess-

ments using formal assessment procedures. Used alone,

without additional data sources that offer other perspec-

tives, formal assessment procedures often fail to measure

important variables such as:

• Students’ natural curiosity;

• Students’ ability to solve problems;

• Emergent creativity in students’ problem solving and

expression;

• Individual patterns or styles of learning;

• Cultural, ethnic, and linguistic similarities and differ-

ences among students.

In addition, when only formal assessment instruments

and procedures are used, there is an assumption that a one-

size-fits-all model of assessment is appropriate for pro-

viding information to make educational decisions. A

one-size-fits-all model of assessment, however, fails to

recognize differences among children’s early experiences,

opportunities to learn, biological maturation, family

structure or cultural, ethnic, and linguistic backgrounds.

Due to these failings, there are also reliability and validity

issues associated with a one-size-fits-all model of assess-

ment. This is particularly true across the age-groups rep-

resented in early childhood education. This results in:

• Not recognizing the developmental characteristics that

are unique to young children and how these character-

istics result in different ‘‘ways of responding’’ in

assessment situations as compared to older children.

These different ‘‘ways of responding’’ may be due to

behavioral constraints, limitations due to language or

problem solving ability, or children being unfamiliar

with assessment and assessment procedures.

• Not recognizing the differences in learning opportuni-

ties and how that impacts assessment outcomes. Young

children come to school with different home and

academic experiential backgrounds. The differences in

their physical and social experiences may affect how

they respond in assessment situations or how they

demonstrate what they know and can do.

• Not recognizing the developmental variability and

change that exists among young children. At very

young ages, children’s developmental trajectories vary

greatly. This is due to differences in their biological

maturation as well as differences in how they benefit

and change from physical and social experiences.

Early Childhood Educ J (2013) 41:413–421 417

123

• Not recognizing that test scores are but one datum of

information that can be used for decision making.

When assessing young children, it is important to

remember that children at this age do not generalize

knowledge and skills in the same way that they do

when they are older. Therefore, we need to consider

that a score on a test represents only one way in which

children are demonstrating what they know and can do.

As emphasized previously, children should be assessed

in multiple ways and in multiple contexts.

• Not recognizing that there is a lack of predictive

validity between early assessment of academic perfor-

mance and later academic performance. Because young

children’s development is rapid and uneven, as previ-

ously discussed, assessment information can only give

us an indication of how the child is performing now and

in this context. A score on a test is only one moment in

time. This information can be used to partially paint a

picture of where the child is now, but we cannot and

should not use this information to predict with accuracy

where the child will be in the future.

• Not recognizing that there is often a misuse of assessment

data that can lead to negative consequences for children

(known as high-stakes testing). This statement represents

the sum total of the previous five points. Making

academic decisions for young children based on the

results of a test often is detrimental, yet this practice is too

often observed. As previously stated, this is due to the

test’s inability to be sensitive to the developmental

characteristics of young children or to unequivocally

predict young children’s future academic needs.

How are the Data Used for Making Decisions?

According to Snow and Van Hemel (2008), Well planned

and effective assessments can inform teaching and pro-

gram improvement, can contribute to better outcomes for

children (p. 12). There are a number of questions that data-

driven decision making can answer:

• Did something happen?

• Why did it happen?

• How did it happen?

• What works and for whom?

It is critical, that once high-quality and meaningful data

are collected, the users of those data be taught how to

develop strong and relevant questions that focus on edu-

cational issues such as student and teacher performance or

program quality (Rankin and Ricchiuti 2007). In this

manner, a meaningful dialogue can begin to take place

around the significance of the implications that are derived

from the data. According to Streifer (2002), there are

several ways in which data can be used for making edu-

cational decisions. These include, but are not limited to:

exploring differences between and among groups; exam-

ining progress, growth, and/or development over time;

evaluating program efficacy; and identifying the root cau-

ses of problems in the curriculum or instructional approach.

In addition to these uses, data-driven decision making can

also be a strong predictor of school improvement team

efficacy (Chrispeels et al. 2000). It was found that the more

school improvement teams learned about and used data in

the decision making process, the more informed important

decisions were made through the use of data.

Mandinach et al. (2006) suggest that there is a framework

for data-driven decision making. The three elements that are

part of this framework include data, information, and

knowledge. Data has no meaning in and of itself. It only

exists in the raw state. Whether the data become useful or not

depends on the understanding of the data that one has in their

interpretation of the data. Information is the result of this

interpretation and when the data are given meaning when

connected to a context. Information is used to comprehend

and organize the learning environment. It unveils the rela-

tions between the data and the context. Alone, information

has no implications for future actions. Knowledge is the

collection of information deemed useful. It is used to guide

action and is created through a sequential process.

Data-driven decision making can result in schools

making changes that will drive improvement in the areas of

teacher quality, curriculum development, and student per-

formance. The elements of the educational process that

drive school improvement are called levers for change.

Data-driven decision making has informed these processes.

With regard to early language and literacy, five levers for

change have been identified and include the following

(Musen 2010).

Teacher Quality and Professional Development

Low scores in language and literacy performance can be

addressed by good teaching or through changes in teaching

strategies. Even though children enter school with gaps in

their performance levels, quality teaching has been found

as a means to close that gap (Haycock 1998).

Early Education and Family Engagement: Birth to Five

Children enter kindergarten at varying levels of language

and literacy development. Between the ages of birth to five,

language and literacy skills and knowledge are shaped by

elements and experiences in the child’s home and in their

early education opportunities. Children’s literacy profi-

ciency in the primary grades is largely determined by their

418 Early Childhood Educ J (2013) 41:413–421

123

language and literacy proficiency upon entering kinder-

garten. Therefore schools and districts with an interest in

improving literacy performance should consider outreach

to families and programs aimed at the birth-to-five popu-

lation of children.

Curriculum and Instruction

While effective and relevant curriculum can do much to

increase literacy performance, it is important to remember

that no one curriculum or instructional strategy is going to

be appropriate for all young learners. Not all children learn

to read in the same way or at the same pace. By collecting

information on children’s reading behavior, schools can

begin to ‘‘paint a picture’’ of what works for whom.

Appropriate modifications in curriculum and instruction

can be made so that all children’s needs are being met.

Assessment and Early Intervention

Documentation of children’s literacy performance is essen-

tial to understanding whether or not they are benefitting from

the curriculum and instruction that is being implemented or

whether they are progressing at appropriate rates. In addi-

tion, it is well-established that children benefit academically

if intervention is early and targeted. Therefore, by collecting

data on young children’s early language and literacy

achievement, teachers and administrators will have valuable

information for improving curriculum and instruction

through appropriate decision making.

Out-of-School Activities and Community Partnerships

The development of early language and literacy skills are

facilitated both by within school and out of school activities.

Schacter and Jo (2005) found that children who come from

homes of economic poverty can show declines in reading

achievement over the summer, when school is not in session.

They also found that when young children are exposed to high

interest language and literacy activities outside of school,

reading achievement losses are non-existent and sometimes

children actually show achievement gains. If community and

out-of-school program data are collected, it becomes possible

to see where additional resources might be needed that will

provide young children with the kinds of experiences they

need to maintain or increase their reading achievement.

Data-Driven Decision Making in Action

To illustrate the process of data-driven decision making

within the context of early literacy instruction, consider the

following example. Douglas Road Elementary School serves

children from kindergarten through third grade. The princi-

pal, Ms. Cordes, has convened a school-wide planning team

for the purpose of formulating their first early literacy cur-

riculum improvement plan. The planning team searched

system-wide for data that could be used to inform their

improvement plan. Data from statewide literacy achieve-

ment tests for third grade were available; however, the school

did not have access to individual classroom data for Douglas

Road School that would be necessary if they wanted to

impact student achievement. Ms. Cordes recognized that

data played a decisive role in instructional decision making

and program improvement. As a result, she set out to create a

comprehensive data plan for the school’s language and lit-

eracy program that exemplified data across all the grade

levels represented in the school. Consequently, teachers—as

well as other professionals in the school—had access to a

wide range of data-collection and data analysis tools related

to early language and literacy.

The instructional teams at the school recognized the

importance of collecting data from a wide variety of

sources. For example, teachers in all grades were encour-

aged to use frequent, embedded assessments as children

were engaged in the process of reading and writing during

classroom activities. Valuable information about children’s

progress and needs were gleaned through these procedures.

These data provided teachers with information that allowed

them to make informed decisions about the selection of

language and literacy materials as well as instructional

strategies that aligned with children’s learning styles and

reading levels. Data collected in this manner provided

teachers with information regarding the potential need for

modification of the curriculum in order to address indi-

vidual children’s strengths and needs.

While these more naturalistic types of data provided

information on individual children for the purpose of indi-

vidualizing the curriculum, other types of data were col-

lected for the purpose of overall curriculum improvement.

More formal whole-class assessments were administered to

all children to determine the degree to which they were

mastering the knowledge and skills that were the focus of the

literacy unit being taught. These assessments provided

teachers with both formative as well as summative data about

children’s performance. As such, these data provided

teachers with information regarding whether or not the class

was ready to move on to the next instructional unit.

The data from these more formal assessments were used

to examine how successful the curriculum was in effec-

tively delivering the targeted literacy information to chil-

dren. For example, in a unit on ‘‘phonetic principles,’’ the

assessment data indicated that the curriculum was effectual

in the class’s mastery of decoding using beginning con-

sonants to decipher single-syllable words, but not in using

onset and rime to decode unfamiliar words. These data

Early Childhood Educ J (2013) 41:413–421 419

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ultimately indicated to teachers that a modification in the

curriculum was needed to ameliorate this apparent

instructional limitation in the literacy curriculum.

Because data can be gleaned from a number of sources,

the planning team determined that the data had wide appli-

cability across grade levels. As part of the continuing literacy

curriculum improvement plan, data from the various class-

rooms were used to align the K-3 literacy curriculum with

state standards. Data also indicated that while families of

kindergarten and first grade children were actively involved

in their children’s literacy development, families of older

children were less involved. As a result, changes were made

in the types of communication and materials that were sent

home with older children; information and materials that

more directly facilitated family involvement in children’s

language and literacy development.

Finally, the school also used the data to devise ways to

ensure that innovations that were developed as part of the

language and literacy curriculum, and that were proven

methods of improving student literacy competence, were

continued. For example, in order to ensure a seamless

kindergarten through third grade continuity in the literacy

curriculum, two hours of weekly literacy block time was

built in for cross-grade curriculum planning. The continued

use of data has become the cornerstone of Douglas Road’s

literacy improvement plan.

One Final Question

It has been shown that collecting and analyzing appropriate

information in appropriate ways will lead to appropriate

decisions being made. Data-driven decision making can

provide the answers to the questions that we have. While

questions provided the framework for discussing data-

driven decision making with regard to early language and

literacy, one question still remains.

A final question that can be asked is: What does this all

mean? There are two answers to this question. Generally

speaking, data-driven decision making goes well beyond

simply complying with NCLB performance requirements. It

can serve as a powerful process for districts to facilitate

more informed decision making, boost overall school per-

formance and improve student achievement (Sagebrush

Corporation 2004, p. 11). More specifically, early reading

proficiency can serve as a useful leading indicator for

academic success in later grades. Districts that can effec-

tively evaluate early reading proficiency as a leading

indicator will be taking an important step toward large-

scale reform through data-driven decision making (Musen

2010, p. 6).

Early childhood education in general and early language

and literacy in particular are gaining prominence as leading

indicators in guiding the decisions that are being made by

curriculum developers and policy makers. These individ-

uals look to early education and early language and literacy

development as a means to an end. Improvement in early

education and improvement in early language and literacy

instruction will lead to improvements in later educational

attainment, overall and with regard to literacy.

References

Arter, J., & Stiggins, R. (2005). Formative assessment as assessment

for learning. National Council on Measurement in Education

Newsletter, 13(3), 4–5.

Boston, C. (2002). The concept of formative assessment, ERIC Digest,

ED470206, College Park, MD: ERIC Clearinghouse on Assess-

ment and Evaluation. Available online at http://www.ericdigest.

org/2003-3/concept.htm.

Chrispeels, J. H., Brown, J. H., & Castillo, S. (2000). School leader-

ship teams: Factors that influence their development and

effectiveness. In K. Leithwood (Ed.), Understanding schools

as intelligent systems (Vol. 4 in series, Advances in research and

theories of school management and educational policy)

(pp. 39–73). Stamford, CT: JAI Press.

Fletcher, J. M., & Lyon, G. R. (1998). Reading: ‘‘A research-based

approach’’. In W. M. Evers (Ed.), What’s gone wrong in America’s

classrooms? (pp. 49–90). Stanford, CA: Hoover Institution Press.

Foley, E., Mishook, J., Thompson, J., Kubiak, M., Supovitz, J., &

Rhude-Faust, M. K. (2008). Beyond test scores: Leading

indicators for education. Providence, RI: Brown University,

Annenberg Institute for School Reform. Available for download

at www.annenberginstitute.org/WeDo/leading_indicators.php.

Gullo, D. F. (2005). Understanding assessment and evaluation in

early childhood education (2nd ed.). New York, NY: Teachers

College Press.

Hamilton, L. D., Stecher, B. M., & Yuan, K. (2012). Standards-based

accountability in the United States: Lessons learned and future

directions. Education Inquiry, 3(2), 149–170.

Haycock, K. (1998). Good teaching matters: How well-qualified

teachers can close the gap. Washington, DC: Education Trust.

Mandinach, E.B., Honey, M., & Light, D. (2006, April). A theoretical

framework for data-driven decision making. Paper presented at

the Annual Meeting of the American Educational Research

Association, San Francisco, CA.

Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of

data-driven decision making in education. Santa Monica, CA:

The Rand Corporation. Available for download at

http://www.rand.org/pubs/occasional_papers/OP170.html.

Massell, D. (2001). The theory and practice of using data to build

capacity: State and local strategies and their effects. In S.

H. Fuhrman (Ed.), From the capitol to the classroom: Standard-

based reform in the states. Chicago, IL: University of Chicago

Press.

McCaffrey, D. F., Lockwood, J. R., Koretz, D. M., & Hamilton, L. S.

(2003). Evaluating value-added models for teacher accountabil-

ity. Santa Monica, CA: The Rand Corporation.

Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing

data-informed decision making in schools: Teacher access,

supports and use. Washington, DC: U.S. Department of Education

Office of Planning, Evaluation and Policy Development.

Musen, L. (2010). Early reading proficiency: Leading indicators for

education. Providence, RI: Annenberg Institute for School

420 Early Childhood Educ J (2013) 41:413–421

123

Reform, Brown University. Available for download at

www.annenberginstitute.org/WeDo/leading_indicators.php.

National Institute for Literacy. (2009). National reading achievement

goals inform early literacy instruction. Press release. Washing-

ton, DC: National Institute for Literacy.www.nifl.gov/news/

NELP01-08-09.html.

Rankin, L. D., & Ricchiuti, L. M. (2007). Data-driven decision

making: Five questions to help make sense of your data.

Classroom connect, 14(1), 4–6.

Sagebrush Corporation. (2004). Data-driven decision making: A

powerful tool for school improvement (A White Paper). Minne-

apolis, MN: Sagebrush Corporation.

Schacter, J., & Jo, B. (2005). Learning when school is not in session:

A reading summer day-camp intervention to improve the

achievement of existing first-grade students who are economi-

cally disadvantaged. Journal of Research in Reading, 28(2),

158–169.

Slavin, R. E., Karweit, N. L., Wasik, B. A., Madden, N. A., & Dolan,

J. L. (1994). Success for all: A comprehensive approach to

prevention and early intervention. In R. E. Slavin, N. L. Karweit,

& B. A. Wasik (Eds.), Preventing early school failure

(pp. 175–205). Boston, MA: Allyn & Bacon.

Snow, C. E., & Van Hemel, S. B. (2008). Early childhood assessment:

Why, what and how. Washington, DC: The National Academies

Press.

Streifer, P. A. (2002). Using data to make better educational

decisions. Lanham, MD: Scarecrow Press.

Strickland, D., & Riley-Ayers, S. (2006). Early literacy: Policy and

practice in the preschool years. New Brunswick: NJ: National

Institute for Early Education Research.

Wayman, J. C. (2005). Involving teachers in data-driven decision

making: Using computer data systems to support teacher inquiry

and reflection. Journal of Education for Students Placed at Risk,

10(3), 295–308.

Early Childhood Educ J (2013) 41:413–421 421

123

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