1. 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
Ó 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 Á Language Á Data-driven decision
making Á 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

2. 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
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3. 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
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4. 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
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5. 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.
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6. • 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
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7. 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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8. 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.
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