Data Driven Decision Making

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Data Driven Decision Making

  1. 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. 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 414 Early Childhood Educ J (2013) 41:413–421 123
  3. 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 Early Childhood Educ J (2013) 41:413–421 415 123
  4. 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 416 Early Childhood Educ J (2013) 41:413–421 123
  5. 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. Early Childhood Educ J (2013) 41:413–421 417 123
  6. 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 418 Early Childhood Educ J (2013) 41:413–421 123
  7. 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) 41:413–421 419 123
  8. 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. 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). 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  10. 10. Copyright of Early Childhood Education Journal is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

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