Teachers’ Use of Assessment Data to Inform Instruction: Lessons From the Past and Prospects for the Future
by Amanda Datnow & Lea Hubbard - 2015
Background Data use has been promoted as a panacea for instructional improvement. However, the field lacks a detailed understanding of how teachers actually use assessment data to inform instruction and the factors that shape this process.
Purpose: This article provides a review of literature on teachers’ use of assessment data to inform instruction. We draw primarily on empirical studies of data use that have been published in the past decade, most of which have been conducted as data-driven decision making came into more widespread use. The article reviews research on the types of assessment data teachers use to inform instruction, how teachers analyze data, and how their instruction is impacted.
Research Design: Review of research.
Findings: In the current accountability context, benchmark assessment data predominate in teachers’ work with data. Although teachers are often asked to analyze data in a consistent way, agendas for data use, the nature of the assessments, and teacher beliefs all come into play, leading to variability in how they use data. Instructional changes on the basis of data often focus on struggling students, raising some equity concerns. The general absence of professional development has hampered teachers’ efforts to use data, as well as their confidence in doing so.
Conclusions: Given that interim benchmark assessment data predominate in teachers’ work with data, we need to think more deeply about the content of those assessments, as well as how we can create conditions for teachers to use assessment to inform instruction. This review of research underscores the need for further research in this area, as well teacher professional development on how to translate assessment data into information that can inform instructional planning.
With the focus on high-stakes accountability, the last decade of educational reform has seen a rise in the promotion and use of data for instructional decision making. Data use has been seen as a panacea for school improvement, and activities ranging from the examination of results from state tests to formative assessment in classrooms have all been put under the umbrella of data use (Kennedy, 2011). The data that educators are drawing on are wide ranging as well, including data on student achievement, student attendance and behavior, course enrollment patterns, postsecondary success rates, and school climate, among others (Bernhardt, 1998; Data Quality Campaign, 2011). Thus, when we talk about or study data use, it is important to be clear about what data are used, for what purposes, and by whom.
Our focus is on how teachers use assessment data to inform instructional decision making. Although we restrict our discussion to teachers use of data from assessments, we acknowledge that assessment data are only one form of data that teachers use to inform their instruction (Mandinach & Gummer, 2013). Assessment data have the potential to inform how teachers plan lessons, identify concepts for reteaching, and differentiate instruction (Hamilton et al., 2009; Kerr, Marsh, Ikemoto, Darilek, & Barney, 2006; Supovitz & Klein, 2003). Yet how teachers actually use assessment data to inform instructional practice and the factors that shape their decision making remain puzzling (Coburn & Turner, 2011; Little, 2012), in part because there is relatively little research on this topic. As a recent analysis of a collective body of research on data-driven decision making noted, we are faced with blunt understandings of data use (Moss, 2012, p. 224).
This article provides a review of literature on teachers use of data. Most of the research we review was conducted in the United States during the period of No Child Left Behind (NCLB) and, to a more limited degree, in other countries. As such, the analysis provides an opportunity to learn from what we know from recent history and derives lessons for the future. These lessons will be important as we move into the era of the Common Core Standards in the United States and movements to shift conceptions of teaching and learning across the globe. We draw primarily on empirical studies of data use that have been published in the past decade, most of which have been conducted as data-driven decision making came into more widespread use. The majority of sources we cite are refereed journal articles; we include a smaller number of empirical research reports, books, and book chapters. Although the list of sources we include is not exhaustive, we believe the included works highlight the important themes in this literature.
The article reviews research on the kinds of assessment data teachers use to inform instruction, how teachers analyze data, and how their instruction is impacted. In our analysis, we identified the following three patterns. First, in the current accountability context, benchmark assessment data predominate in teachers work with data. Because teachers have been asked to administer and analyze benchmark assessments, it is not surprising that benchmark assessments are most associated with data use. Second, although teachers are often asked to analyze data in a consistent way, agendas for data use, the nature of the assessments, and teacher beliefs all come into play, leading to variability in how they use data. Third, instructional changes on the basis of data often focus on struggling students, raising some equity concerns. We review some of the factors that influence teachers use of data, arguing that leadership, structural, and cultural supports are important. We also find that the general absence of professional development has hampered teachers efforts to use data, as well as their confidence in doing so. We close with a discussion of gaps in the literature, provide suggestions for further research, and raise questions for the future of policy and practice.
THE PREDOMINANCE OF BENCHMARK ASSESSMENT DATA
Teachers have always relied on numerous forms of assessment data to guide their instruction (Young & Kim, 2010). Within the data use movement, much of the focus has been on teachers use of benchmark assessment data. Most districts engaged in data use have adopted or developed these assessments in recent years and have asked teachers to analyze and act upon the data from them (Datnow & Park, 2014; Hamilton et al., 2009; U.S. Department of Education, 2010). However, the data that arise from these assessments are limited, and thus teachers draw upon other forms of assessment data as well as they plan for instruction, including data from curriculum embedded assessments, teacher-generated assessments, and other forms of assessment (Hamilton et al., 2009; Supovitz & Klein, 2003; Wayman & Stringfield, 2006).
Interim benchmark assessments are defined as those that evaluate student knowledge and skills in a limited time frame and can be easily aggregated across schools and classrooms (Perie, Marion, Gong, & Wurtzel, 2007). The frequency of interim assessments, which are typically administered three times a year or more, is intended to enable teachers to track current students progress toward standards (Hamilton et al., 2009). Such assessments are formalized and designed to give information to educators and policy makers (Andrade, Huff, & Brooke, 2012). In the United States, many districts adopted these assessments in the past decade to help track students achievement toward standards measured on state high-stakes accountability tests. The results of interim assessments are typically made available in electronic formats, and score reports can often be generated that present data in ways that are intended for classroom teachers use. For example, reports often identify students who are meeting particular standards, students who are on the cusp of doing so, and those who fall significantly below the cut points for proficiency.
Interim assessments are distinctive from summative assessments, such as the end-of-year state assessments that have accompanied NCLB (Andrade et al., 2012). Interim assessments are also distinctive from the ongoing minute-by-minute, day-by-day classroom assessments administered by teachers in the course of teaching and learning activities, which are considered to be formative assessments (Andrade et al., 2012; Bulkley, Nabors Oláh, & Blanc, 2010). However, there is an assumption that benchmark assessments will be used in a formative way (Young & Kim, 2010). It is important to point out that there is a current debate involving the definition of formative assessment as to whether it is an instrument or a process (Bennett, 2011). Bennett (2011) argued that this is an oversimplification, and in fact formative assessment might be best conceived as neither a test nor a process, but some thoughtful integration of process and purposefully designed methodology or instrumentation (p. 7). He explained that the use of assessments in support of learning is not limited to a certain kind of assessment (e.g., summative or formative) but that more than one type of assessment could contribute to teachers judgments of student achievement. These distinctions are useful because it helps us consider what data teachers are using and for what purposes.
Bulkley and colleagues (2010, p. 117) described interim assessments as occupying an unchartered middle ground between formative assessments and summative assessments. They explained that the multiple-choice or short-answer formats of many interim assessments resemble state tests, but they have different goals. They are intended to help predict performance on end-of-year state tests and also to provide information on students strengths and weaknesses. They are used to examine how well students have mastered curriculum content by a particular point in the year so that teachers can adjust instruction accordingly. For example, a teacher in Christman and colleagues (2009) study discussed how the benchmarks acted as checkpoints that helped ascertain his progress with the district curriculum and the students level of understanding (p. 23). In some cases, teachers reported that the use of benchmark assessments in their districts led them to also use the data from curriculum embedded assessments and teacher-generated assessments more frequently to inform their instruction (Datnow & Park, 2014).
The wide-scale adoption of interim assessments is supported by the belief that they can contribute to the process of continual school improvement (Bulkley et al., 2010). As Bulkley et al. noted, whether this occurs depends a great deal on how these data are actually used to inform decision making at the classroom, school, and district levels. For example, teachers in Nabors Oláh, Lawrence, and Rigganss (2010) study universally analyzed benchmark assessment data in math. The teachers in the study were working in the School District of Philadelphia, which had instituted interim assessments in Grades K8 in reading and math. Christman and colleagues (2009) survey of teachers in Philadelphia also found widespread and frequent use of benchmark assessment data by teachers, and the majority found them to be a useful source of information on students strengths and weaknesses. There were some limitations as well, however, as we will discuss.
Interim assessments are not necessarily used for formative purposes, nor do they have the same evidence base as formative assessments in spite of claims made by their developers (Bulkley et al. , 2010; Perie, Marion, & Gong, 2009; Shepard, 2010). Interim assessments do not occur within the context of instruction as a short quiz might, for example. Because they are provided in standardized formats, they also may not provide sufficient information on how students understand (Christman et al., 2009, p. 2). When assessments have not been specifically mapped to the content, standards, or skills being taught in the classroom, teachers find it difficult to use the inform instruction (Cosner, 2011).
Shepards (2009) literature review also noted that teachers have struggled to use interim assessment data to inform instruction. Drawing on Perie and colleagues (2009) study, Shepard (2009) suggested that the usefulness of interim assessments seems to erode in practice (p. 35). If teachers do not know how to address students conceptual problems identified by the data, and if they are unable to adjust their practice accordingly, then the information derived from the assessments is of little use (Heritage, Kim, Vendlinski, & Herman, 2009). If we are to truly understand the teachers use of data, Shepard (2009) argued that we need validity frameworks to evaluate assessment applications.
Although benchmark assessments are a feature of many teachers work in the current accountability context, it is clear that teachers are administering a wide array of assessments (Datnow & Park, 2014; Hoover & Abrams, 2013). What is less clear is how their instructional planning is informed by these sources of assessment data. Hoover and Abrams (2013) conducted a large survey of teachers in Virginia to ascertain which kinds of assessment data they used and how they analyzed the data. Teachers used data from a wide variety of assessments, including teacher-generated assessments, departmental common assessments, benchmark assessments, and norm-referenced assessments. Although they administered assessments frequently, teachers did not analyze data with nearly the same frequency. They also did not analyze the data with much depth, focusing mainly on measures of central tendency. Teachers seldom disaggregated data, which may have produced a more fine-grained and useful analysis. The authors concluded, Much of the information that could be used to support learning and instructional practice is left untapped (p. 227).
Apart from the assessments themselves, the literature also reveals that if teachers are going to use assessments in meaningful ways to improve instruction, they will need new knowledge and skills (Mandinach & Gummer, 2013). Teachers must have the skills to analyze classroom questions, test items, and performance assessment tasks to ascertain the specific knowledge and thinking skills required for students to do them, among other things (Brookhart, 2011, p. 7). Teachers also need to understand the purposes and uses of the range of available assessment options and must be skilled in translating them into improved instructional strategies. These skills are particularly important as we move toward new goals for teaching and learning. With the implementation of the Common Core Standards in most U.S. states, some districts have abandoned their former interim assessments and are now looking to new ways to assess students. Organizations such as the Smarter Balanced Consortium have designed new interim assessments linked to the standards. At the same time that assessments continue to be generated by external organizations, Andrade et al. (2012) argued that interim assessments that are created by teams of teachers and aligned to curricular content at the school level could be effective in helping teachers share practices. They noted that the utility of such assessments would be further improved if students could be involved in analyzing the results and making plans to address their own learning needs. Similarly, Schnellert, Butler, and Higginson (2008) found when teachers co-constructed assessments, set context-specific goals themselves, and were engaged as partners in the accountability system, more meaningful instructional changes occurred.
Informal formative assessments in which students thinking is made explicit in the course of instructional dialogue are another promising avenue (Ruiz-Primo, 2011). Ruiz-Primo (2011) explained how students questions and responses become material for stimulating assessment conversations in which teachers are able to collect information (orally or from written work) on students thinking and make instructional decisions accordingly. Based on their inferences of students understanding, teachers are able to move students toward specific learning goals. This embedded process of assessment allows teachers to give the kind of feedback that addresses not only students cognitive demands but also their motivational and affective needs as well.
CONSISTENCIES AND VARIATIONS IN TEACHERS ANALYSIS AND USE OF DATA
The process by which assessment data are used to inform instructional improvement is often described as involving numerous steps. These steps include: (1) accessing and organizing data, (2) making sense of data to identify problems and solutions, (3) trying the interventions, and (4) assessing and modifying the interventions (Blanc et al., 2010). These steps are often depicted in a circular fashion to represent this process as an ongoing cycle. For example, the teachers in Nabor Oláh and colleagues (2010) study were all comfortable logging into the districts data management system to access results from benchmark assessment in math. They engaged in a process of identifying weak points for the class as a whole or for individual students. The teachers then went through a process of validating the data to ensure that they accurately reflected students understanding. They also considered the instructional context, such as whether they had covered particular topics in depth and the districts pacing plan. In other words, the majority of teachers moved from analyzing the data and linking this analysis to curricular content, which was the districts expectation. This pattern is common in other studies as well (e.g., Datnow & Park, 2014; Hamilton et al., 2009).
In spite of these consistent patterns, Nabors Oláh and colleagues’ (2010) study also highlighted the importance of delving deeper into how teacher judgment and intuition play into the analysis of data to understand the variations that exist among teachers. Few studies explore how teachers analyze the data, much less how they enact instructional changes on the basis of them. Nabors Oláh and colleagues explained how teachers’ interpretations of data were influenced by their personal thresholds for mastery of the subject matter. These thresholds were influenced by their knowledge of the students and their beliefs about teaching mathematics. As the authors noted, these thresholds served as a critical link between interpretation and action in the formative assessment cycle (p. 235). For the teachers in this study, the demonstration of mastery could fall anywhere between 60% and 80%, and it varied depending on the student, the class composition, and the districts curriculum and pacing schedule. That teachers took all these issues into account, even in the presence of defined cut scores for mastery from the district, suggests a rather complex process of data analysis that involved much more than the assessment data on the page. Part of this process was a diagnosis of student mistakes (i.e., was it procedural, conceptual, and reflective of other cognitive weaknesses, and did it take into account other external influences?). By and large, the diagnosis focused on procedural mistakes because the items on the assessments did not allow for much diagnosis of conceptual misunderstandings.
As we alluded to earlier, teachers use of data is clearly influenced by the nature of the assessment. Davidson and Frohbieter (2011) found that multiple-choice assessments motivated actions that often resulted in class placement decisions. In contrast, assessments that required more constructivist responses generated dialogue and collaboration among teachers and created opportunities for shared understandings of assessment purposes and use. As Christman et al. (2009) explained, because questions on the Philadelphia districts interim assessments were not open ended, they failed to provide information on why student confusion might exist or reveal the students missteps in solving a problem. As they argued, If the items operate only at the lower levels of cognition (e.g., knowledge and comprehension), and do not tap into analytical thinking, they are not good tests of conceptual proficiency (p. 29). Teachers then struggled to make use of the data that emerged as they planned for instruction.
Cosners (2011) qualitative study of teacher teams use of literacy assessment data also suggests that assessments that have not been specifically mapped to content-knowledge, skills or standards may prove more challenging for the generation of student learning knowledge by teachers (p. 582). In the first year of a data use effort, teachers in Cosners study focused on identifying broad patterns of achievement in their classes rather than student learning needs. By the third year of their work with data, several teacher teams made connections between assessment results and the skills and content they had taught; however, considerations of past instructional efficacy were slow to develop.
How closely data are connected to the classroom affects teachers ability to make sense of them in ways that are useful for their practice. Teachers in Kerr and colleagues (2006) study found reviewing student work to be more useful in guiding instruction than the results from benchmark assessments or state tests, though they drew on all of them to some extent. Classroom assessments and reviews of student work were deemed more meaningful and valid. Teachers viewed state tests as not very useful because they came too late in the year and were judged to be less relevant to classroom practice. This is not surprising, given findings from other studies showing that the data from large-scale assessments may be useful for school and system planning, but they are less useful at the teacher or student level (Rogosa, 2005; Supovitz, 2009). Similarly, in Schildkamp and Kuipers (2010) qualitative study of Dutch teachers and school leaders, teachers focused on data that would help them meet the needs of learners (e.g., data from examinations they administered) rather than the more broad school-level assessment data that preoccupied the leaders. These data yielded more helpful information for making instructional decisions. Data that compared schools with national results were of less interest to teachers.
Explaining variations in teachers data use and the types of data they draw on calls for a focus on the interactions among educators and their responses to the situations they are in. These processes are driven by how teachers and districts frame the purpose of data, as well as the limits of the assessments themselves. Shepard, Davidson, and Bowmans (2011) study of middle school teachers in six districts found that teachers believed districts implemented benchmark assessments for two primary reasons: to provide data on mastery toward the standards and to prepare for state tests. It is therefore not surprising that teachers examination of benchmark assessment data focused primarily on mastery of the standards and test taking and procedural insights, and much less on substantive insights. In a study of high school teachers in two schools, Park (2008) found that the relationship between teachers perceptions and data use were tightly connected. Although most teachers perceived data use as an important and necessary tool for improving classroom practice, the majority of teachers either used data to address accountability demands or viewed data-driven decision making as a bureaucratic task to be completed.
In a recent cross-case comparison study of two elementary schools, Moriarty (2013) found that teachers who had more autonomy . . . felt more ownership in their work and had more capacity to engage in data-driven practices (p. 161). Although both schools were located in the same district, principal leadership accounted for some teachers having less power in making instructional decisions and using the type of data they felt would inform their work. The result was that teachers felt less ownership and were less likely to view data as valuable in informing their practice.
The broader policy environment also influences teachers’ sense making about data use (Spillane, 2012). Teachers in Blanc and colleagues’ (2010) study often centered on what was needed for the school to meet Adequate Yearly Progress (AYP) as part of No Child Left Behind. The belief was that the benchmark assessment data were predictive of student performance on the annual high-stakes assessment. Teachers focused data use discussions on test-taking strategies, underscoring the link between the interim assessments and the state assessments. However, as Christman and colleagues (2009) study noted, the benchmarks in Philadelphia were not intended to be predictive, and that practitioners erroneously viewed them this way proved to be a distraction from their intended use for strengthening instructional capacity.
Challenges also arose for some teachers in Heppen and colleagues (2012) study designed to examine teachers use of interim assessment data. Some teachers complained that there was a misalignment among district pacing guides, curriculum and state assessments, and their instructional programs. Teachers were also concerned about the validity of interim assessment data and were suspicious about the intent and expectations of the district in using interim assessment data, particularly as it related to accountability. They preferred instead to rely on quizzes and unit tests. A lack of communication about district goals for the use of interim assessments exacerbated the problems, causing some teachers to adopt practices that the district did not support. For example, cut scores were used for high-stakes promotion and placement decisions, which was not intended.
Jenningss (2012) review of literature also draws our attention to the ways in which accountability models may shape the use of data to inform instruction. In a status or proficiency model, there is an incentive for teachers to move as many students over the cut score as possible but need not attend to the average growth in their class (Jennings, 2012, p. 11). In the case of a growth model, there is an incentive for teachers to focus on students who they believe have the greatest potential for growth. This strategy obviously presents consequences for students who are not the recipients of the interventions.
In sum, there are both consistencies and variations in how teachers use data. Teachers have been asked to engage in a fairly common process of analyzing data to inform instruction. It seems that many teachers are familiar with and have engaged in this process designed to inform continuous improvement. However, when we dig deeper, we see some difficulties along the way. A consistency we find in the literature is that benchmark assessments that include only closed-ended response items, such as multiple-choice questions, are limited in informing teachers about students conceptual understanding. There is also significant variability in how teachers engage in the process of data use, depending on the context for data use, teachers views of the purpose of the assessment, and their beliefs about its utility. In some cases, the results from benchmark assessments are being used in unintended ways. These dynamics, not surprisingly, have consequences for the instructional changes we see.
INSTRUCTIONAL CHANGES RESULTING FROM TEACHERS USE OF DATA
In spite of the enthusiasm in the policy world for data-driven instruction, only small number of studies speak to the instructional changes that teachers make as a result of their analysis of assessment data (e.g., Blanc et al., 2010; Christman et al., 2009; Cosner, 2011; Davidson & Frohbieter, 2011; Datnow & Park, 2014; Hoover & Abrams, 2013; Nabors Oláh et al., 2010; Pierce & Chick, 2011). These studies provide some information on the types of instructional adjustments teachers make, their teaching strategies, and when such activities were implemented. These studies primarily report on what teachers report doing, rather than documenting what teachers actually do by examining classroom practice in depth.
Hoover and Abrams (2013) argued that although the teachers they surveyed did not analyze data frequently or with the depth required to obtain the full benefits, most of the teachers in the study reported making instructional changes on the basis of assessment data. A total of 96% of teachers reported differentiating instruction for remediation, 94% reported reteaching, and 92% changed the pace of future instruction. At the same time, 64% of teachers said that the pacing prevented reteaching, which appears inconsistent with the fact that most of them reported reteaching. More information is needed on the degree to which instructional planning is informed by assessment data and what kinds of instructional changes result.
Numerous qualitative studies give some insight into teachers instructional decision making on the basis of data. In theory, teachers instructional planning would be aimed at improving student learning. Some studies have found that teachers joint instructional planning centered less on how on to address students conceptual understanding and more on how to motivate students to improve testing performance (Blanc et al., 2010; Nabors Oláh et al., 2010). A common pattern is to devote instructional planning time to identifying students of concern and planning remediation or review (Blanc et al., 2010; Cosner, 2011; Christman et al., 2009; Nabors Oláh et al., 2010; Shepard et al., 2011). For example, teachers in Blanc et al.s (2010) study spent some of their benchmark assessment data analysis time focused on what they called strategic sensemaking, which involved focusing on students who were on the bubble or just below the target of performance (p. 212). Christman and colleagues (2009) study corroborated this finding, noting that identifying students on the bubble was a uniform practice in data discussions, and interventions were planned accordingly. As one teacher noted, The teachers put stars next to those kids they were going to target. And we made sure those kids had interventions, from Saturday school to extended day, to Read 180. And then we followed their benchmark data (p. 50).
Christman et al. (2009) found that the designated reteaching weeks in Philadelphia were helpful in providing time set aside to address issues arising from the benchmark assessment data. During the designated reteaching weeks, teachers in Nabors Oláh and colleagues (2010) study targeted instructional changes in two main areas: students who were lowest performing and content areas that proved challenging for a large number of students. Reteaching happened either in small- or whole-group instructional settings, rarely 1:1, and usually involved presenting material in a different way, such as through visual aids or manipulatives. High-scoring students received less direct instruction during instructional time allocated for reteaching. Some teachers also admitted that they didnt actually use the reteaching time for addressing instructional needs arising from the assessments. Rather, they used the time to catch up with the district pacing guide.
Administrators in Philadelphia imagined that after the reteaching week, teachers would again check for mastery using their own assessments (Christman et al., 2009). Although assessing the results of re-teaching is an essential part of determining whether interventions have been successful, this seldom occurred, representing a fundamental disjuncture in the interim assessment process (Christman et al., 2009, p. 29). If teachers did not assess whether the instructional changes were positively influencing student achievement, then the process of continuous improvement was compromised.
Pierce and Chicks (2011) study also reported limited instructional changes on the basis of data. The authors examined teachers use of data to inform instruction in Australia, which administers national tests in numeracy and literacy and whose government also promotes data use. Pierce and Chicks survey found that 61% of teachers reported that they had not made changes to their teaching plans as a result of using data. Thirty-nine percent of teachers had made changes, mostly modifications for weaker students but also for some stronger students. For example, addressing the needs of weaker students, one teacher described the instructional change he/she made as a result of examining the data: Realized remedial Year 7 group was weaker than originally thought. Brought in much more concrete tasks (p. 445). The survey allowed for only brief details of instructional changes, thus, more information is needed to gain a more comprehensive understanding of changes in teacher practice.
A small body of research speaks to the intersection of data use and grouping students for instruction. Although no studies were found to have a specific focus on this, some findings have emerged in broader studies. Teachers in Shepard et al.s (2011) study frequently used grouping to differentiate instruction. Teachers in Datnow and Parks (2014) qualitative study used weekly assessments to group students of concern for reteaching in math and English during a set aside time. These groupings were flexible and changed weekly. Teachers in Nabors Oláh and colleagues study (2010) also used pull-out, small-group instruction in math during a designated reteaching week and before school. In other cases, teachers used data to arrange heterogeneous grouping and peer teaching.
Hoover and Abramss (2013) study also reported that teachers regrouped for instruction on the basis of data: Elementary (97%) teachers were more likely to report using data to make changes to student groups than were middle (81%) or high school (66%) teachers, albeit large percentages of teachers in all three groups did indicate using assessment data for this purpose (p. 226). However, because this was a self-report survey of teachers, we lack information on how teachers used data to make these decisions and on whether these groupings were fixed or dynamic.
As noted earlier, Davidson and Frohbieter (2011) found that when districts administered interim assessments that included only multiple choice data, this had the unintended consequence of leading to data use for the purpose of tracking decisions, grouping, or placing students into various classes. Shepard et al.s (2011) related study found similar patterns of the use of test scores to determine placements, as did Heppen et al.s (2012). These instructional trends about the overlap between assessment use and ability grouping are noteworthy in light of a recent report that found that ability grouping in the upper elementary grades in reading and math grew considerably in the past 20 years (Loveless, 2013). The percentage of fourth-grade teachers reporting ability grouping in reading grew from 28% in 1998 to 71% in 2009, and ability grouping in math grew from 40% to 61% during a similar period. Loveless (2013) posited that this significant rise in ability grouping may be a consequence of NCLB and the emphasis on data because they focus educators attention on students below the threshold for proficiency on state tests and provide justification for grouping struggling students (p. 20). It is unclear what exactly teachers mean by ability grouping, how it is arranged in the classroom, for how long, and for what purposes. It is also unclear how exactly accountability data, in the form of state, district, and schoolwide assessments, inform and shape this instructional decision-making process.
FACTORS THAT INFLUENCE TEACHERS USE OF DATA
As the preceding discussion suggests, teachers are embedded in multiple organizational contexts that influence, direct, and guide how they conceptualize and implement data use for instructional decision making. Compared with the areas already discussed, a great deal has been written about the factors that influence teachers data use, including the role of leadership and the structures and cultures that support the use of data in schools and districts. Thus, we restrict our discussion here to the most critical topics that arose in the studies we reviewed. The studies report on both facilitators of teachers use of data as well as inhibitors.
School principals and teacher leaders are key players in facilitating data use among teachers (Blanc et al., 2010; Cosner, 2011; Earl & Katz, 2006; Halverson, Grigg, Pritchett, & Thomas, 2007; Ikemoto & Marsh, 2007; Mandinach & Honey, 2008; Marsh, 2012; Park, Daly, & Guerra, 2012) because they help to set the tone for data use among teachers and provide support at the school level. For example, principals in Halverson and colleagues (2007) study adapted policies and practices to structure social interaction and professional discourse on data use in their schools. District leaders also play a critical role in framing the purpose of data use and setting the direction for data use practices (Park et al., 2012). At both levels, a productive role for the leader is to guide staff in using data in thoughtful ways that inform action rather than promoting the idea that data in and of themselves drive action (Datnow & Park, 2014; Knapp, Copland, & Swinnerton, 2007).
In actuality, there is a range of ways in which leaders scaffold data use, some more generative of continuous improvement than others. Some leaders promote an accountability-focused culture in which data are used in a short time frame to identify problems and monitor compliance (Firestone & González, 2007). In such environments, sanctions and remediation are chief tactics, and increased test scores are the primary outcome of improvement efforts. In contrast, when the culture of a district supports organizational learning, data use is more conducive to educational improvement (Firestone & González, 2007; Wayman & Cho, 2008). Without a fear of punishment, educators working in cultures focused on organizational learning can go beyond simply identifying a problem and work to understand the nature of the problem.
Diamond and Coopers (2007) study of Chicago principals and teachers also found that data use among educators depended on a schools relationship with the accountability regime (p. 242). Elementary schools placed on probation and trying to avoid sanctions were less intent on transforming the whole school. Under the pressure to perform, principals in Diamond and Coopers study used standardized test score data to identify individuals who were most likely to show the quickest gains. This leadership focus on students on the bubble (Booher-Jennings, 2005) is consistent with the teachers focus we discussed earlier. Whereas low-performing schools adopted this approach, high-achieving schools adopted an orientation to data use that focused on improving instruction for all students by changing pedagogy and increasing instructional rigor. These schools took a more holistic look at what the data told them, recognizing the importance of addressing academic weaknesses schoolwide. The majority of the students in schools under sanction were low-income and African American students, whereas students in the high-achieving schools were predominantly White. Profiles of data use across schools with different accountability cultures illustrate the kind of consequences that can influence students. Depending on the purpose and the context, data use strategies promote improvement achievement, or disadvantage groups of students and perpetuate inequality. Leadership is key in orienting data use in ways that disrupt rather than reinforce patterns of inequity.
Research also highlights how classroom, grade-level, and school organizational contexts shape data use in important ways. Providing structured time for collaboration is one of the most common ways that districts and schools attempt to support teachers use of data (Honig & Venkateswaran, 2012; Lachat & Smith, 2007; Marsh, 2012; Means, Padilla, & Gallagher, 2010). Strong instructional communities in which to analyze data can assist teachers in using data in productive ways (Blanc et al., 2010; Cosner, 2011; Datnow, Park, & Kennedy-Lewis, 2013; White & Anderson, 2011). For example, White and Andersons (2011) study of Australian math teachers found that when professional learning opportunities were arranged so that teacher could dialogue around data and strategize pedagogy, teachers instruction improved, and student achievement improved as well.
However, studies also have found that grade-level agendas, norms, and the level of expertise in the group all play into teacher collaboration around data use (Horn & Little, 2010; Young, 2006). Consequently, teacher teams with limited expertise can misinterpret or misuse data, or they can work together to perpetuate poor classroom practice (Daly, 2012). Various tools for supporting teachers use of data, including district protocols for analyzing data and reflecting on data, also assist in the process of data use in some cases (Christman et al., 2009) but prove constraining in others (Datnow et al., 2013). The variance in the quality of conversations can impact student learning, as Timperley (2009) found. In her study of schools in New Zealand, conversations in which there was a sense of urgency to address student progress and in which multiple sources of data were brought to bear were more generative than those in which the purpose of data use was not clearly defined (Timperley, 2009).
A school or districts context for reform also shapes teachers use of data. Hubbard, Datnow, and Pruyn (2013) found that requirements to implement multiple other educational initiatives at the school created challenges that hampered teachers ability and motivation to fully integrate data use into their daily practice. How and when teachers used data were determined by the interaction of multiple factors, including a broad set of policies and structures in place at the federal, district, and school levels, as well as the capacity of the teachers to engage in data-driven practice.
TEACHER CAPACITY FOR DATA USE
Several important factors related to data use exist at the level of the teacher capacity. As we discussed briefly, teachers need a range of knowledge to make sense of and use assessment data effectively. A national survey found that 43% of teachers surveyed received some training on how to analyze data from state and benchmark assessments, though they did not find it adequate (Means, Padilla, DeBarger, & Bakia, 2009). Most studies have found that teachers have had little professional development to aid in their understanding of data or in their instructional planning on the basis of data (Davidson & Frohbieter, 2011; Dunn, Airola, Lo, & Garrison, 2012; Kerr et al., 2006; Mandinach & Gummer, 2013; Shepard et al., 2011; U.S. Department of Education, 2010; Wayman & Cho, 2008). Most teachers also have had little training in assessment in general, in either their preservice or in-service years (Young & Kim, 2010).
The lack of training limits teachers capacity to use data effectively. For example, Davidson and Frohbieter (2011) found that a lack of teacher professional development and support contributed to a failed data use initiative even though district administrators pinned the failure on the teachers lack of interest in change. In their study of three urban school districts, Kerr et al. (2006) reported that training for teachers with regard to data analysis and interpretation was an important factor in teachers ability to use data because capacity gaps were highly visible. The more successful districts in Kerr and colleagues study were the ones that offered professional development to teachers in data analysis.
Absent sufficient training, teachers lack confidence in their ability to use data to improve instruction. In Pierce and Chicks (2011) study, teachers felt handicapped in making sense of the data they were provided. Teachers found the reports difficult to understand, and they lacked confidence in dealing with statistical data. Dunn and colleagues (2012) survey of over 1,700 teachers found that teachers lack of efficacy in using data and their anxiety about the process limited their ability to use data effectively. An important finding that emerged in this study was that teachers viewed the ability to analyze and interpret data as distinct from the ability to use the data to inform instructional practice. The authors noted that, just as we have found, there is scant research information on the latter.
Data use is also shaped by teachers beliefs about assessment, teaching, and learning. Jimersons (2014) study of a school district in central Texas revealed that teachers understanding of data and data use was specifically tied to their mental models, which vary along a number of dimensions. Brown, Lake, and Matters (2010) multigroup analysis of teachers in Australia found that although conceptions about assessment among primary and lower secondary teachers were similar in that they were not anti-assessment, their beliefs and uses of assessment were statistically different. Primary teachers agreed more than secondary teachers that assessment improves teaching and learning, while the latter agreed more that it makes students accountable (p. 210). Differences were related to beliefs that mediated policy and outcomes. Teachers also were willing to take professional responsibility for improving school outcomes while rejecting the notion that assessment should focus on students (p. 218). This rejection stemmed from concerns regarding the quality and usefulness of the assessment resources being used to make students and schools accountable (p. 218). Assessment was viewed as irrelevant if the data were punitive for children or if the validity of the data was called into question. Teachers viewed poor quality assessments as having unjust consequences for learners.
Along similar lines, Remesals (2011) qualitative study of 50 primary and secondary mathematics teachers in Spain found that four different belief categories built teachers conceptions about assessment: teaching, as separate from the influence of assessment on learning, and accountability, as separate from accreditation of achievement. These beliefs caused teachers to identify assessment as either a positive change or a disruptive measure. Teachers who held a pedagogical orientation toward assessment (typically primary teachers) viewed assessment as a valuable tool to monitor and support student learning, as an instrument for quality control, and as a way to know if students had indeed learned and reflected on their lessons. Conversely, high school teachers were more likely to hold a societal conception, viewing assessment as a benchmark, a reference point for the establishment of minimum levels of expected performance (p. 477). The majority of the teachers, however, reported mixed conceptions of assessment. According to the author, this variation in beliefs indicates that the nature of assessment is complex: all purposes of assessment (for/of learning, accreditation/accountability) [are] part of the whole system affecting the daily classroom practices [and they are in a] continuous and inevitable tension (p. 479).
Importantly, Remesal (2011) reminded us of the importance of context in understanding teachers beliefs about assessment. She argued that the higher incidence of societal conceptions among high school teachers was likely related to Spains new educational system, and the external assessment policy demands that it placed on teachers (the move from a basic plan of education to a compulsory education for secondary students) meant that students promotion decisions and career paths (college or vocational education) were heavily reliant on assessment data. Increased pressure to report quantitative data caused teachers to view assessment as an instrument of one-way communication with families and studentsa way to report on students progress but also a process that was viewed as disassociated from their teaching duties (p. 477).
Teachers beliefs play out in other ways that affect data use. The absence of teacher buy-in was found to limit Dutch teachers use of data in Schildkamp and Kuipers (2010) study. Lack of buy-in was associated with teachers belief in an external locus of control. In other words, according to one teacher, students achievement could be understood by whether you had a year of good students or not so good students (p. 488). Having data would thus not be helpful. So, too, some teachers in Pierce and Chicks (2011) study felt that the data reported to them merely indicated what they already knew about students.
The myriad factors influencing teachers use of data remind us of the interdependency among the classroom, school, and district levels (Hamilton et al., 2009). The general lack of capacity many teachers feel regarding data use, as well as their beliefs about the utility of assessments, figures strongly into their efforts to use data to inform instruction.
This review of research reveals numerous lessons from the past and for the future. Given that interim benchmark assessment data predominate in teachers work with data, we will need to think more deeply about the content of those assessments as well as how we can create conditions for teachers to use assessment to inform instruction. This is especially important given that we know that the assessments themselves, combined with teachers beliefs and their levels of support, influence their decisions about instruction. Up to now, instructional changes on the basis of data use have focused primarily struggling students, which raises concerns about whether instructional differentiation is meeting the needs of all students. Although we have a great deal of knowledge of the structural supports for data use, one striking pattern is the fact that teachers feel underprepared to use data effectively, which has undermined their confidence and their efforts.
This review of research reveals gaps in significant areas. First, there is a need for much more research on how teachers instruction is informed by the use of data. Few studies have focused in depth on classroom instruction, and thus we know little about what data-informed instruction looks like, particularly with respect to instructional differentiation, which is often a goal of data use efforts. Second, there is a need for further research on the activity settings in which teachers use data to inform instruction. Such research could focus on both the individual ways in which teachers use data and how they work together in groups to analyze and act on data.
Understanding data use for instructional decision making requires close investigation into how educators engage with data so that we can find out why it fosters positive outcomes in some places and not others (Coburn & Turner, 2012). For example, studies have found that the use of data from interim assessments has a positive impact on achievement in some grades but not others (Konstantopoulos, Miller, & van der Ploeg, 2013) and in some content areas and not others (Carlson, Borman, & Robinson, 2011). To understand the source of these variations, we need to take a deep research dive into teacher practice. As Little (2012) argued, we need more studies that either zoom in on teachers daily and weekly activities around data or zoom out and focus on how data use fits in within a larger context of teachers work.
As schools in most states shift to the Common Core Standards and with the push for 21st-century learning in many countries, lessons from the recent decade of data use prompt us to ask several important questions and emphasize significant areas for additional study. Common Core Standards demand that teachers engage in a new kind of teachingone that is focused on supporting students to analyze intricate arguments, compare texts, synthesize complex information, explain problem solving, and dig more deeply than ever into the nature of evidence. What counts as data will be more wide ranging.
We are now asking teachers not only to use data to inform decision making but also to use more complex forms of data and to implement new instructional strategies to respond to students needs. These initiatives will likely involve activities such as inquiry-based learning, collaboration, and discussion, which are not easily measured by traditional assessments (Levin, Datnow, & Carrier, 2012). There are some promising signs given that the new assessments are intended to provide a fuller picture of student understanding than the prior ones that have been in use in the NCLB era. As teachers turn to data to provide instructional guidance, it will be important to investigate how these new assessments facilitate teaching and learning.
Given the findings from the research reviewed here, it is essential that teachers receive professional development on assessment and how to translate assessment data into information that can inform instructional planning. Teachers need to be provided with a rich understanding of assessment. They also need support in accessing data, translating them, and disaggregating them in ways that will support goals of equity and excellence for all students (Hoover & Abrams, 2013; Marsh, 2012; Young & Kim, 2010). Finally, the need for support in planning instructional changes on the basis of data, particularly with respect to differentiation. There have been calls for reform in teacher education to place a much higher priority on developing these skills in teachers preservice years (Mandinach & Gummer, 2013; Mandinach, Friedman, & Gummer, 2015, this issue; Young & Kim, 2010). With respect to in-service training, we need to know how to support teachers varied needs with respect to data literacy preparation (Mandinach & Gummer, 2013). District and school administrators are challenged to configure new ways of improving communication with teachers and providing them with the skills and knowledge to take advantage of the growing availability of technologies and cutting-edge pedagogies that best support teaching and learning.
As students are asked to monitor their own progress and to design learning strategies to boost their individual achievement, they too will need to learn how to become reflective learners and gain the capacity to examine data. A closer examination of how this process unfolds will be critical. And finally, as past experience with data use has also shown, a schools accountability context has shaped what data are used, how they are used, and for whom. When stakes are high, perverse incentives come into play that may work against improving teaching and learning for all students. There is no evidence that high-stakes accountability will diminish, causing us to ask: Will new assessments generate more substantive information about students needs and result in greater attention given by teachers to scaffold learning for all students? Enabling teachers to use data in ways that improve instruction will require systemic support.
Andrade, H., Huff, K., & Brooke, G. (2012). Assessing learning: The students at the center series. Retrieved from http://studentsatthecenter.org/topics/assessing-learning
Bennett, R. E. (2011). Formative assessment: A critical review. Assessment in education: Principles, Policy, and Practice, 18(1), 525.
Bernhardt, V. (1998). Multiple measures. Invited Monograph No. 4. Oroville: California Association for Supervision and Curriculum Development.
Blanc, S., Christman, J. B., Liu, R., Mitchell, C., Travers, E., & Bulkley, K. E. (2010). Learning to learn from data: Benchmarks and instructional communities. Peabody Journal of Education, 85(2), 205225.
Booher-Jennings, J. (2005). Below the bubble: Educational triage and the Texas accountability system. American Educational Research Journal, 42(2), 231268.
Brookhart, S. M. (2011). Educational assessment knowledge and skills for teachers. Educational Measurement: Issues and Practice, 30(1), 312.
Brown, G. T., Lake, R., & Matters, G. (2011). Queensland teachers conceptions of assessment: The impact of policy priorities on teacher attitudes. Teaching and Teacher Education, 27(1), 210220.
Bulkley, K. E., Nabors Oláh, L. N., & Blanc, S. (2010). Introduction to the special issue on benchmarks for success? Interim assessments as a strategy for educational improvement. Peabody Journal of Education, 85(2), 115124.
Carlson D., Borman G. D., & Robinson M. (2011). A multi-state district-level cluster randomized trial of the impact of data-driven reform on reading and mathematics achievement. Educational Evaluation and Policy Analysis, 33, 378398.
Christman, J. B., Neild, R. C., Bulkley, K., Blanc, S., Liu, R., Mitchell, C., & Travers, E. (2009). Making the most of interim assessment data. Lessons from Philadelphia. Retrieved from http://www.researchforaction.org/wp-content/uploads/publication-photos/41/Christman_J_Making_the_Most_of_Interim_Assessment_Data.pdf
Coburn, C. E., & Turner, E. O. (2012). The practice of data use: An introduction. American Journal of Education, 118(2), 99111.
Cosner, S. (2011). Teacher learning, instructional considerations and principal communication: Lessons from a longitudinal study of collaborative data use by teachers. Educational Management Administration & Leadership, 39(5), 568589.
Daly, A. J. (2012). Data, dyads, and dynamics: Exploring data use and social networks in educational improvement. Teachers College Record, 114(11), 138.
Data Quality Campaign. (2011). Data: The missing piece to improving student achievement. Washington, DC: Author. Retrieved from http://www.dataqualitycampaign.org/files/dqc_ipdf.pdf
Datnow, A., & Park, V. (2014). Data-driven leadership. San Francisco, CA: Jossey-Bass.
Datnow, A., Park, V., & Kennedy-Lewis, B. (2013). Affordances and constraints in the context of teacher collaboration for the purpose of data use. Journal of Educational Administration, 51(3), 341362.
Davidson, K. L., & Frohbieter, G. (2011). District adoption and implementation of interim and benchmark assessments (Report No. 806). Los Angeles, CA: National Center for Research on Evaluation, Standards, and Student Testing (CRESST).
Diamond, J. B., & Cooper, K. (2007). The uses of testing data in urban elementary schools: Some lessons from Chicago. National Society for the Study of Education Yearbook, 106(1), 241263.
Dunn, K. E., Airola, D. T., Lo, W., & Garrison, M. (2012). What teachers think about what they can do with data: Development and validation of the data-driven decision making efficacy and anxiety inventory. Contemporary Educational Psychology, 38, 8798.
Earl, L., & Katz, S. (2006), Leading schools in a data-rich world. Thousand Oaks, CA: Corwin Press.
Firestone, W. A., & González, R. A. (2007). Culture and processes affecting data use in school districts. In P. A. Moss (Ed.), Evidence and decision making: Yearbook of the National Society for the Study of Education (pp. 132154). Malden, MA: Blackwell.
Halverson, R., Grigg, J., Pritchett, R., & Thomas, C. (2007). The new instructional leadership: Creating data-driven instructional systems in schools. Journal of School Leadership, 17(2), 159193.
Hamilton, L., Halverson, R., Jackson, S. S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). IES Practice Guide: Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance.
Heppen, J., Jones, W., Faria, A., Sawyer, K., Lewis, S., Horwitz, A. & Casserly, M. (2012). Using data to improve instruction in the Great City Schools: Documenting current practice. Washington, DC: American Institutes for Research and The Council of Great City Schools.
Heritage, M., Kim, J., Vendlinski, T., & Herman, J. (2009). From evidence to action: A seamless process in formative assessment? Educational Measurement: Issues and Practice, 28(3), 2431.
Honig, M. I., & Venkateswaran, N. (2012). Schoolcentral office relationships in evidence use: Understanding evidence use as a systems problem, American Journal of Education, 118(2), 199222.
Hoover, N. R., & Abrams, L. M. (2013). Teachers instructional use of summative student assessment data. Applied Measurement in Education, 26, 219231.
Horn, I. S., & Little, J. W. (2010). Attending to problems of practice: Routines and resources for professional learning in teachers workplace interactions. American Educational Research Journal, 47(1), 181217.
Hubbard, L., Datnow, A., & Pruyn, L. (2013). Multiple initiatives, multiple challenges: The promise and pitfalls of implementing data. International Studies in Educational Evaluation. doi:10.1016/j.stueduc.2013.10.003
Ikemoto, G. S., & Marsh, J. A. (2007). Cutting through the data driven mantra: Different conceptions of data-driven decision making. National Society for the Study of Education Yearbook, 106(1), 105131.
Jennings, J. L. (2012). The effects of accountability system design on teachers use of test score data. Teachers College Record, 114(11), 1-23.
Jimerson, J. B. (2014). Thinking about data: Exploring the development of mental models for data use among teachers and school leaders. Studies in Educational Evaluation, 42, 514.
Kennedy, M. M. (2011). Data use by teachers: Productive improvement or panacea? (Working Paper No. 19). East Lansing: Education Policy Center at Michigan State University.
Kerr, K. A., Marsh, J. A., Ikemoto, G. S., Darilek, H., & Barney, H. (2006). Strategies to promote data use for instructional improvement: Actions, outcomes, and lessons from three urban districts. American Journal of Education, 112(3), 496520.
Knapp, M. S., Copland, M. A., & Swinnerton, J.A. (2007). Understanding the promise and dynamics of data-informed leadership. Yearbook of the National Society for the Study of Education, 106(1), 74104.
Konstantopoulos, S., Miller, S. R., & van der Ploeg, A. (2013). The impact of Indianas system of interim assessments on mathematics and reading achievement. Educational Evaluation and Policy Analysis, 35(4), 481499.
Lachat, M. A., & Smith, S. (2005). Practices that support data use in urban high schools. Special Issue on transforming data into knowledge: Applications of data-based decision making to improve instructional practice. Journal of Education Change for Students Placed At-Risk, 10(3), 333-339.
Levin, B., Datnow, A., & Carrier, N. (2012). Changing school district practices. Boston, MA: Jobs for the Future. Retrieved from http://www.studentsatthecenter.org/papers/changing-school-district-practices
Little, J. W. (2012). Understanding data use practices among teachers: The contribution of micro-process studies. American Journal of Education, 118(2), 143166.
Loveless, T. (2013). The 2013 Brown Center report on American education: How well are American students learning?(Vol. 3, No. 2). Washington, DC: Brookings. Retrieved from http://www.brookings.edu/2013-brown-center-report
Mandinach, E. B., Friedman, J. M., & Gummer, E. S. (2015). How can schools of education help to build educators capacity to use data? A systemic view of the issue. Teachers College Record, 117(4).
Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 3037.
Mandinach, E. B., & Honey, M. (Eds.). (2008). Data driven school improvement: Linking data and learning. New York, NY: Teachers College Press.
Marsh, J. A. (2012). Interventions promoting educators use of data: Research insights and gaps. Teachers College Record, 114(11), 148.
Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision making in schoolsteacher access, supports and use. Washington, DC: U.S Department of Education, Office of Planning, Evaluation, and Policy Development.
Means, B., Padilla, C., & Gallagher, L. (2010). Use of education data at the local level: from accountability to instructional improvement. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.
Moriarty, T. (2013). Data-driven decision-making: Teachers use of data in the classroom (Doctoral dissertation). Available from ProQuest Dissertations and Theses database. (UMI No. 3571708)
Moss, P. A. (2012). Exploring the macro-micro dynamic in data use practice. American Journal of Education, 118(2), 223232. doi:10.1086/663274
Nabors Oláh, L., Lawrence, N. R., & Riggan, M. (2010). Learning to learn from benchmark assessment data: How teachers analyze results. Peabody Journal of Education, 85, 226245.
Park, V. (2008). Beyond the numbers chase: How urban high school teachers make sense of data use (Unpublished doctoral dissertation). University of Southern California, Los Angeles.
Park, V., Daly, A. J., & Guerra, A. W. (2012). Strategic framing: How leaders craft the meaning of data use for equity and learning. Educational Policy, 27(4), 645675. doi:10.1177/0895904811429295
Perie, M., Marion, S., & Gong, B. (2009). Moving toward a comprehensive assessment system: A framework for considering interim assessments. Educational Measurement: Issues and Practice, 28(3), 513.
Perie, M., Marion, S., Gong, B., & Wurtzel, J. (2007). The role of interim assessments in a comprehensive assessment system: A policy brief. Washington, DC: Aspen Institute.
Pierce, R., & Chick, H. (2011). Teachers intentions to use national literacy and numeracy assessment data: A pilot study. Australian Educational Research, 38(3), 433477.
Remesal, A. (2011). Primary and secondary teachers conceptions of assessment: A qualitative study. Teaching and Teacher Education, 27(2), 472482.
Rogosa, D. (2005). Statistical misunderstandings of the properties of school scores and school accountability. In J. L. Herman & E. H. Haertel (Eds.), Uses and misuses of data for educational accountability and improvement. The 104th Yearbook of the National Society for the Study of Education (pp. 147174). Malden, MA: Blackwell.
Ruiz-Primo, M. A. (2011). Informal formative assessment: The role of instructional dialogues in assessing students learning. Studies in Educational Evaluation, 37(1), 1524.
Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26(3), 482496.
Schnellert, L. M., Butler, D. L., & Higginson, S. K. (2008). Co-constructors of data, co-constructors of meaning: Teacher professional development in an age of accountability. Teaching and Teacher Education, 24(3), 725750.
Shepard, L. A. (2009). Commentary: Evaluating the validity of formative and interim assessment. Educational Measurement: Issues and Practice, 28(3), 3237.
Shepard, L. A. (2010). What the marketplace has brought us: Item-by-item teaching with little instructional insight. Peabody Journal of Education, 85(2), 246257.
Shepard, L., Davidson, K., & Bowman, R. (2011). How middle school mathematics teachers use interim and benchmark assessment data. (CSE Technical Report). Los Angeles, CA: University of California, National Center for Research on Evaluation, Standards, and Student Testing (CRESST)
Spillane, J. (2012). Data in practice: Conceptualizing the data-based decision-making phenomena. American Journal of Education, 118, 113141.
Supovitz, J. (2009). Can high stakes testing leverage educational improvement? Prospects from the last decade of testing and accountability reform. Journal of Educational Change, 10(2&3), 211227.
Supovitz, J. A., & Klein, V. (2003). Mapping a course for improved student learning: How innovative schools systematically use student performance data to guide improvement. Philadelphia, PA: Consortium for Policy Research in Education.
Timperley, H. (2009) Evidence-informed conversations making a difference to student achievement. In L. Earl & H. Timperley (Eds.), Professional learning conversations: Challenges in using evidence for improvement (pp. 6979). New York, NY: Springer.
U.S. Department of Education, Office of Planning, Evaluation and Policy Development. (2010). Teachers ability to use data to inform instruction: Challenges and supports. Washington, DC: Author.
Wayman, J. C., & Cho, V. (2008). Preparing educators to effectively use student data systems. In T. J. Kowalski & T. J. Lasley (Eds.), Handbook on data-based decision-making in education (pp. 89104). New York, NY: Routledge.
Wayman, J. C., & Stringfield, S. (2006). Data use for school improvement: School practices and research perspectives. American Journal of Education, 112(4), 463468.
White, P., & Anderson, J. (2011). Teachers use of national test data to focus numeracy instruction. In J. Clark, B. Kissane, J. Mousley, T. Spencer, & S. Thornton (Eds.), Mathematics: Traditions and [new] practices (pp. 777785). Adelaide, Australia: AAMT and MERGA.
Young, V. M. (2006). Teachers use of data: Loose coupling, agenda setting, and team norms. American Journal of Education, 112(4), 521548.
Young, V. M., & Kim, D. H. (2010). Using assessments for instructional improvement: A literature review. Educational Policy Analysis Archives, 18(19). Retrieved from http://epaa.asu.edu/ojs/article/view/809/852