Structuring Professional Learning to Develop a Culture of Data Use: Aligning Knowledge From the Field and Research Findings
by Nancy Gerzon - 2015
Background: This research review provides an analysis of current research related to school and district data use, with a particular focus on identifying key characteristics of schools and districts with effective “data using cultures.” The research review identifies and analyzes findings in five key areas of practice: communicating professional expectations for data use; providing resources and assistance to make meaning from data; participating in the flow of information for data use; providing professional development on data use knowledge and skills; and providing leadership to nurture a culture of data use.
Purpose: The goal of this literature review was to identify key elements that the research identifies as essential to developing a school or district culture of data use. Through the literature review and analysis, this article proposes a conceptual framework for school and district data use practices that can be used to guide professional learning in the area of data use.
Research Design: The research design is an analytic essay. The article includes an analysis of current literature on school and district data use, compares key concepts presented in current studies and literature reviews, and offers conclusions based on these findings.
Conclusions: This research review provides a conceptual framework of five elements that school and district leaders can use to guide professional learning in data use. The framework provides a “mental map” for addressing the range of knowledge and skills teachers must learn to use data as a routine part of their daily practice. In particular, the Culture of Data Use Framework is designed to help school and district leaders and professional development providers tease apart the specific areas of focus for training and support. The author outlines considerations for professional learning for each of the five framework elements and closes with a set of questions that may help to highlight future research needs in the area of school-level data use.
WHAT WOULD PROFESSIONAL LEARNING FOR A CULTURE OF DATA USE INCLUDE?
I was recently asked to conduct a series of workshops for school and district leaders, in both urban and rural settings, on the topic Developing a Culture of Data Use. Over the past 15 years, my colleagues and I have conducted professional development supporting school and district data use practices, which includes supporting educators to implement many of the elements that are part of a culture of data use. Despite having extensive experience working with schools and districts on multiple aspects of data use, it was not immediately clear to us how to structure professional learning around this idea of developing a culture that supports data use. Key questions arose, including:
Is there a consistent understanding in the field about the key elements of a data use culture?
How do professional development providers best communicate to schools and districts how to simultaneously attend to the varied skills, knowledge, and abilities embedded in effective data use practices?
Is there specific guidance that would lend itself to professional learning constructs that would help school and district leaders attend to developing or improving their own culture of data use?
What are the right leverage points to enter into this work?
Is there a well-understood continuum of practice that would more effectively support educators as the culture shifted toward more consistent implementation of data use practices?
A primary goal of a professional development provider is to support educators to establish effective data use practices that are consistent with current research findings. In several cases, project evaluation data from school and district data use initiatives with which my team has been involved have shown positive results in this regard, highlighting examples of school and district practices that are well aligned with effective practice. For example, in some settings, principals and teachers have reported benefits (including improved teacher dialogue, teacher comfort with data, and student outcomes) from the use of a structured data inquiry process (as reported in Copland, 2003). In settings where coaches have provided guidance to support data analysis in peer dialogue groups, teachers have reported that this helps them better understand their own role in improving student learning (Gallimore, Ermeling, Saunders, & Goldenberg, 2009). In professional development for formative assessment, teachers have identified that they have learned new routines to gather assessment data on a daily basis and apply it immediately to instruction (Black & Wiliam, 1998; Heritage, Kim, Vendlinski, & Herman, 2009).
In both research and professional development practice, it is clear that leadership to support a culture of data use is essential. School and district leaders, both formal and informal, play a critical role in supporting teacher learning by providing opportunities for teachers to conduct data analysis and establish a culture of inquiry related to using data (Herman & Gribbons, 2001; Jandris, 2002; Mason, 2002). Leaders who support a culture of data use have modeled and fostered specific norms and expectations related to data use (Datnow, Park, & Wohlstetter, 2007). Leaders have increased capacity by developing data use expertise among teachers and then providing those teachers with informal leadership roles (Feiler, Heritage, & Gallimore, 2000; Gleason & Gerzon, 2013). In several professional development engagements, leaders have developed differentiated support for staff learning, invested in resources that support teachers opportunities to review evidence of learning, and identified innovative practices to support teachers collaborative work (Gleason & Gerzon, 2013; Knapp, Copland, Honig, Plecki, & Portin, 2010; Wayman, Brewer, & Stringfield, 2009). In these schools and districts, data constitute one tool in the administrators toolkit to support effective instruction (Knapp et al., 2010).
However, both research and practice indicate that these examples of developing a successful data culture are the exception, not the norm (Horn & Little, 2010; Lachat & Smith, 2005; Love, 2004; Supovitz & Klein, 2003). When meeting with districts to support developing teachers data use skills and knowledge, it is more often the case that teachers report drowning in data, and leaders speak about being overwhelmed by the increasing expectation for data use (Celio & Harvey, 2005; Goren, 2012; Roderick, 2012) In many cases, the lack of foundational elements for data use (i.e., limited technology infrastructure, lack of access to resources or to aligned or instructionally useful assessments, limited assessment literacy skills, or poor relational trust) raises even more questions about how to best approach implementation of data use practices (Coburn, Honig, & Stein, 2009; Marsh, Pane, & Hamilton, 2006; Mason, 2002; Supovitz & Klein, 2003; Wayman, Cho, & Johnston, 2007). Even districts that have had strong implementation of data use professional development (meaning that the process was implemented with fidelity) are also likely to exhibit very different implementation levels and dramatically different needs for support across schools (Halverson, Grigg, Pritchett, & Thomas, 2007; Knapp et al., 2010; Little, 2012). And, as noted by Horn and Little (2010), there have been times in professional development work when the same tools and processes used by teams in the same school resulted in some teams using data to focus deeply on teaching and learning, and other teams using data in ways that shut down dialogue and promoted form over substance.
These examples suggest that data use practices are complicated by the fact that there are no roadmaps. The work of developing capacities for teachers and educational leaders to use data does not involve any one process or any one approach that a leader can implement with fidelity. It involves developing a variety of skills over time and attending to the continued growth of those skills as they mature (Ikemoto & Marsh, 2007). Professional development to support a developing culture of data use must necessarily provide constructs, tools, and resources that would be useful to schools and districts over time and provide supports to meet their learning needs whether they are novice or expert on the developmental continuum of data use practices (Knapp et al., 2010).
These reflections led to the idea to design this requested professional development based on a framework for a culture of data use. This framework would provide an outline of the nonnegotiable topics that make up the required elements within an effective culture of data use. In this way, the framework and supporting materials would provide a mental map through which school and district leaders could organize their thinking around key areas of focus, select leverage points based on their context and needs, and identify next step steps to deepen or extend their practicesregardless of their current implementation status.
EXPLORING THE ELEMENTS OF A CULTURE OF DATA USE FRAMEWORK: REFLECTIONS ON CURRENT PRACTICE
In a culture of data, using data is part of the daily work of teaching and learning. Jamentz (2001) clarified that a culture supportive of data use includes the expectation that evidence should drive the day-to-day practices of administrators and teachers. Heritage and Yeagley (2005) identified a data culture as one in which teachers and administrators use data to focus on results and to guide systemic reflection and planning. In their words, in a culture of data use, educators will say using data is the way we do things around here (p. 335).
These definitions imply that in a culture of data use, there is ongoing support to help educators explore how to use the data to inform ongoing instructional or curricular improvements. The skills required for this work involve a focus on content knowledge in order to deepen the interpretive frames and references that teachers use to guide instructional practice (Ikemoto & Marsh, 2007; Little, 2012; Murnane, City, & Singleton, 2008; Supovitz, 2012; Timperley, 2009). In addition, there are routines, protocols, and organizational practices that need to be in place to provide time, resources, and supports to help teachers use data as part of the daily work of teaching and learning (Brunner et al., 2005; Lachat & Smith, 2005; Simmons, 2012; Supovitz & Klein, 2003).
These definitions of a culture of data use also imply that data analysis and use are occurring consistently. Developing consistent data use practices begins with a clear vision of how data use can improve teaching and learning, and a shared understanding of the value of educational data use (Knapp et al., 2010; Knapp, Swinnerton, Copland, & Monpas-Huber, 2006; Wayman et al., 2009). The vision of data use must be grounded in concrete understandings that the skills and knowledge needed take time to develop and that resources to do this work, including resources related to data access and teacher content knowledge, will be supported over time. In schools with a culture of data use, there is a common understanding about what this work looks like, and teachers work to hold each other accountable to ensure that they are all supporting this work (Gallimore et al., 2009; Hargreaves & Fink, 2006).
In schools with a culture of data use, all educators are supported to use data, are steadily learning new skills to access and interpret data, and do so in a safe environment (Ikemoto & Marsh, 2007; Marsh, 2012). Teachers, administrators, and students feel comfortable using data to determine next steps in instruction and learning (Andrade, 2010; Heritage, 2013). Both teachers and students feel safe to explore next steps in their work and understand that learning involves ongoing reflection, analysis, and sometimes risk.
AN INITIAL FRAMEWORK FOR A CULTURE OF DATA USE: REFLECTIONS ON PRACTICE
Table 1 shows the initial draft outline of the five elements of the Culture of Data Use Framework. This initial outline offers a high-level overview of the key concepts in each element. This draft of the framework elements is based primarily on reflections and dialogue with colleagues who support professional development in data use. Through these reflections, this initial draft of the framework captures five elements that are in place in schools and districts where data is being used every day to inform teaching and learning and are consistent with preliminary findings from the research.
Table 1. Initial Reflections on the Five Elements of a Culture of Data Use
The initial design from this framework includes leadership as the foundation (at the bottom), required to support the four other elements in the framework. Along the right side, the focus is on teacher learning of new skills and knowledge; at the top right, that focus is on developing internal capacity through collaborative inquiry skills; and just below, the learning addresses deepening content knowledge and related pedagogical literacy. Along the left side, the topics are structured around communication. At the top left, the framework addresses the critical importance of establishing systems that provide access to usable data. Just below, the issue of communicating a vision of effective data use is identified, with a focus on how these practices will support instructional improvements (and developing teacher capacity) over time.
This framework is intentionally neutral, related to specific interventions, and it does not attempt to address how effective data use cultures are developed. There are several reasons for these caveats. First, there is not yet enough knowledge from research about which instructional interventions are most effective and under what circumstances (Hamilton et al., 2009; Marsh, 2012). Second, it is often the case that schools are not able to provide specific interventions. If a school, for example, is not able to create a schedule that includes structured time for data team meetings, it is then a shared responsibility to identify how to organize time for data analysis in other ways. Time might be provided during early release days, within faculty meetings, at department meetings, or in data rooms. Although these intervention methods might not meet the full measure of continuous work in data analysis, with the right structures and protocols, these can be effective in helping schools move forward in their data use practices (Knapp et al, 2010; Marsh, 2012).
BRIDGING PRACTICE AND RESEARCH: A REVIEW OF RESEARCH FINDINGS RELATED TO THE FRAMEWORK AS A WHOLE
To determine if this initial Culture of Data Use Framework was consistent with current research findings, a modified literature review was conducted. This literature review is modified in that a specific coding process was not used; rather, information was organized based on its alignment or lack of alignment with the framework elements. The review began with a search of electronic databases (EBSCOhost, ERIC) using the search teams culture of data use, data capacity, leading data use, data inquiry, and data support. Further, several recent special editions of peer-reviewed journals were identified that focused on school and district data use, and all the articles from those were reviewed regardless of whether they met the exact search terms. Finally, colleagues sent journal articles focused on developing a culture of data use. The focus was narrowed by then identifying only those materials in peer-reviewed journals and papers presented at national educational research conferences. This significantly narrowed the scope and ended in a review of 52 documents. Existing literature reviews were used extensively, several of which provide excellent analysis of the limitations of current data use research (for example, see Hamilton et al., 2009; Marsh, 2012).
There are two phases to the literature review. First, a review took place to determine if the Culture of Data Use Framework elements were on track based on current research. In particular, this involved an analysis of recommendations from six literature reviews: Honig and Venkateswaran (2012); Hamilton et al. (2009); Means, Padilla, and Gallagher (2010); Marsh (2012); Park and Datnow (2009); and Wayman, Jimerson, and Cho (2012). This first phase of review is shown in Table 2, a comparison of the authors recommendations of five framework elements with the research findings from each of these research papers.
The second phase of the literature review involved a deeper analysis of the research in each of the five framework elements. This phase was designed to identify any gaps in thinking and clarify the relative strength of these ideas. Because this framework is to be used to design professional learning opportunities, an important goal was to use this literature review as a way to build a bridge between research and practice. The results of the second phase of the literature review resulted in the findings outlined in Table 3.
Table 2. Comparison of Culture of Data Use Framework to Current Research Findings
From the overview shown in Table 2, the initial draft of the Culture of Data Use Framework is consistent with current findings in the research literature. There is strong alignment between the five elements identified in the initial high-level overview of the framework and those named as key considerations from research. Both district- and school-level studies indicate the importance of these five framework elements. Although there were no findings that did not align, the three elements in Table 2 that are italicized involve a more narrow lens than that indicated in the Culture of Data Use Framework. This initial review offers a good starting point for moving forward into the second phase of the literature review.
Though these elements show tight alignment, some caveats are worth noting. None of these reviews speaks to causal links between these elements and improvements in student learning. In their large-scale review of data use research (Hamilton et al., 2009), a panel of national experts pointed out that there is low evidence of causationdue partly to the fact that the research does not yet provide clear causal links between specific factors of data use and improvement. Marsh (2012), in her synthesis of current research findings, found three promising practices (noted in Table 2). Beyond that, she identified mixed findings along with a set of common conditions appearing to influence implementation of data use practices, which are largely aligned to the mentioned categories; however, in her analysis, more research is needed to verify which practices in these areas are most effective. These conditions include intervention characteristics (capacity, data properties), broader context (leadership, organizational structures), and individual relationships and characteristics (trust, beliefs, and knowledge). In other words, the clarity and strength of some findings are not yet known.
Following the alignment outlined in Table 2, the next round of review involved identifying research findings to inform understanding of each of the five Culture of Data Use Framework elements. The aim of this second phase of analysis was to clarify details within each element of the framework. The following section provides a brief overview of research and summary of findings in each element and culminates with the summary findings from each element listed in Table 3.
BRIDGING RESEARCH AND PRACTICE: A REVIEW OF FINDINGS RELATED TO EACH OF THE FIVE FRAMEWORK ELEMENTS
COMMUNICATE PROFESSIONAL EXPECTATIONS FOR DATA USE
Hamilton et al. (2009) recommended establishing a clear vision for data use that includes a written plan articulating activities, roles and responsibilities for all data users in the system (Datnow et al., 2007; Halverson, et al., 2007; Mason, 2002). Developing a common vision is an entry point toward consistent communication regarding expectations for data use (Knapp et al., 2010). Common understandings provide a vehicle for articulating goals and for fostering meaningful conversations about teaching and learning (Wayman, Jimerson, & Cho, 2012). At the school level, expectations for data use are primarily communicated by principals and teacher leaders and can be conveyed in multiple ways (Datnow et al., 2007). Schedule changes that promote time for teacher reflection and dialogue, for instance, help communicate that data-driven inquiry has value and will be supported through structured time (Wayman et al., 2009). Similarly, schools that document common understandings that emerge from team dialogue can move closer to an aligned vision of teaching and learning (Wayman, Snodgrass-Rangel, Jimerson, & Cho, 2010).
Spillane (2012) described how schools redesign organizational routines and expectations to continually frame the changing expectations regarding instructional practices. In these environments, over time, data can help to establish more consistent language and instructional practice throughout the school (Wohlstetter, Datnow, & Park, 2008). Setting expectations at the district level is an important role of central office leaders. Establishing consistent data use practices that are used at the principal and school levels appears to support more focused data use practices (Honig & Venkateswaran, 2012; Park & Datnow, 2009). Central office administrators also play an informal role in disseminating understandings of data use practices. There is a positive correlation between increased data use in schools and positive informal relationships between district and school staff (Daly, 2012). Relational trust within schools is an important prerequisite to data use, and the ways in which principals and teacher leaders communicate clear expectations about the use of protocols, norms, and language can impact teachers comfort with data use practices (Copland, 2003; Ikemoto & Marsh, 2007; Marsh, 2012).
Principals ability to filter information from central office, to shift from messages about accountability to messages focused on the central issues of teaching and learning, may be able to strengthen the impact of key messages at the school level (Knapp et al., 2010). School leaders ability to reframe district messages helped teachers internalize improvement practices. In other words, when leaders found ways to capture district messages to help support the schools own improvement practices, principals reported that this improved coordination helped support internal values of teachers working collaboratively to meet internal school improvement goals. Principal leadership has consistently been shown to positively impact data use practices within schools (Copland, 2003; Herman & Gribbons, 2001; Lachat & Smith, 2005; Mason, 2002; Wayman et al., 2009).
Communicate Professional Expectations for Data UseSummary of Research Findings
Districts and schools establish and communicate a common interpretation and orientation toward data-driven decision making.
Schools and the district provide clear messages about how data use supports improvements in student learning.
District data use expectations are mediated at the school level by formal and informal school leaders so that establishing professional expectations shifts over time as the disposition and skills to use data grow.
District and school leaders clarify when data needs are changing.
Expectations for data use are communicated through presentations, policy documents, and modeling of expected practice.
PARTICIPATE IN THE FLOW OF INFORMATION FOR DATA USE
Investing in data management systems is an essential first step in supporting data use practices at the school and district levels (Honig, 2004; Means et. al., 2010; Wayman et al., 2010). Decision makers at different levels of the educational system have different information needs, and depending on their role, they will need to access information in slightly different ways. Developing an effective information system requires first identifying and analyzing the needs of decision makers (Breiter & Light, 2006; Park & Datnow, 2009). Any information system should be flexible enough to accommodate multiple users of data (Mandinach, Rivas, Light, Heinze ,& Honey, 2006) and should be structured to ensure that data are available to users in simplified and comprehensible forms. Ideally, data systems should be organized such that users can ask questions that address current educational issues and needs (Means et al., 2010) and should allow teachers to focus on specific questions of student achievement (Hamilton et al., 2009). Marsh (2012) indicated that a key element that increases the likelihood that data will be used for improvement is making data usable, safe, and easily digestible.
In the national survey of data use practices conducted by Means et al. (2010), the researchers found that teachers reported that they rarely prepared data on their own. Teachers relied on district, school, and data team leaders to collect and prepare data for review. When lacking effective data management systems, teachers in one study reported significant time spent compiling data, resulting in teachers feeling constrained by a lack of time to explore instructional actions that might arise from data analysis (Wayman, Cho, Jimerson, & Spikes, 2012). Typically, central office serves a critical role in organizing and providing data management systems, but schools play an important role as well. In a study of urban school leadership related to data use (Knapp et al., 2010), principals in many schools used the districts data system only as a starting point. In these schools, principals and teacher leaders created their own within-school data systems that were more able to provide continual feedback to teachers and teacher leaders about student learning. This frame of having both district and school data systems relates to the ideal of having differentiated access based on the needs of data users.
As leads in ensuring access to data, districts are also cautioned not to be singularly directive in their role as data managers. In their study of central office processes related to district data use, Honig and Venkateswaran (2012) found that the role of central office is best thought of regarding supporting data use as both top down and bottom up. Central offices that encouraged strong bottom-up flow of information had stronger evidence-use practices and were more likely to access important information from schools that would inform central office support over time. Viewed from a different vantage point, in their study of social networking, Daly and Finnegan (2010) reported that when schools have weak ties with central office, the top-down nature of information from central office can serve to limit rather than enable schools use of data in decision making. Ensuring access to data that are useful and transparent, then, seems to be helped by ensuring strong central office and school communication about the purposes and applications of data use at the school level.
Participate in the Flow of Information for Data UseSummary of Research Findings
Districts work in cooperation with schools to develop data systems that ensure appropriate data for classroom, school, and district use.
Districts and schools coordinate how to centralize and streamline data reporting.
Districts and schools should work together to clarify when data analysis needs are changing and revise current systems to accommodate emerging needs.
Both central office and schools may have a role to ensure that data reports meet the needs of teachers and can address the inquiry questions of teacher teams.
PROVIDE RESOURCES AND ASSISTANCE TO MAKE MEANING FROM DATA
It is fairly well established in the literature that analyzing data, and moving from analysis to instructional action, is most likely to occur in collaborative teams. Data use in schools is primarily a process of interpretation (Coburn, 2010; Lachat & Smith, 2005) in which teachers are engaged in making sense of data (Little, 2012; Spillane, 2012; Wohlstetter et al., 2008). Collaborative teams provide a vehicle for this analysis. Structured collaborative time for teachers is essential to help them move from analysis of data to instructional action (Wayman & Stringfield, 2006). Collaborative team practices should take place regularly within subject area and grade level teams (Hamilton et al., 2009) where data analysis can be focused on developing common expectations for student learning and consistent instructional practices (Halverson et al., 2007; Lachat & Smith, 2005). Teams can also be structured as vertical or cross-subject area collaboration to provide time for teachers to develop and align consistent instructional practices and review broader student learning needs (Datnow et al., 2007; Knapp et al., 2006). Marsh (2012) highlighted the value of both horizontal and vertical teaming practices as an important component of more successful interventions. Similar findings are described in a case review of data use practices in high-performing, high-poverty schools (Gleason & Gerzon, 2013), where vertical teams served to deepen teachers content knowledge, and horizontal (grade-level or department) teams focused on developing teachers use of inquiry.
Using an inquiry approach to data analysis within collaborative teams is a fairly well-established practice (Nelson, Slavit, & Deuel, 2012). There are a variety of inquiry models reviewed in the literature (see Hamilton et al., 2009; Ikemoto & Marsh, 2007; Mandinach, Honey, Light, & Brunner, 2008; Means et al., 2010; National Forum on Education Statistics, 2012) and emerging evidence that teams use of a structured cycle of inquiry can lead to improvements in student learning. Gallimore et al. (2009) found that more frequent data team meetings led to improved practices. Similarly, Slavit, Nelson, and Deuel (2012) framed that the use of an inquiry cycle can support teacher learning that is ongoing and focused on instruction, teachers knowledge of content, and learning goals.
Collaborative teams appear to benefit from a sense of safety (Marsh, 2012; Means et al., 2010) that ensures that conversations about data, and the data themselves, will not be used in punitive ways. Teams also benefit from relational trust within the group (McLaughlin & Talbert, 2006; Talbert, 2009), the use of well-established norms (Louis, 2006), and having at least one person on the team with strong content knowledge (Nelson et al., 2012). Collaborative teams also benefit from a distributed leadership model whereby team members are provided with opportunities for leadership and ongoing professional learning (Hargreaves & Fink, 2006).
For collaborative inquiry to support data analysis that leads to instructional (and student learning) changes, teachers must be willing to work in teams to explore their current instructional practices in light of evidence (Horn & Little, 2010; Little, 2012). Nelson et al. (2012) explored the idea of measuring the conversational routines of teams. In their analysis of PLCs over five years, they determined that professional learning communities (PLCs) with conversational routines that are consistently focused on improving student learning resulted in more transformational changes in teachers beliefs, values, and instruction, as compared with teachers whose conversational routines focused on proving what students knew. Groups focused on improving student learning through inquiry carefully examined student data, openly wondered about what they could do differently to better support student learning, and were willing to change their practice. In these teams, knowledge became a dynamic, ongoing negotiation of learning goals, student understandings, and implications on practice (Nelson et al., 2012, p. 16). In a subsequent analysis of their analysis of PLCs, Slavit, Nelson, and Dueul (2012) submitted that a teacher groups stance toward student learning data can determine the nature of their collaborative work (p. 1).
Provide Resources and Assistance to Make Meaning From DataSummary Of Research Findings
District and school staff work together to ensure that teachers have adequate structures and supports to review data.
Educators are supported to participate in collaborative inquiry in order to make sense of data and apply findings to instruction and improvement.
Educators from multiple levels of the educational system work together to collectively understand how to use evidence from data-analysis in decision making.
District and school leaders work together to ensure that teachers apply new knowledge to improve classroom instruction or school-level practices.
PROVIDE PROFESSIONAL DEVELOPMENT ON DATA USE KNOWLEDGE AND SKILLS
In their study of current data use practices in U.S. schools, Means, Padilla, DeBarger, and Bakia (2009) identified that teachers report two major barriers to implementing data use: a lack of preparation on how to use data and a lack of technical skills of staff to use data systems. Collaborative data analysis practices are improved when the individual teachers each have strong knowledge and skills regarding how to use data and how to make meaning from data (Marsh, 2012). Professional development for data use helps teachers develop skills to be used in collaborative work, such as understanding data literacy and assessment literacy (Mandinach & Gummer, 2013), applying interpretive frames of reference for data analysis (Knapp et al., 2006), and understanding how to move from data analysis to using information to support instructional or administrative practice (Mandinach & Gummer, 2012; Mandinach & Honey, 2008). Means et al. (2010) noted that only half of districts surveyed provided training to teachers on how to use data to inform instructional practice.
Professional development that focuses on educators expanding their repertoire of instructional strategies helps them to better respond to the needs of individual students or groups of students who are identified during data analysis (Breiter & Light, 2006). This focus on deepening instructional practice through data, with regard to professional development, indicates that professional learning should be customized to meet the specific needs of teachers. The Means et al. (2010) survey indicates that a majority of teachers want more training on how to interpret data and connect them to instructional practices. Orland (2013) suggested that data literacy for teachers must necessarily differ by content area because the kind of data that teachers will be asked to interpret will differ from grade to grade and across curriculum areas. In other words, professional development should be differentiated based on teachers needs (Wayman et al., 2010) and focused on deepening content knowledge in order to help them identify instructional changes that will lead to student improvement (Timperley, 2009).
Honig, Copland, Rainey, Lorton, and Newton (2010) identified the role of central office as essential to supporting professional development for data use in schools. Central office leaders appear to be a main provider of professional development and have a particular focus on helping school staff build capacity to use evidence. A recommendation made by Datnow et al. (2007) is to provide professional development for district leaders who can then provide turnkey training to school and district staff as necessary. This is a strategy for building internal capacity and for developing consistent practices across schools. Wayman et al. (2010) noted that when a district did not embed data use and associated learning opportunities in the regular workday, the additional work created for teachers resulted in them opting out of data use practices entirely.
Provide Professional Development on Data Use Knowledge and SkillsSummary of Research Findings
Districts and schools provide opportunities for professional learning that builds educators capacities to identify data, interpret data, make meaning from evidence, and use evidence to inform instruction.
Professional development should combine information about data literacy and assessment literacy with content-focused expertise in order to build knowledge of how to apply data findings.
Learning opportunities should include expanding teachers repertoire of instructional strategies to ensure that teachers can more effectively transition from analysis to classroom practice that is informed by the evidence.
Whenever possible, learning opportunities should take place during the school day and be conducted by internal leaders.
PROVIDE LEADERSHIP TO NURTURE A CULTURE OF DATA USE
Leaders recognition that data analysis processes support the real work of teaching and learning appears to serve as an important leadership frame through which data analysis can take hold. In this way, leading for data use is largely consistent with leading for improvement (Knapp et al., 2010). In their study of urban leadership practices, Knapp and his colleagues found that leadership for data use is strongly focused on issues of teaching and learning, identifying innovative resources to support teachers and teacher leaders, structuring time to attend to issues of teaching and learning, and providing a consistent focus on using evidence to guide instructional improvement. Schools with a high level of data use exhibit more effective practices to support teachers to use data, including providing structural supports to ensure time for collaborative team meetings (Wayman, Jimerson, & Cho, 2012), modeling data use practices with faculty (Young, 2006), and distributing internal expertise among faculty (Anderson, Leithwood, & Strauss, 2010).
Schools with a focus on continuous improvements in learning often see a proliferation of individuals engaged in within school instructional leadership (Knapp et al., 2010, p. 11). Involving multiple teacher leaders in playing roles in supporting collaborative inquiry, professional development, and communication allows for more internal capacity building related to developing inquiry-based practices. Deepening the connection to student learning can be enhanced through the use of distributed leadership models. Gallimore et al. (2009) found that using teacher-facilitators during inquiry team meetings opened the door for coaches, content experts, and principals to take on more informal leadership roles in meetings, providing necessary support and leadership to teacher teams. In this way, informal teacher leaders provide much-needed support to ensure that data practices can take hold. Similarly, when principals support teachers and teacher leaders to share developing strategies for data analysis, it can lead to more effective team practices because team members come to understand and implement more useful collaborative inquiry practices (Nelson et al., 2012).
Central office plays an important leadership role in developing a culture of data use. As leaders, they can frame key messages, provide resources and supports for school-level implementation, convene cross-school dialogue groups to streamline data use practices, and develop strategies to build capacity for data use over time. A central focus of their role is to support principals to use data themselves. Luo (2008) found that central office implementing models for which principals are held accountable to use data had an overall positive impact on principals use of and comfort with data. Central office can help schools set aside time that is required for collaborative inquiry, and streamline the use of inquiry practices (Wayman, Cho, Jimerson, & Spikes, 2012).
Leadership for data use is not a one-way street, nor is it only top down. Data use processes in central offices depend on schools. Honig and Venkateswaran (2012) noted that school staff support central office staff to make sense of evidence about school progress and can help central office use this evidence in their decision making. This two-way focus toward leading for data use may ensure greater transparency in data use. Marsh (2012) identified data transparency as a key element that increased the likelihood that data will be used for improvement because transparency assures teachers that they will not be evaluated based on data findings.
Provide Leadership to Nurture a Culture of Data UseSummary of Research Findings
The principal recognizes and models how data use informs instruction, and he or she fosters shared mental models of how data use can improve teaching and learning.
Principals and superintendents ensure access to resources that establish a data culture, such as setting aside time for data practices, ensuring a safe environment for teachers to engage in dialogue about best practices, and modeling effective data use practices.
The role of central office includes ensuring that the principals data responsibilities are clearly defined and manageable.
The role of central office is both to empower principals to use data to make decisions and to hold principals accountable for data use practices at the school level.
Leaders report having more successful data use and inquiry practices using shared or distributed leadership models that include teacher leaders in a variety of roles.
THE CULTURE OF DATA USE FRAMEWORK
The framework, which includes the summary statements from each preceding section, is outlined in Table 3. This provides a useful construct toward the initial professional learning goalto provide a mental map for school and district leaders as they work to develop and sustain a culture of data use. This framework is designed to highlight the specific areas of focus for learning, support, guidance, and policy. Further, the framework is organized in such a way that these ideas are not framed as a laundry list of activities; rather, they are organized to support leaders to identify and explore key ideas in each of the five elements.
The goal of this framework is to provide a lens that will help leaders tease these elements apart and attend to each as needed. With the complex nature of developing data use skills and knowledge, and with the range of new initiatives often under way in schools and districts, it can be difficult to identify appropriate next steps to support improved data use practice. This framework outlines a range of potential strategic actions that can build internal capacity over time.
The Culture of Data Use Framework helps leaders clarify and frame goals in each area with a greater sense of how different aspects of this work benefit from being teased apart. A goal of leadership coaching work designed with this framework will be to help school and district leaders understand the importance of operationalizing each of the five elements simultaneously. Professional developers often hear frustration from educators who, for example, are placed in collaborative teams but are not sure how to make those teams a good use of their time. If they lack opportunities for professional learning to support that work, or if there is no real access to useful data, or if there are unclear messages about the expectations for the work, it is likely these collaborative models will not take hold and will add to teachers current workloads. Engaging in dialogue related to each elements impact on the other will provide leaders with better clarity about the work necessary in each area and potentially increase ability to review, diagnose, and correct problems as they arise.
Again, caveats must be raised. Most important, the research does not yet direct leaders to specific interventions in each area. As Turner and Coburn (2012) reported,
We know little about how people in schools are interacting with the datainterpreting it, responding to it, ignoring itand how these responses contribute to various outcomes of interest. This is unfortunate because understanding that comes without understanding the mechanisms that produced them means that we have little insight into how to redesign data use interventions so as to increase their impact in practice. (p. 10)
The important consideration is that although the key elements of sustained and effective data use are becoming clear, the intervention strategies that support specific work within each element are not well understood. The work of professional development providers and external coaches will be to assist schools in navigating that by helping educational leaders set goals around outcomes, not implementation activities. Given the pressures that educators face to implement data systems and practices, the press for better understanding interventions is an important next step in the research.
Table 3. Research Summaries for the Culture of Data Use Framework
PLANNING PROFESSIONAL LEARNING FOR DEVELOPING A CULTURE OF DATA USE
The Culture of Data Use Framework (Table 3) is an entry point to a much broader set of professional development tools that build on the five elements outlined in the framework. The professional learning design for this work involves supporting school and district data teams through both workshops and coaching sessions to improve their data use practices and address elements in the framework that they self-select as needing support. The varied structures to support this work include:
A series of frameworks, similar to Table 3, that outlines key considerations in each framework element such as barriers, guiding questions, and examples of written guidance.
A series of school-level vignettes to explore examples of effective practices in each framework element.
Multiyear case studies of district and school development of a culture of data use. These cases explore how different elements of the framework impact each other, and they help leaders investigate strategies to address implementation challenges.
A self-assessment tool that can be completed by district leaders, principals, or the school data team to clarify current practice and outline next steps for their school or district.
Examples of written guidance to support communicating expectations for developing a culture of data use.
Examples of professional learning plans, alignment to teacher professional goals, teacher leader roles and responsibilities, and other tools to focus this work on teaching and learning.
Professional learning on using an inquiry model and developing collaborative inquiry teams.
Leadership and data-team coaching to support ongoing support as leaders navigate the implementation process.
NEXT STEPS TO SUPPORT SCHOOLS AND DISTRICTS TO DEVELOP AND SUSTAIN A CULTURE OF DATA USE
The expectations for educators to use data are significant, and they are continuing to increase. Districts and schools in which my colleagues and I work are forging ahead in using data. Their work is often guided by knowledge about teaching and learning and school improvement but not necessarily with a deep understanding of what is needed to move from uneven adoption of data use practices by some educators to transformational change of all educators. Although there is still information that is not well understood, the professional learning supported by the Culture of Data Use Framework provides an entry point to better understand main levers for change. As districts work continues, we will continue to look for research that helps answer critical questions to guide deeper understanding and knowledge of using data. These questions include:
Communicate Professional Expectations for Data Use
Which district and school policies are the best levers to effectively communicate expectations about data use practices?
Are there pictures of practice, or exemplars, that can help a district explore what it looks like in schools that have an effective culture of data use?
How do communications need to change over time to clarify ongoing expectations of changes in practices as the culture of data use evolves?
Participate in the Flow of Information for Data Use
Do we understand how to help districts navigate complex data sets when they lack the technology infrastructure and resources to access or streamline data?
What are effective approaches for schools and districts to align data systems?
Provide Resources and Assistance to Make Meaning From Data
Under what conditions do teacher teams create opportunities for teacher learning, and under what conditions do they not?
What factors contribute to rich discussions about teaching and learning within collaborative teams? How do teams learn to have an inquiry stance that effectively supports exploration of new instructional practices?
What is the difference between providing a content area coach and a data coach (who is largely focused on managing the data analysis process) to support teacher teams? Are there times in the developmental stages of teams when one or the other is more appropriate?
Provide Professional Development on Data Use Knowledge and Skills
What do we know about the progression of skills and knowledge in data use to help frame different starting points for different schools or districts?
What is the progression of learning that educators take as they learn new skills to use and apply evidence?
What are effective strategies to align deepening teachers content knowledge with developing inquiry skills? Are there synergies that would make professional development more efficient?
Provide Leadership to Nurture a Culture of Data Use
Do we know precisely which leadership capabilities are most supportive of developing a culture of data use? Which policies, practices, or alignments can best support school leaders to establish a culture of data use?
How do we help educational leaders think about the unique roles of those who use data at different levels of the educational system?
What specific leadership practices are necessary? Do these change, or how do these change, as skills and knowledge within the system mature?
When considering the range of potential interventions (data coaches, data teams, leadership development, professional learning teams, data use protocols, technology tools, improving the quality of assessments, structuring time for reflection), how does a district decide which of these would be most useful in their context? How can we be more specific about which interventions are most useful and which interventions are most useful in which settings?
How might data practices shift in light of adoption of new standards and assessments? Are there some natural alignments that can be considered as new standards and assessments are widely adopted?
Anderson, S. E., Leithwood, K., & Strauss, T. (2010). Leading data use in schools: Organizational conditions and practices at the school and district levels. Leadership and Policy in Schools, 9(3), 29.
Andrade, H. L. (2010). Students as the definitive source of formative assessment. In H. L. Andrade & G. J. Cizek (Eds.), Handbook of formative assessment (pp. 90105). Abingdon, England: Routledge.
Breiter, A., & Light, D. (2006). Data for school improvement: Factors for designing effective information systems to support decision-making in schools. Educational Technology & Society, 9(3), 206217.
Black, P. J., & Wiliam, D. (1998). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 80, 139148.
Brunner, C., Fasca, C., Heinze, J., Honey, M., Light, D., Mandinach, E., & Wexler, D. (2005). Linking data and learning: The Grow Network study. Journal of Education for Students Placed At Risk, 10(3), 241267.
Celio, M. B., & Harvey, J. (2005). Buried treasure: Developing a management guide from mountains of school data. Seattle, WA: Center on Reinventing Public Education.
Coburn, C. E. (2010). Partnership for district reform: The challenges of evidence use in a major urban district. In C. E. Coburn & M. K. Stein (Eds.), Research and practice in education: Building alliances, bridging the divide (pp. 167182). New York, NY: Rowman and Littlefield.
Coburn, C. E., Honig, M. I., & Stein, M. K. (2009). Whats the evidence on districts use of evidence? In J. Bransford, D. J. Stipek, N. J. Vye, L. Gomez, & D. Lam (Eds.), Educational improvement: What makes it happen and why? (pp. 6786). Cambridge, MA: Harvard Educational Press.
Copland, M. A. (2003). Leadership of inquiry: Building and sustaining capacity for school improvement. Educational Evaluation and Policy Analysis, 25(4), 375395.
Daly, A. (2012). Data, dyads and dynamics: Exploring data use and social networks in educational improvement. Teachers College Record, 114(11), 138.
Datnow, A., Park. V., & Wohlstetter, P. (2007). Achieving with data: How high-performing school systems use data to improve instruction for elementary students. Center on Educational Governance, Rossier School of Education, University of Southern California, Los Angeles. Retrieved from http://people.uncw.edu/kozloffm/AchievingWithData.pdf
Feiler, R., Heritage, M., & Gallimore, R. (2000). Teachers leading teachers. Educational Leadership, 57 (7), 6669.
Gallimore, R., Ermeling, B. A., Saunders, B., & Goldenberg, C. (2009). Moving the learning of teaching closer to practice: Teacher education implications of school-based inquiry teams. Elementary School Journal, 109(5), 537553.
Gleason, S., & Gerzon, N. (2013). Growing into equity: Professional learning and personalization in high-achieving schools. Thousand Oaks, CA: Corwin Press.
Goren, P. (2012). Data, data, and more datawhats an educator to do? American Journal of Education, 118(2), 233237.
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), 159194.
Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from http://ies.ed.gov/ncee/wwc/practiceguide.aspx?sid=12
Hargreaves, A., & Fink, D. (2006). Redistributed leadership for sustainable professional learning communities. Journal of School Leadership, 16(5), 550565.
Heritage, M. (2013). Formative assessment in practice: Making it happen in the classroom. Cambridge: MA: Harvard Education Press.
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.
Heritage, M., & Yeagley, R. (2005). Data use and school improvement: Challenges and prospects. In J. L. Herman & E. H. Haertel (Eds.), Uses and misuses of data for educational accountability and improvement (pp. 320339). Malden, MA: Blackwell.
Herman, J., & Gribbons, B. (2001). Lessons learned in using data to support school inquiry and continuous improvement: Final report to the Stuart Foundation (CSE Technical Report 535). Los Angeles: University of California, National Center for Research on Evaluation, Standards, and Student Testing, Graduate School of Education & Information Studies.
Honig, M. I. (2004). Wheres the up in bottom-up reform? Educational Policy 18(4), 527561.
Honig, M., Copland, M. A., Rainey, L., Lorton, J. A., & Newton, M. (2010). Central office transformation for district-wide teaching and learning improvement. Seattle: Center for Teaching and Learning, University of Washington.
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.
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.
Ikemoto, G. S., & Marsh, J. A. (2007). Cutting through the data driven mantra: Different conceptions of data-driven decision making. Yearbook of the National Society for the Study of Education, 106(1), 105131.
Jamentz, K. (2001). Beyond data mania. Leadership Magazine, 31(2), 813.
Jandris, T. P. (2002). Data-based decision-making: Essentials for principals. Alexandria, VA: National Association of Elementary School Principals.
Knapp, M. S., Copland, M. A., Honig, M. I., Plecki, M. L., & Portin, B. S. (2010). Learning-focused leadership and leadership support: Meaning and practice in urban systems. Seattle: Center for the Study of Teaching and Policy, University of Washington.
Knapp, M. S., Swinnerton, J. A., Copland, M. A., & Monpas-Huber, J. (2006). Data-informed leadership in education. Seattle, WA: Center for the Study of Teaching and Learning.
Lachat, M. A., & Smith, S. (2005). Practices that support data use in urban high schools. Journal of Education for Students Placed at Risk, 10(3), 333349.
Little, J. W. (2012). Understanding data use practice among teachers: The contribution of micro-process studies. American Journal of Education, 118(2), 143166.
Louis, K. S. (2006). Changing the culture of schools: Professional community, organizational learning, and trust. Journal of School Leadership, 16(5), 477489.
Love, N. (2004). Taking data to new depths. Journal of Staff Development, 25(4), 2226.
Luo, M. (2008). Structural equation modeling for high school principals data-driven decision making: An analysis of information use environments. Educational Administration Quarterly 44(5), 603634.
Mandinach, E. B., & Gummer, E. S. (2013). Defining data literacy: A report on a convening of experts. Journal of Educational Research and Policy Studies, 13(2), 2850.
Mandinach, E. B., & Honey, M. (Eds.). (2008). Data-driven school improvement: Linking data and learning. New York, NY: Teachers College Press.
Mandinach, E. B., Honey, M., Light, D., & Brunner, C. (2008). A conceptual framework for data-driven decision-making. In E. B. Mandinach & M. Honey (Eds.), Data-driven school improvement: Linking data and learning (pp. 1331). New York, NY: Teachers College Press.
Mandinach, E. B., Rivas, L., Light, D., Heinze, C., & Honey, M. (2006, April). The impact of data-driven decision making tools on educational practice: A systems analysis of six school districts. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA.
Marsh, J. A. (2012). Interventions promoting educators use of data: Research insights and gaps. Teachers College Record, 114(11), 1-48.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research (No. OP-170-EDU). Santa Monica, CA: RAND.
Mason, S. (2002, April). Turning data into knowledge: Lessons from six Milwaukee public schools. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
McLaughlin, M. W., & Talbert, J. E. (2006). Building school-based teacher learning communities: Professional strategies to improve student achievement. New York, NY: Teachers College Press.
Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision making in schools: Teacher access, supports, and use. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation and Policy Development.
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.
Murnane, R. J., City, E. A., & Singleton, K. (2008). Using data to inform decision making in urban school districts: Progress and new challenges. Voices in Urban Education, 18, 513.
National Forum on Education Statistics. (2012). Forum guide to taking action with education data (NFES 2013-801). U.S. Department of Education. Washington, DC: National Center for Education Statistics.
Nelson, T. H., Slavit, D., & Deuel, A. (2012). Two dimensions of an inquiry stance toward student-learning data. Teachers College Record, 114(8), 142.
Orland, M. (2013). Why definitions matter: Data literacy and education policy change. Journal of Educational Research and Policy Studies, 13(2), 5055.
Park, V., & Datnow, A. (2009). Co-constructing distributed leadership: District and school connections in data-driven decision-making. School Leadership and Management, 29(5), 477494.
Roderick, M. (2012). Drowning in data but thirsty for analysis. Teachers College Record, 114(11), 19.
Simmons, W. (2012). Data as a lever for improving instruction and student achievement. Teachers College Record, 114(11), 110121.
Slavit, D., Nelson T. H., & Deuel, A. (2013). Teacher groups conceptions and uses of student-learning data. Journal of Teacher Education, 64(1), 821.
Spillane, J. P. (2012). Data in practice: Conceptualizing the data-based decision-making phenomena. American Journal of Education, 118(2), 113141.
Supovitz, J. (2012). Getting at student understandingthe key to teachers use of test data. Teachers College Record, 114(11), 129.
Supovitz, J., & Klein, V. (2003). Mapping a course for improved student learning: How innovative schools use student performance data to guide improvement. Philadelphia, PA: Consortium for Policy Research in Education.
Talbert, J. E. (2009). Professional learning communities at the crossroads: How systems hinder or engender change. In A. Lieberman (Ed.), Second international handbook of educational change (pp. 555571). Dordrecht, the Netherlands: Springer.
Timperley, H. (2009). Evidence informed conversations making a difference to student achievement. In L. M. Earl & H. Timperley (Eds.), Professional learning conversations: Challenges in using evidence for improvement (pp. 6979). Dordrecht, the Netherlands: Springer.
Turner, E. O., & Coburn, C. E. (2012). Interventions to promote data use: An introduction. Teachers College Record, 114(11), 113.
Wayman, J. C., Brewer, C., & Stringfield, S. (2009, April). Leadership for effective data use. Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA.
Wayman, J. C., Cho, V., Jimerson, J. B., & Spikes, D. D. (2012). District-wide effects on data use in the classroom. Education Policy Analysis Archives, 20(25). Retrieved from http://epaa.asu.edu/ojs/article/view/979
Wayman, J. C., Cho, V., & Johnston, M. (2007). The data-informed district: A district-wide evaluation of data use in Natrona County School District. University of Texas, Austin.
Wayman, J. C., Jimerson, J. B., & Cho, V. (2012). Organizational considerations in establishing the data-informed district. School Effectiveness and School Improvement, 23(2), 159178.
Wayman, J. C., Snodgrass Rangel, V. W., Jimerson, J. B., & Cho, V. (2010). Improving data use in NISD: Becoming a data-informed district. University of Texas, Austin.
Wayman, J. C., & Stringfield, S. (2006). Technology supported involvement of entire faculties in examination of student data for instructional improvement. American Journal of Education 112(4), 549571.
Wohlstetter, P., Datnow, A., & Park, V. (2008). Creating a system for data-driven decision making: Applying the principal-agent framework. School Effectiveness and School Improvement Journal, 19(3), 239259.
Young, V. M. (2006). Teachers use of data: Loose coupling, agenda setting and team norms. American Journal of Education, 112(4), 521548.