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This exploratory research study employed a mixed methods research design to examine the data-driven decision making supports of data system infrastructure, analytic capacity, and data-use leadership from the perspective of the DDAs (district data administrators that oversee the provisions of data-driven decision making supports throughout the school district) and from the perspective of school and district personnel (teachers, school and district administrators, school support staff, and district staff that actively use data-driven decision making toward their educational practices). Qualitative data were collected through five individual interviews of DDAs. Quantitative data were collected through a district-wide online survey of school and district personnel (N = 218). Qualitative and quantitative data were used together to capture the overall state of the data-driven decision making supports within the school district.Findings indicate the district under study is still in the early phases of implementing quality data-driven decision making supports such that supports are provided, but they have limitations and are “a work in progress.” The quality of the district’s data-driven decision making supports is reflected in the perceptions of the school and district personnel. On average, the school and district personnel were between somewhat disagree to somewhat agree that the district provides quality data driven-decision making supports in the three areas of data system infrastructure, analytic capacity, and data-use leadership. The findings also show predictive relationships between data-driven decision making supports and data-driven decision making processes, indicating the importance of having quality data-driven decision making supports. The findings of the study also highlight notable considerations for implementing quality data-driven decision making supports such as implementation phase, district size and breadth, organizational structures, and time.

This special issue explores the complexity and interconnectedness of the many components of data-based decision making. The selection of papers represents many countries (i.e., Belgium, The Netherlands, New Zealand, Norway, and the United States), theories, methods, and foci. All the papers seek to explicate how data are used at the different level of the system, ranging from students, teachers, schools, and districts. Together these papers offer a view of the current data-based decision making landscape, including in- and pre-service professional development, district and school organizational capacity, the data use process (from goal setting to collaborative instructional decision making), and effects on student achievement. The intent of the special issue is to stimulate future work in terms of impact on research, theory, policy, and practice.

This qualitative phenomenological study explored how school leaders in seven urban high schools in California make sense of and use equity-related data to create more equitable educational opportunity for their students. Equity-related data here refers to school data (accountability data included) that demonstrate unequal access to educational opportunity and disparity of outcomes for subgroups of students. Data has been part of the U.S. educational system increasingly since 1965. The amount of data available has accelerated even more in the last decade in the wake of the No Child Left Behind (NCLB) Act. Much of that data has been collected and used by educators for a broader category of assessment and measurement of students and school performance. Recently, however, there is an increasing interest in and research about how school leaders can use data for equity purposes.Utilizing a qualitative phenomenological approach, this research examined how 19 school leaders, at seven urban high schools in the state of California, make sense of and use equity-related data to disrupt patterns of inequality. Through interviews, observation, shadowing, and collection of documents and paper artifacts, this studycollected data that demonstrate how these school leaders were making sense of data at their schools and using such to create more equitable opportunities for students. This study suggests that school leaders' interpretation and use of equity-related data could lead to the transformation and equalization of educational opportunities for all students. However, school leaders do not make sense of data in a void. They come to data with some set of mindscapes or ideological frame of reference that has been shaped by social background, beliefs, values, education, etc. It is important to understand the mindset with which school leaders come to data. This study then provides understanding and perspective that is too often missing in educational research about school leaders and data-driven decisions.

Collaborative data use requires educator capacity in moving data to action to address root causes of student underperformance. Implementation of the model used in the present study has proved promising in European countries for building educator capacity and addressing problems-of-practice, but this model has not been studied in the United States (US), where structural issues and accountability factors present different challenges. In the present study, we explored enabling and hindering factors of the implementation in an elementary school in the US to better understand how differences in policy and practice contexts influence collaborative data use. Organizational structures and some policies in the US hindered implementation. Drawing on our findings, we suggest shifting thinking around data use to accommodate for both short cycles of data use (for straightforward problems) and intentionally slow cycles for stickier problems; furthermore, nesting collaborative data use within high-priority initiatives may help mitigate barriers to future implementations.

Research on data-based decision making has proliferated around the world, fueled by policy recommendations and the diverse data that are now available to educators to inform their practice. Yet, many misconceptions and concerns have been raised by researchers and practitioners. To better understand the issues, a session was convened at AERA’s annual convention in 2018, followed by an analysis of the literature based on misconceptions that emerged. This commentary is an outgrowth of that exploration by providing research, theoretical, and practical evidence to dispel some of the misconceptions. Our objective is to survey and synthesize the landscape of the data-based decision making literature to address the identified misconceptions and then to serve as a stimulus to changes in policy and practice as well as a roadmap for a research agenda.

Data-informed decision making is considered important for school improvement. Working in data teams is a promising strategy for implementing data use in schools. Data teams consist of teachers and school leaders, who collaboratively analyze data to solve educational problems at their school. Studies show that school leaders can enable and hinder data use in such teams. This study aims at exploring what types of leadership behaviors are applied to support data use in data teams. The results of this study point to five key building blocks for school leaders wanting to build effective data teams in their school: (1) establishing a vision, norms, and goals (e.g., discussing vision, norms, and goals with teachers); (2) providing individualized support (e.g., providing emotional support); (3) intellectual stimulation (e.g., sharing knowledge and providing autonomy); (4) creating a climate for data use (e.g., creating a safe climate focused on improvement rather than accountability, and engaging in data discussions with teachers); and (5) networking to connect different parts of the school organization (e.g., brokering knowledge and creating a network that is committed to data use). Not only formal school leaders, but also teachers, can display these types of behavior. Finally, it is important to stress here that all these building blocks are needed to create sustainable data use practices. These building blocks can be used in a new wave of data-informed decision making in schools, in which teachers and school leaders collaboratively use a multitude of different data sources to improve education.

Higher education institutions have multiple technologic subsystems for administrative, pedagogical management, and quality purposes, which gather an immense volume of data from various sources and which do not communicate with each other. The domain of the analytic performances in education emerges from the need to aggregate multiple sources of data, which the complexity of treatment associated with the ease of mobilizing selected information will make it possible to understand reality and optimize management actions. In this paper, we present the architecture and results achieved in the development of an academic analytics aggregator of multiple sources of data on the educational activity.

Siden begynnelsen av 2000-tallet har det vært et utdanningspolitisk ønske i Norge om å kvalitetsvurdere opplæringen i skolen, og det er i den forbindelse innført obligatoriske kartleggingsprøver og nasjonale prøver. Vi vet at lærere er usikre på oppfølgingen av prøveresultatene. Som et ledd i å styrke underveisvurderingen, har Utdanningsdirektoratet lansert digitale læringsstøttende prøver med veiledninger for mellomtrinnet. En av disse prøvene, en vokabularprøve, er særlig innrettet mot den konkrete oppfølgingen av flerspråklige elever. I denne studien undersøker vi, inspirert av et kritisk case-design, hvordan åtte lærere forstått som deltakere med fullt medlemskap i et praksisfellesskap (Lave & Wenger, 1991), reflekterer over flerspråklige elevers prøveresultater. Ved å anvende Breiter og Lights (2006) begreper knyttet til læreres beslutningstaking basert på data – såkalt data-driven decision making – analyserer vi hvordan lærerne i løpet av en samtale beveger seg fra å forklare elevenes resultater på vokabularprøven til å bygge på disse forklaringene når de skal ta målrettede valg om fremtidig undervisning. Studien viser at deltakerne i løpet av samtalen utvikler felles kunnskap om flerspråklige elevers opplæringsbehov som potensielt vil kunne føre til endring i praksis. Samtidig avdekker studien at lærernes nyervervede kunnskap bare delvis er forankret i elevresultatene. Studien peker mot et behov for at skolene gir rom for strukturerte samtaler om prøveresultater hvor også flerspråklige elever blir tematisert. Siktemålet for slike samtaler må være å skape anvendbar kunnskap basert på alle elevers prøveresultater.Nøkkelord: pedagogisk bruk av prøver, data-driven decision making, praksisfellesskap, flerspråklige elever, akademisk vokabularTeachers in conversation about the use of second language learners' test results for instructional purposesAbstractOver the last two decades, the Norwegian Government has focused strongly on assessing the quality of education in schools. This focus has led to the introduction of both compulsory screening tests and national tests. Current research indicates uncertainty on the part of teachers about how to use and follow up the test results. In order to strengthen formative assessment practice and teachers’ competence in using test results to guide instruction, the Norwegian Directorate for Education and Training has introduced a series of tests in digital format, with accompanying teacher manuals, for use in grades 5-7. One of these tests, a vocabulary test, focuses particularly on supporting second language learners’ (SLLs) learning. In this study, we employ a critical case design to investigate how eight teachers, understood as full participants in a community of practice (Lave & Wenger, 1991), engage in a collective learning process around SLLs’ test results. Using Breiter and Light’s (2006) concepts related to research on teachers’ data-driven decision making, we analyze how these teachers during a conversation move from explaining SLLs test results from the vocabulary test, to making targeted decisions for future instruction. The analysis reveals that during the conversation, the participants develop shared knowledge about the academic needs of SLLs, which could potentially lead to instructional improvement. However, this knowledge is only partly grounded in the test results. The findings suggest that the schools make room for structured conversations about SLLs test results to guide future decisions. Further research should continue to investigate how teachers could improve their instruction based on test results.Keywords: instructional use of tests, data-driven decision making, community of practice, second language learners, academic vocabulary

Standards for Reporting Data to Educatorsprovides a synthesis of research and best practices of how data should be presented to educators in order to optimize the effectiveness of data use. Synthesizing over 300 sources of peer-reviewed research, expert commentary, and best practices, Rankin develops a set of data reporting standards that education data system vendors, providers, and creators can apply to improve how data is displayed for educators. The accurate and effective presentation of data is paramount to educators’ ability to successfully implement and make use of the most current knowledge in the field. This important book reveals the most effective ways to communicate data to ensure educators can use data easily and accurately.

At the end of secondary education in The Netherlands, students have to pass a final examination, consisting of an internal school-based assessment and an external national assessment. According to the Dutch inspectorate, to ensure the quality of final examinations, the discrepancy between both assessments must be less than 0.5 points (on a scale from 1 to 10). In the first part of this study, we demonstrate that these examination results are a rich source of data schools can use. We investigated the discrepancy between school and central examination grades for different groups of students and found that the discrepancies for some student groups are too high. The second part of this study focuses on the use of examination results as an important source of data in improvement planning. The results show that final examination results are underutilized and that schools rarely investigate discrepancies for certain student groups.

The purpose of this article is to examine big data and learning analytics in blended learning environments. It will examine the nature of these concepts, provide basic definitions, and identify the benefits and concerns that apply to their development and implementation. This article draws on concepts associated with data-driven decision making, which evolved in the 1980s and 1990s, and takes a sober look at big data and analytics. It does not present them as panaceas for all of the issues and decisions faced by higher education administrators, but sees them as part of solutions, although not without significant investments of time and money to achieve worthwhile benefits.

The current high-stakes testing environment has resulted in intense pressure on schools to build professional learning communities focused on data-driven decision-making (DDDM). As a result, schools and school districts are implementing systems where teachers, teacher leaders, and school leaders collaboratively analyze assessment data and use the results to inform instructional practice. One promising approach to providing teachers better guidance on using data to inform practice is the use of instructional coaches - master teachers who offer on-site and ongoing instructional support for teachers. Even though there are current studies on the various roles of instructional coaches, one prominent role that has rarely been examined is the instructional coaches' role in data-driven decision making. This qualitative case study examines the convergence of two popular school improvement policies: instructional coaching and data-driven decision making (DDDM). Building upon current large-scale research studies on DDDM as well as instructional coaching, this study examined how an instructional literacy coach in an urban, high-poverty, public charter middle school supports DDDM and how this support relates to teacher practices. Interviews, observations, and document/artifact analysis were utilized to inform this study. Findings show that while the instructional coach improves teachers' data use knowledge and skills, they also indicate that the coach's support had minimal impact on actual teaching practices. Findings also indicate that the coach possessed key attributes that deemed him `effective' in his support to teachers with DDDM: strong pedagogical and content expertise, which allowed him to gain the respect of teachers; strong interpersonal skills, which assisted him with building trusting relationships; and, a strong belief in the capacity of others to grow and develop, which helped him to develop teachers' self-efficacy. Furthermore, an analysis of the attributes of an effective instructional literacy coach may contribute to the way schools and school districts evaluate the effectiveness of their instructional coaches. Results of the study also have potential implications for federal and local policy on professional development for teachers, teacher leaders, and instructional coaches.