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The purpose of this paper (case study) is to capture a novel school culture that values instructional leadership (school leaders and teachers) and serves students in ways that create a culturally responsive and socially just schooling environment.

A qualitative methodology was best suited for the collection and analysis of data with the hope that the study will assist practitioners in seeing the connective threads that bind school leaders, teachers, students and parents in an organizational cultural shift that is student focused. Interviews and observations of professional learning communities, meetings and classrooms were the types of data collected and analyzed.

The principal and assistant principal were professionally and ethically challenged with an all too familiar problem – 30 percent of their Latinx and economically disadvantaged students scored below proficient in reading comprehension. To address this opportunity gap, consideration was given to data-informed decision-making; professional learning communities; and distributed leadership for social justice. Findings suggested that problems of practice are solved when educators engage in a continuous culture of learning through authentic dialogue focused on student data with an eye on equity.

Although research demonstrates that school improvement works best when principals distribute leadership to teachers, principals tend to maintain the share of the responsibility. Examples of instructional leadership beyond the school principal are rare. This case study provides an example of how principals can build leadership capacity in teachers and develop them to be instructional leaders.

Evidence-informed practice (EIP), broadly conceived as a data and research-based approach to enhance practice, has recently come to the fore of the Irish education system. With changes to the structure and duration of professional education over the last decade, most notably Initial Teacher Education (ITE) programs, coupled with the implementation of a school leadership framework upon which a mandatory data-informed school improvement process of school self-evaluation (SSE) is based, multiple reforms connected to EIP have been introduced. Furthermore, in terms of compulsory education, assessment practices at the lower secondary level have also been significantly reformed. EIP has now become a core element of almost all educational reform initiatives in Ireland. This is a remarkable achievement given that prior to the Education Act (Government of Ireland, 1998) the conception that data and research-informed decision-making should form a core component part of school life was rarely conceived if at all in the policy discourse of educational reform. We draw on the Malin et al. (2020) interpretation of Hoods (1998) social cohesion/regulation matrix to describe and classify the Irish system. The chapter concludes with a discussion of key lessons for policy and practice based on Ireland's experience over the course of the last decade.

The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics?

A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education.

The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching.

The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.

The purpose of this paper is to: first, explain why a new model of the provision of welfare services to citizens arises from the digital society; second explore some core elements of the competition between the new model of the provision of welfare services and the classic ideal model of the professionals’ provision of welfare services; third, suggest why it is most likely that the two models of the provision of services are combined into a symbiotic co-evolution scenario; and fourth, examine why and how this symbiotic co-evolution scenario results in new participatory spaces for the main actors associated with the provision of welfare services.

The review of the literature examines how the new model for the provision of welfare services facilitated by big data challenges the traditional professional model for the provision of welfare services. The authors use the Danish case to illustrate a number of themes related to this looking at the hospital sector as an example.

The proposition is that a symbiotic co-evolution scenario will emerge. A mix of the classic ideal model and practice of the service professionals’ provision of services and the digital society’s model of the provision of services is the most likely scenario in the years to come. Furthermore, Data-driven management (DDM) as an integrated key element in a symbiotic co-evolution creates (opens up) participatory environments and spaces for the main actors and agents associated with the provision of welfare services to the citizens.

DDM’s impact on the provision of welfare services is still being realised and worked out, and more empirical research is needed before it is possible to point at the most likely scenario. However, according to the authors’ analytical framework, the institutional logics perspective, as presented in Section 2, a symbiotic co-evolution is most likely such that DDM will constitute a new logic within the provision of welfare services on the basis of which citizens as end-users could be provided with welfare services, but it is not likely that the new logic of DDM can displace the classic service professionals’ model of the provision of welfare services. Therefore, the new logic of DDM will be combined with and integrated into the existing logics within service provision, such as the Weberian bureaucracy, the Street-Level Bureaucracy, the New Public Governance and the Market. In spite of this, DDM can successfully be promoted by international management consulting firms, as a management concept which can remedy all the problems of the classic service professionals’ model of the provision of welfare services to citizens.

As a consequence of this, new relationships among professionals, data analytics, (middle) managers and citizens will be created regarding the provision of welfare services. Considering the new participatory environments and spaces and the new relationships among the classic service professionals, the data analytics, the (middle) managers and the citizens as end-users, the provision of welfare services may become an arena for negotiation of a new future model of the provision of welfare services to citizens.

The digital society has emerged from and developed further via: digitising, online information in almost real time, algorithms, data-informed decision-making processes, DDM and, ultimately, big data. The authors expect to see further digitising, more sophisticated algorithms and more big data. The authors suggest that a new model of the provision of welfare services to citizens will emerge from the development of the digital society. The authors also suggest that this new model will compete with the classic model of the provision of welfare services.

Tiered systems may hold particular benefit for students with intensive intervention needs, as this continuum of supports is ideally comprised of evidence-based practices. In this chapter, we explore three intensive interventions: functional assessment-based interventions (FABI), First Step to Success, and Read 180 as they feature a range of practices and programs to meet students' academic, behavioral, and social needs. We define and describe each intervention, including information on essential features necessary for drawing valid inferences: treatment integrity, social validity, as well as student performance. We also provide examples of supporting evidence, featuring treatment-outcome studies documenting intervention effectiveness. We close with clarifications and considerations for meeting the multiple needs of students requiring intensive intervention efforts.

This paper aims to describe how data were used to design and evaluate new services at Notre Dame and how data were used to evaluate changes in user behavior at Emory University.

Each library (Emory, Virginia Polytechnic Institute and Notre Dame) describe sources, methods and applications of data, gathered through a variety of sources to plan and evaluate new services and user satisfaction.

There is no one perfect approach to data-informed decision making; every approach has advantages, costs and disadvantages. What is important is that resource sharing practitioners become comfortable with a variety of assessment tools.

The user experience is increasingly recognized as a critical part of any service or library product but most resource sharing practitioners have little to no background in assessment and statistics. This article provides an introduction to methods, sources and applications of data specifically applied to interlibrary loans.

The purpose of this paper is to advocate for change in academic library space assessment and use philosophy in favor of a more user-centered approach emphasizing space designed for and by users themselves. This goal is achieved by analyzing the implementation of a recent space assessment project at the University of Northern Colorado Libraries to investigate specific patterns of library space utilization.

This paper presents a case study for which data were collected through a multi-method approach, including flip chart and whiteboard questions, brief semi-structured “tabling” interviews, and more in-depth “roving” interviews.

The current library literature on space assessment does not encompass broader, more holistic approaches to how library space is used by students, faculty, staff and community users. The findings from this study highlight the diversity of ways patrons may use an academic library, many of which are related to academic work. However, visitors also come to the library for other purposes, such as socializing or attending an event. It is imperative that the space be adequately equipped to meet varied visitor needs and to create a welcoming environment for all patrons.

The paper has several implications for planning and managing the operations of medium-sized academic libraries. It contributes to the larger conversation in higher education about the importance of user research for enhancing visitor experience through data-informed decision-making. Furthermore, the project it details is not an isolated assessment effort but part of the library’s ongoing space assessment work.

Applications for tracking and managing classroom behavior have become increasingly commonplace, thus making it possible to incorporate nonacademic data into collaborative problem-solving and school improvement. Whether or how these platforms might support such aims, however, is not known. Accordingly, this study explores practices involving these applications, focusing especially on problem-solving among educators and with students' families.

This comparative case study took place in three schools. In total, 34 semistructured interviews were conducted with teachers and school leaders. Analysis included qualitative coding as well as the development of within- and cross-case summaries.

Schools varied greatly when it came to using behavior management platforms as a part of problem-solving. At a basic level, it was not uncommon for educators to use behavioral data for classroom troubleshooting or check-ins with students and transactional communications with families. However, only two schools attempted to use behavioral data for more systemic, “big picture” problem-solving, such as to make discipline policies more equitable or to improve teacher practices. The richness of collaboration with families seemed especially shaped by how and how frequently data were shared (e.g. automated notifications and paper printouts).

Empirical research about behavior management applications has been limited and focused only at the classroom level. The present study contributes new knowledge about the school-level implications of these platforms, while also expanding conversations about how behavioral data may be incorporated into data-informed problem-solving. Implications for leadership and theory are also discussed.

An increasing number of schools and districts across the US are requiring teachers to collaborate for the purpose of data‐driven decision making. Research suggests that both data use and teacher collaboration are important ingredients in the school improvement process. Existing studies also reveal the complexities of teacher collaboration and the importance of context in shaping teachers’ collaborative work, especially with data. Yet, the intersection of teacher collaboration and data use has been understudied. The purpose of this paper is to examine the affordances and constraints that exist in the context of established teacher collaboration time for the purposes of data‐driven decision making.

The paper draws upon qualitative case study data gathered in six schools that structured teacher time for collaboration on data use.

An analysis of the data revealed that a variety of leadership activities and organizational conditions shaped teachers’ collaborative work with data. These included leadership focused on thoughtful use of data and the framing of data‐driven decision making in terms of collective responsibility; the establishment of norms for teacher collaboration; the implementation of data discussion protocols; and teacher groupings and subject matter subcultures.

Knowing how and when a leadership activity or organizational condition becomes either an affordance or a constraint to teacher collaboration around data use has important implications for leadership and educational change. The findings of this study also help to lay the groundwork for future research regarding teacher collaboration around data use.