Investigating the Use of Data to Inform Instructional Leadership and Build Data-Use Capacity: Case Studies in Kuwaiti Context

2022 ◽  
pp. 1-19
Author(s):  
Amal Abdulwahab Alsaleh
Keyword(s):  
Instructional Leadership ◽  
Case Studies ◽  
Data Use ◽  
Use Of Data

scholarly journals Data Use Among Principals and Teachers: Divergent Paths or Common Ground?

2020 ◽  
Vol 5 (2) ◽  
pp. 63-76
Author(s):  
Waheeb Albiladi ◽  
Kara Lasater ◽  
Ed Bengtson
Keyword(s):  
School Districts ◽  
Common Ground ◽  
Data Use ◽  
Research Approach ◽  
Central State ◽  
Use Of Data ◽  
South Central ◽  
Multiple Cycles ◽  
The Common ◽  
Depth Interviews

This study examines teachers’ and administrators’ use of data to inform their practice in one south-central state. Using a qualitative research approach, the study involved 76 educators representing eight school districts. Data were collected using focus groups with teachers and in-depth interviews with school principals. Data were inductively and deductively analyzed using multiple cycles of coding. Analysis of data revealed three themes that exposed differences in the use of data by teachers and administrators: the challenges of data use, the “levels” at which data are viewed (micro and macro lenses), and the value placed on formal and informal data. Findings suggest that by understanding the differences between teachers’ and administrators’ perspectives on data use and recognizing the common ground that unites their perspectives, schools can create data cultures that foster shared expectations, collaboration, and trust between teachers and administrators.

Leadership to Support and Sustain Data Use in Data Teams

2018 ◽  
pp. 98-118 ◽  
Author(s):  
Kim Schildkamp ◽  
Cindy Louise Poortman
Keyword(s):  
School Improvement ◽  
Leadership Behaviors ◽  
School Leaders ◽  
Data Use ◽  
Intellectual Stimulation ◽  
Data Teams ◽  
Use Of Data ◽  
Data Team ◽  
Different Parts

This chapter focuses on how school leaders can support the use of data in data teams with the data team intervention, a step-by-step systematic approach to school improvement. First, the data team professional development intervention is described and an example of a data team in action is provided. Next, the authors closely examine the role of the school leader in supporting the use of data in data teams. Several leadership behaviors that are important to support data teams are described: developing a vision, norms, and goals for data use; providing individualized support; providing intellectual stimulation; creating a climate for data use; and, networking to connect different parts of the organization. Concrete examples are provided with regard to how these behaviors are demonstrated in data teams. The chapter ends with a checklist and reflection tool, which school leaders can use to reflect on their own leadership behaviors with regard to supporting data use in data teams.

scholarly journals All for one and one for all? Leadership approaches in complementary schools

2020 ◽  
pp. 174114322097128
Author(s):  
Linet Arthur ◽  
Ana Souza
Keyword(s):  
Instructional Leadership ◽  
School Leadership ◽  
Case Studies ◽  
Questionnaire Survey ◽  
Primary Schools ◽  
Small Primary ◽  
Using Data ◽  
The Uk ◽  
Leadership Approaches

This article explores the nature of leadership in Brazilian complementary schools in the UK. Such schools are typically parent-driven, voluntary and financially vulnerable. Using data from a questionnaire survey ( n=14; more than three-quarters of Brazilian complementary schools) and three in-depth case studies, leadership is examined in relation to five established approaches: directive, instructional, transformational, distributed and collaborative. The study found that the size of the school and the personality of the leader appeared to influence the type of leadership adopted. In terms of effectiveness, a combination of instructional leadership with an approach that motivated staff and volunteers (whether directive, collaborative or transformational, depending on the school’s circumstances) seemed most appropriate to the context of complementary schools. The research illustrates the complexity of school leadership and the overlap between different models. Leadership flexibility was important in responding to the needs of staff, students and parents. The findings are transferable to mainstream schools with contexts similar to those of complementary schools, particularly small primary schools and free schools.

Teaching Research and Data Use in the Education Doctorate

2019 ◽  
pp. 194277511987223 ◽  
Author(s):  
William A. Firestone ◽  
Jill Alexa Perry ◽  
Andrew S. Leland ◽  
Robin T. McKeon
Keyword(s):  
Case Studies ◽  
Data Use ◽  
Education Doctorate ◽  
Practical Research ◽  
Teaching Research ◽  
Research Findings

Schools now face a sea of “evidence”—supposedly validated products, research findings, and test, demographic, and teacher-generated data—that leaders must use. How have recent reforms to educational doctorate (EdD) programs addressed these demands? Case studies of four exemplary EdD programs illustrate how the better ones help graduates learn to use evidence. These programs have well-developed strategies for teaching students to find, assess, and conduct practical research. While they provide opportunities for students to share their work with users, they rarely provide the intellectual tools and frameworks to think about putting different kinds of evidence into practice, even in leadership courses.

On meeting students where they are: Teacher judgment and the use of data in higher education

2017 ◽  
Vol 15 (3) ◽  
pp. 321-338 ◽  
Author(s):  
Gina Schouten
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Big Data ◽  
Data Use ◽  
Statistical Information ◽  
Educational Context ◽  
Ongoing Debate ◽  
Moral Hazards ◽  
Use Of Data ◽  
Legal Context

It is treated as a truism that teaching well requires ‘meeting students where they are’. Data enable us to know better where that is. Data can improve instructional practice by informing predictions about which pedagogies will be most successful for which students, and it can improve advising practice by informing predictions about which students are likely to thrive on which pathways moving forward. But moral hazards lurk, and these have been highlighted especially in response to the burgeoning use of new data mining technologies to produce ‘big data’. This article explores the ethics of data use in higher education. I consider the ethics of aggregate data as a tool for meeting students where they are, comparing it to an ongoing debate about the use of statistics in the legal context. The comparison generates two important insights: First, even the most viable moral concerns about using statistical information in the educational context are not deal-breakers: Those concerns should lead us to exercise careful judgment in the use of statistical information but do not justify eschewing that information altogether. Second, surprisingly, those viable moral concerns show big data to have a moral advantage over traditional little data, suggesting that some of the resistance to the use of big data in education is either unfounded or at least needs to be balanced against the moral advantages big data offer.

scholarly journals Graded Modal Dependent Type Theory

2021 ◽  
pp. 462-490
Author(s):  
Benjamin Moon ◽  
Harley Eades III ◽  
Dominic Orchard
Keyword(s):  
Case Studies ◽  
Type Theory ◽  
Data Use ◽  
Type Checking ◽  
Fine Grained ◽  
Dependent Type Theory ◽  
Checking Procedure ◽  
Dependent Type

AbstractGraded type theories are an emerging paradigm for augmenting the reasoning power of types with parameterizable, fine-grained analyses of program properties. There have been many such theories in recent years which equip a type theory with quantitative dataflow tracking, usually via a semiring-like structure which provides analysis on variables (often called ‘quantitative’ or ‘coeffect’ theories). We present Graded Modal Dependent Type Theory (Grtt for short), which equips a dependent type theory with a general, parameterizable analysis of the flow of data, both in and between computational terms and types. In this theory, it is possible to study, restrict, and reason about data use in programs and types, enabling, for example, parametric quantifiers and linearity to be captured in a dependent setting. We propose Grtt, study its metatheory, and explore various case studies of its use in reasoning about programs and studying other type theories. We have implemented the theory and highlight the interesting details, including showing an application of grading to optimising the type checking procedure itself.

scholarly journals Facilitators for using data from a quality registry in local quality improvement work: a cross-sectional survey of the Danish Cardiac Rehabilitation Database

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e028291 ◽  
Author(s):  
Cecilie Lindström Egholm ◽  
Charlotte Helmark ◽  
Jan Christensen ◽  
Ann Catrine Eldh ◽  
Ulrika Winblad ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Cardiac Rehabilitation ◽  
Regression Coefficient ◽  
Data Use ◽  
Cross Sectional ◽  
Frontline Staff ◽  
Quality Registry ◽  
Use Of Data ◽  
Personal Motivation ◽  
Local Quality

ObjectivesTo investigate use of data from a clinical quality registry for cardiac rehabilitation in Denmark, considering the extent to which data are used for local quality improvement and what facilitates the use of these data, with a particular focus on whether there are differences between frontline staff and managers.DesignCross-sectional nationwide survey study.Setting, methods and participantsA previously validated, Swedish questionnaire regarding use of data from clinical quality registries was translated and emailed to frontline staff, mid-level managers and heads of departments (n=175) in all 30 hospital departments participating in the Danish Cardiac Rehabilitation Database. Data were analysed descriptively and through multiple linear regression.ResultsSurvey response rate was 58% (101/175). Reports of registry use at department level (measured through an index comprising seven items; score min 0, max 7, where a low score indicates less use of data) varied significantly between groups of respondents: frontline staff mean score 1.3 (SD=2.0), mid-level management mean 2.4 (SD=2.3) and heads of departments mean 3.0 (SD=2.5), p=0.006. Overall, department level use of data was positively associated with higher perceived data quality and usefulness (regression coefficient=0.22, p=0.019), management request for data (regression coefficient=0.40, p=0.008) and personal motivation of the respondent (regression coefficient=1.63, p<0.001). Among managers, use of registry data was associated with data quality and usefulness (regression coefficient=0.43, p=0.027), and among frontline staff, reported data use was associated with management involvement in quality improvement work (regression coefficient=0.90, p=0.017) and personal motivation (regression coefficient=1.66, p<0.001).ConclusionsThe findings suggest relatively sparse use of data in local quality improvement work. A complex interplay of factors seem to be associated with data use with varying aspects being of importance for frontline staff and managers.

Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles

2021 ◽  
Author(s):  
Jennifer Dukarski ◽  
Keyword(s):  
Autonomous Vehicles ◽  
Data Privacy ◽  
Data Use ◽  
Legal Issues ◽  
Security And Privacy ◽  
Use Of Data ◽  
Vehicle Data ◽  
Legal Challenges ◽  
The Us ◽  
Infrastructure Services

Modern automobiles collect around 25 gigabytes of data per hour and autonomous vehicles are expected to generate more than 100 times that number. In comparison, the Apollo Guidance Computer assisting in the moon launches had only a 32-kilobtye hard disk. Without question, the breadth of in-vehicle data has opened new possibilities and challenges. The potential for accessing this data has led many entrepreneurs to claim that data is more valuable than even the vehicle itself. These intrepid data-miners seek to explore business opportunities in predictive maintenance, pay-as-you-drive features, and infrastructure services. Yet, the use of data comes with inherent challenges: accessibility, ownership, security, and privacy. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles examines some of the pressing questions on the minds of both industry and consumers. Who owns the data and how can it be used? What are the regulatory regimes that impact vehicular data use? Is the US close to harmonizing with other nations in the automotive data privacy? And will the risks of hackers lead to the “zombie car apocalypse” or to another avenue for ransomware? This report explores a number of these legal challenges and the unsettled aspects that arise in the world of automotive data.

Factors Influencing the Functioning of Data Teams

2015 ◽  
Vol 117 (4) ◽  
pp. 1-42 ◽  
Author(s):  
Kim Schildkamp ◽  
Cindy Poortman
Keyword(s):  
Professional Development ◽  
Decision Making ◽  
Data Use ◽  
Quality Data ◽  
Data Teams ◽  
Use Of Data ◽  
Data Team ◽  
Factors Influencing ◽  
Team Characteristics ◽  
Data Based Decision Making

Background Data-based decision making can lead to increased student achievement; however, schools struggle with the implementation of data-based decision making. Professional development in the use of data is therefore urgently needed. However, professional development is often ineffective in terms of improving the knowledge, skills, and attitude of the receiver. Purpose We need a more fundamental understanding of how we can increase the effectiveness of data-use-related professional development. This study therefore focuses on the factors influencing a professional development intervention for data-based decision making: the data team procedure. Data teams are teams of teachers and school leaders who collaboratively learn how to use data, following a structured approach and guided by a facilitator from the university. Based on an extensive literature review, we developed a data use framework in which the use of data is influenced by data characteristics, school organization characteristics, and user and team characteristics. Research Design We conducted case studies. Data Collection We focused on observing in depth the factors that influence the work of the data teams and interviewing the data team members about these factors. Four data teams of six schools for upper secondary education were followed over a period of 2 years. We observed and analyzed 34 meetings and analyzed 23 interviews, combined with our field notes. Although this pilot study only permits analytical generalization of the findings, the findings provide more in-depth insight into the factors that enable and hinder interventions, focusing on supporting collaborative data use in schools. Findings The results show that several data characteristics (access and availability of high-quality data), school organizational characteristics (a shared goal, leadership, training and support, involvement of relevant stakeholders), and individual and team characteristics (data literacy, pedagogical content knowledge [PCK], organizational knowledge, attitude, and collaboration) influence the use of data in data teams. The results also show that these influencing factors are interrelated. Conclusions Schools need support in all aspects of the use of data (from formulation of a problem definition to taking action based on the data). This study can form a starting point for larger studies into the factors influencing these types of professional development interventions to ensure effective implementation and sustainability.

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