scholarly journals We're The Economist's data team. Ask us anything!

The Winnower ◽  
2021 ◽  
Author(s):  
theeconomist ◽  
r/Science
Keyword(s):  
Data Team

Building a Big Data Team

2016 ◽  
pp. 249-252
Author(s):  
Scott Shaw ◽  
Andreas François Vermeulen ◽  
Ankur Gupta ◽  
David Kjerrumgaard
Keyword(s):  
Big Data ◽  
Data Team

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.

Improving teacher education in the Netherlands: data team as learning team?

2016 ◽  
Vol 39 (3) ◽  
pp. 320-339 ◽  
Author(s):  
E. D. Bolhuis ◽  
K. Schildkamp ◽  
J. M. Voogt
Keyword(s):  
Teacher Education ◽  
The Netherlands ◽  
Data Team ◽  
Learning Team

scholarly journals What's My Superpower? by A. Johnston

2019 ◽  
Vol 8 (3) ◽  
Author(s):  
Kirk MacLeod
Keyword(s):  
Positive Attitude ◽  
Open Data ◽  
Book Clubs ◽  
Open Government ◽  
Northern Canada ◽  
Great Books ◽  
Data Team ◽  
Positive Message ◽  
The Government ◽  
Government Portal

Johnston, Aviaq. What's My Superpower? Illustrated by Tim Mack, Inhabit Media. 2017. Following her debut novel, Those Who Run in the Sky, Johnston and illustrator Tim Mack have put together the delightful story of a young Inuit girl named Nalvana who sees superpowers in all her friends, but isn't sure if she has one of her own. The book has a bright and colourful style highlighting a young girl's world in the Canadian Territories. Following Nelvana through her story, the reader is introduced to her mother, her unnamed (but adorable) dog, her friends and her community. Her infectious smile and positive attitude come through on every page and as she discovers each of her friends’ superpowers, she, and the reader, begin to wonder about her own.  The book mixes Inuktitut terms throughout, and includes a glossary at the end. An excellent introduction to a young girl's world in Northern Canada and with a positive message and vibrant illustrations, the book would work well for young schoolchildren as well as those who might be interested in a view of Canada's different communities and questions about their own superpowers. A great read! Highly recommended: 4 out of 4 stars Reviewer: Kirk MacLeod Kirk is the Open Data Team Lead for the Government of Alberta’s Open Government Portal. A Life-Long reader, he moderates two book clubs and is constantly on the lookout for new great books!

scholarly journals The Nature of Teacher Learning in Collaborative Data Teams

2016 ◽  
Author(s):  
Robert Michaud
Keyword(s):  
Teacher Learning ◽  
Conceptual Models ◽  
Empirical Studies ◽  
Data Use ◽  
Educational Leaders ◽  
Learning Opportunities ◽  
Data Teams ◽  
Key Factors ◽  
Data Team ◽  
Effective Data Use

As data teams have grown in popularity in recent years, they have been increasingly looked to by educational researchers because of the tantalizing prospect of combining teachers’ on the job professional development with increased and effective data use to drive instruction. Data teams have been increasingly implemented within schools by educational leaders attempting to take advantage of what teachers learn from each other in the context of a data team. Many conceptual models of data team function have been proposed, but few empirical studies have examined how teachers learn from collaborating with each other in a data team. This paper explores the nature of teachers’ learning in data teams, uncovering key factors that impact the learning opportunities created by collaborating around student data.

scholarly journals Spreading the Word: Boundary Crossers Building Collective Capacity for Data Use

2019 ◽  
Vol 121 (1) ◽  
pp. 1-45
Author(s):  
Mireille D. Hubers ◽  
Cindy L. Poortman ◽  
Kim Schildkamp ◽  
Jules M. Pieters
Keyword(s):  
Common Ground ◽  
Data Use ◽  
Background Information ◽  
Boundary Crossing ◽  
Educational Problem ◽  
Team Members ◽  
Improvement Plan ◽  
Data Team ◽  
Using Data ◽  
Collective Capacity

Background/Context The data team intervention was designed to support schools in using data while developing a solution to an educational problem. The participating data team members are responsible for building collective capacity within their school for using data and implementing actions related to the improvement plan. This can be challenging, because although they have gained knowledge and experience with data use and the educational problem, their colleagues who were not on the data team have not. As a result, there is a heightened risk for discontinuity between the behaviors of such colleagues and of the data team members: Colleagues will not automatically use data nor implement the actions for improvement. These discontinuities are referred to as boundaries. To establish common ground with their colleagues, data team members need to act as boundary crossers by brokering their knowledge. Purpose The present study used a process view to determine how data team members acted as boundary crossers by studying what content they had brokered, the level at which they had addressed that content, and what activities they had used to cross boundaries. Intervention Data teams consist of six to eight educators who collaboratively learn how to use data to analyze and address an educational problem at their school. They work following a cyclical procedure. Research Design A longitudinal qualitative case study was conducted in four Dutch schools that implemented the data team intervention. Data Collection and Analysis Artifacts were collected and all team members were interviewed twice. Log files, minutes of the meetings, and progress reports were used to obtain a complete picture of boundary crossing and to provide background information. A coding scheme was used in order to determine what content was brokered, the level at which the content was addressed, and the activities used to broker the content. Findings/Results Findings illustrated that team members mainly brokered knowledge about the educational problem and data use as applied to the educational problem, but rarely about data use in general. Overall, content was almost exclusively addressed at the level of awareness, indicating that only basic information was brokered. Conclusions/Recommendations Successful boundary crossing cannot be taken for granted: Team members brokered their knowledge in ways less likely to be effective. When they receive additional support for this, they are likely to increase their team's effectiveness in building school-wide capacity for both data use and the implementation of actions related to the improvement plan.

scholarly journals Productive myopia: Racialized organizations and edtech

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110504
Author(s):  
Roderic Crooks
Keyword(s):  
Public Schools ◽  
Los Angeles ◽  
Digital Data ◽  
Data Driven ◽  
Data Intensive ◽  
For Profit ◽  
Goods And Services ◽  
Data Team ◽  
Racial Projects ◽  
Charter Management Organization

This paper reports on a two-year, field-based study set in a charter management organization (CMO-LAX), a not-for-profit educational organization that operates 18 public schools exclusively in the Black and Latinx communities of South and East Los Angeles. At CMO-LAX, the nine-member Data Team pursues the organization's avowed mission of making public schools data-driven, primarily through the aggregation, analysis, and visualization of digital data derived from quotidian educational activities. This paper draws on the theory of racialized organizations to characterize aspects of data-driven management of public education as practiced by CMO-LAX. I explore two examples of how CMO-LAX shapes data to support racial projects: the reconstruction of the figure of chronic truants and the incorporation of this figure in a calculative regime of student accomplishment. Organizational uses of data support a strategy I call productive myopia, a way of pursuing racial projects via seemingly independent, objective quantifications. This strategy allows the organization to claim to mitigate racial projects and, simultaneously, to accommodate them. This paper concludes by arguing for approaches to research and practice that center racial projects, particularly when data-intensive tools and platforms are incorporated into the provision of public goods and services such as education.

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.

scholarly journals Opening the black box: knowledge creation in data teams

2016 ◽  
Vol 1 (1) ◽  
pp. 41-68 ◽  
Author(s):  
Mireille D. Hubers ◽  
Cindy L. Poortman ◽  
Kim Schildkamp ◽  
Jules M. Pieters ◽  
Adam Handelzalts
Keyword(s):  
Knowledge Creation ◽  
Added Value ◽  
Data Teams ◽  
Content Type ◽  
Team Members ◽  
Creation Process ◽  
Team Meetings ◽  
Seci Model ◽  
Data Team ◽  
Personal Engagement

Purpose – In this study, Nonaka and Takeuchi’s socialization, externalization, combination and internalization (SECI) model of knowledge creation is used to gain insight into the process of knowledge creation in data teams. These teams are composed of school leaders and teachers, who work together to improve the quality of education. They collaboratively create knowledge related to data use and to an educational problem they are studying. The paper aims to discuss these issues. Design/methodology/approach – A qualitative micro-process case study was conducted for two data teams. The modes, transitions and content of the knowledge creation process were analyzed for all data team meetings over a two-year period. In addition, all team members were interviewed twice to triangulate the findings. Findings – Results show that the knowledge creation process was cyclical across meetings, but more iterative within meetings. Furthermore, engagement in the socialization and internalization mode provided added value in this process. Finally, the SECI model clearly differentiated between team members’ processes. Team members who engaged more often in the socialization and internalization modes and displayed more personal engagement in those modes gained greater and deeper knowledge. Research limitations/implications – The SECI model is valuable for understanding how teams gain new knowledge and why they differ in those gains. Practical implications – Stimulation of active personal engagement in the socialization and internalization mode is needed. Originality/value – This is one of the first attempts to concretely observe the process of knowledge creation. It provides essential insights into what educators do in professional development contexts, and how support can best be provided.

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