scholarly journals Data-driven decision-making in creating class rosters

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rebecca Wolf ◽  
Joseph M. Reilly ◽  
Steven M. Ross
Keyword(s):  
Decision Making ◽  
Student Learning ◽  
Low Income ◽  
School Leaders ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Use Of Data ◽  
Using Data ◽  
Rich Data

PurposeThis article informs school leaders and staffs about existing research findings on the use of data-driven decision-making in creating class rosters. Given that teachers are the most important school-based educational resource, decisions regarding the assignment of students to particular classes and teachers are highly impactful for student learning. Classroom compositions of peers can also influence student learning.Design/methodology/approachA literature review was conducted on the use of data-driven decision-making in the rostering process. The review addressed the merits of using various quantitative metrics in the rostering process.FindingsFindings revealed that, despite often being purposeful about rostering, school leaders and staffs have generally not engaged in data-driven decision-making in creating class rosters. Using data-driven rostering may have benefits, such as limiting the questionable practice of assigning the least effective teachers in the school to the youngest or lowest performing students. School leaders and staffs may also work to minimize negative peer effects due to concentrating low-achieving, low-income, or disruptive students in any one class. Any data-driven system used in rostering, however, would need to be adequately complex to account for multiple influences on student learning. Based on the research reviewed, quantitative data alone may not be sufficient for effective rostering decisions.Practical implicationsGiven the rich data available to school leaders and staffs, data-driven decision-making could inform rostering and contribute to more efficacious and equitable classroom assignments.Originality/valueThis article is the first to summarize relevant research across multiple bodies of literature on the opportunities for and challenges of using data-driven decision-making in creating class rosters.

scholarly journals Analysis of Decision Making Process in Moneyball: The Art of Winning an Unfair Game

The Winners ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 57
Author(s):  
Mochamad Sandy Triady ◽  
Ami Fitri Utami
Keyword(s):  
Decision Making ◽  
Excellent Result ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making ◽  
General Manager ◽  
Use Of Data ◽  
Bottom Position ◽  
Using Data ◽  
Baseball Team

Billy Beanes’s success in using data-driven decision making in baseball industry is wonderfully written by Michael Lewis in Moneyball. As a general manager in baseball team that were in the bottom position of the league from the financial side to acquire the players, Beane, along with his partner, explored the use of data in choosing the team’s player. They figured out how to determine the worth of every player.The process was not smooth, due to the condition of baseball industry that was not common with using advanced statistic in acquiring   players. Many teams still use the old paradigm that rely on experts’ judgments, intuition, or experience in decision making process. Moneyball approached that using data-driven decision making gave excellent result for Beane’s team. The team won 20 gamessequently in the 2002 season and also spent the lowest cost per win than other teams.This paper attempts to review the principles of Moneyball – The Art of Winning an Unfair Game as a process of decision making and gives what we can learn from the story in order to win the games, the unfair games.

Teacher Talk about Student Ability and Achievement in the Era of Data-Driven Decision Making

2018 ◽  
Vol 120 (4) ◽  
pp. 1-34 ◽  
Author(s):  
Amanda Datnow ◽  
Bailey Choi ◽  
Vicki Park ◽  
Elise ST. John
Keyword(s):  
Decision Making ◽  
Student Achievement ◽  
Student Learning ◽  
Data Use ◽  
Data Driven ◽  
Educational Reforms ◽  
Data Driven Decision Making ◽  
Use Of Data ◽  
Student Ability ◽  
Teacher Team

Background Data-driven decision making continues to be a common feature of educational reform agendas across the globe. In many U.S. schools, the teacher team meeting is a key setting in which data use is intended to take place, with the aim of planning instruction to address students’ needs. However, most prior research has not examined how the use of data shapes teachers’ dialogue about their students’ ability and achievement. Purpose This study examines how teachers talk about student ability and achievement in the era of data-driven decision making and how their talk is shaped by the use of data within teams, their school contexts, and broader accountability systems. Research Design The study draws on interview and observational data gathered from teacher teams in four elementary schools. In each of these schools, teachers were expected to use data to inform instructional differentiation. Data collection efforts involved regular visits to each school over the course of one year to interview teachers and conduct observations of teacher team meetings. In the process of analysis, interview transcripts and field notes were coded, and themes were extracted within and across codes. Findings Across schools, teachers used common labels (e.g., “low,” “middle,” “GATE”) to describe students of different achievement levels and the programs they were involved in. The use of labels and student categories was relational and comparative and influenced by the accountability and policy contexts in which teachers worked. At the same time, regular meetings in which teachers jointly examined data on student learning provided a space for teachers to examine students’ strengths and weaknesses on a variety of measures and talk in terms of student growth. Teachers questioned whether assessment data provided an accurate picture of student achievement and acknowledged the role of student effort, behavior, and family circumstances as important factors that were not easily measured. These discussions opened up deeper inquiry into the factors that supported or hindered student learning. The implementation of the Common Core State Standards also led some teachers to question prior categorizations of student ability. Conclusions/Recommendations The findings from this study suggest that educational reforms and policies regarding data use influence educators’ conceptions of student achievement and ability. On the one hand, accountability policies can narrow the dialogue about students. On the other hand, educational reforms and policies could also lead to new ways of thinking about student learning and to an examination of a broader range of data, and provide opportunities for professional learning.

Data capital can boost global productivity growth

2016 ◽  
Keyword(s):  
Decision Making ◽  
Market Value ◽  
Data Driven ◽  
Strategic Decisions ◽  
Business Decision ◽  
Content Type ◽  
Use Of Data ◽  
Opportunity Finding ◽  
Using Data ◽  
Do So

Subject The rise of data capital. Significance The importance of data to business decision-making and opportunity-finding has been touted in recent years. Nevertheless, many companies fail to realise the value of this asset, even as they sit on great stores of data. A recent survey of 180 large firms found that those using 'data-driven decision-making' see a 5-6% increase in output and productivity over corporations that do not make full use of data. Intangible assets make up 84.0% of the market value of S&P 500 companies, up 9.5% from ten years earlier, according to Ocean Tomo, an intellectual property bank. This trend is set to accelerate. Impacts Firms that are unprepared to take advantage of data as a strategic asset could lose their dominance. Incumbent firms able to keep competencies around data, including technological and human capital, could avoid disruption by newcomers. If able to do so, incumbent firms could keep a competitive advantage in the short and longer term. New firms failing to use data to drive strategic decisions may face challenges in attempts to grow.

Data-driven decision-making in the library

2016 ◽  
Vol 117 (1/2) ◽  
pp. 131-134 ◽  
Author(s):  
Bruce Massis
Keyword(s):  
Decision Making ◽  
Design Methodology ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Aggregated Data ◽  
Strategic Direction ◽  
Reporting Decisions ◽  
Using Data ◽  
Support Decision Making

Purpose – The purpose of this paper is to describe the current environment for libraries to consider the value of using data to support decision-making. Design/methodology/approach – This paper contains literature review and commentary on this topic that has been addressed by professionals, researchers and practitioners. Findings – In developing a library’s strategic direction, it is essential that evidentiary data be referenced to supplement the organization’s rationale for decision-making. There is an expectation by stakeholders that libraries are able to generate reports and decisions based on aggregated data for in-demand reporting. Therefore, capturing, analyzing and reporting decisions based on data are indispensable in today’s libraries. Originality/value – The value in addressing this topic is to examine the option by libraries to use data to support data-driven decision-making.

scholarly journals Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

Improving the use of analytics and big data by changing the decision-making culture

2017 ◽  
Vol 55 (10) ◽  
pp. 2074-2088 ◽  
Author(s):  
Jane Elisabeth Frisk ◽  
Frank Bannister
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Approach ◽  
Action Research Project ◽  
Content Type ◽  
Use Of Data ◽  
Support Organizations ◽  
Novel Approach ◽  
Using Data

Purpose Evolving digital technologies continue to enable new ways to collect and analyze data and this has led some researchers to claim that skillful use of data analytics and big data can radically improve a company’s performance, but that in order to achieve such improvements managers need to change their decision-making culture and to increase the degree of collaboration in the decision-making process. The purpose of this paper is to create an increased understanding of how a decision-making culture can be changed by using a design approach. Design/methodology/approach The paper presents an action research project in which the authors use a design approach. Findings By adopting a design approach organizations can change their decision-making culture, increase the degree of collaboration and also reduce the influence of power and politics on their decision-making. Research limitations/implications This paper proposes a new approach to changing a decision-making culture. Practical implications Using data analytics and big data, a design approach can support organizations change their decision-making culture resulting in better and more effective decisions. Originality/value This paper bridges design and decision-making theory in a novel approach to an old problem.

scholarly journals The Rapid Adoption of Data-Driven Decision-Making

2016 ◽  
Vol 106 (5) ◽  
pp. 133-139 ◽  
Author(s):  
Erik Brynjolfsson ◽  
Kristina McElheran
Keyword(s):  
Decision Making ◽  
Companion Paper ◽  
Census Bureau ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Manufacturing Plants ◽  
Using Data ◽  
Large Representative ◽  
The U.S ◽  
Case Based

We provide a systematic empirical study of the diffusion and adoption patterns of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for a large representative sample of manufacturing plants, we find that DDD rates nearly tripled (11%-30%) between 2005 and 2010. This rapid diffusion, along with results from a companion paper, are consistent with case-based evidence that DDD tends to be productivity-enhancing. Yet certain plants are significantly more likely to adopt than others. Key correlates of adoption are size, presence of potential complements such as information technology and educated workers, and firm learning.

Using Data-Driven Decision Making

2021 ◽  
pp. 83-99
Author(s):  
Mary Ruth Coleman ◽  
Jennifer Job
Keyword(s):  
Decision Making ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Using Data

Empowering human resource functions with data-driven decision-making in start-ups: a narrative inquiry approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deepkumar Varma ◽  
Pankaj Dutta
Keyword(s):  
Decision Making ◽  
Narrative Inquiry ◽  
Technology Implementation ◽  
Data Driven ◽  
Structured Interviews ◽  
New Paradigm ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Start Up ◽  
Start Ups

Purpose Across industries, firms want to adopt data-driven decision-making (DDDM) in various organizational functions. Although DDDM is not a new paradigm, little is known about how to effectively implement DDDM and which problem areas to focus on in these functions. This study aims to enable start-ups to use DDDM in human resources (HR) by studying five HR domains using a narrative inquiry technique and aims to guide managers and HR practitioners in start-ups to enable data-driven decisions in HR. Design/methodology/approach This study adopts the narrative inquiry technique by conducting semi-structured interviews with HR practitioners and senior members handling HR functions in start-ups. Interview memos are thematically analyzed to identify repeated ideas, concepts or elements that become apparent. Findings The study findings indicate that start-ups need to have canned operational reports with right attributes in each of these HR domains, which members should use when performing HR tasks. Few metrics, like cost-to-hire in recruitment, distinctly surfaced relatively higher in importance that each start-up, should compute and use in decision-making. Practical implications Managers, HR practitioners and information technology implementation teams will be able to consume the findings to effectively design or evaluate HR processes or systems that empower decision-making in a start-up. Originality/value Start-ups have a fast-paced culture where creativity, relationships and nimbleness are valued. Prevalent decision models of larger organizations are not suitable in start-ups’ environments. This study, being cognizant of these nuances, takes a fresh approach to guide start-ups adopt DDDM in HR and identify key problem areas where decision-making should be enabled through data.

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