scholarly journals Data-Driven Decision Making: Teachers' Use of Data in the Classroom

2013 ◽  
Author(s):  
◽  
Tammy Wu Moriarty
Keyword(s):  
Decision Making ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Use Of Data

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.

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 Data-driven decision making in early education: Evidence From North Carolina’s Pre-K program

2019 ◽  
Vol 27 ◽  
pp. 18
Author(s):  
Michael Harris Little ◽  
Lora Cohen-Vogel ◽  
James Sadler ◽  
Becca Merrill
Keyword(s):  
Decision Making ◽  
Data Sharing ◽  
Early Education ◽  
Screening Tools ◽  
Data Driven ◽  
Policy And Practice ◽  
Data Driven Decision Making ◽  
Use Of Data ◽  
Depth Interviews ◽  
Shed Light

The purpose of this study is to shed light on the use of data in early education settings—specifically, North Carolina’s Pre-K program. In this mixed-methods study, we draw upon in-depth interviews and survey data to examine (1) the types of data available to educators in Pre-K, (2) the ways in which data are intended to be used, (3) how data are reportedly used, and (4) the facilitators and inhibitors of effective data-driven decision making. Our findings reveal that Pre-K settings are data-rich environments, often with informal data collected through developmental screening tools and formative assessment systems. We find that engagement with and use of these data for instruction is variable. Finally, we find data sharing between grades is inconsistent, but an important factor predicting data sharing is co-location of Pre-K programs within elementary school buildings. We consider our findings in the context of existing academic literature and discuss the implications for policy and practice. 

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.

scholarly journals Enabling Smart Manufacturing Technologies for Decision-Making Support

2016 ◽  
Author(s):  
Moneer Helu ◽  
Don Libes ◽  
Joshua Lubell ◽  
Kevin Lyons ◽  
K. C. Morris
Keyword(s):  
Decision Making ◽  
Manufacturing Systems ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Advanced Manufacturing ◽  
Data Driven Decision Making ◽  
Use Of Data ◽  
Manufacturing Technologies ◽  
Common Understanding ◽  
Decision Making Support

Smart manufacturing combines advanced manufacturing capabilities and digital technologies throughout the product lifecycle. These technologies can provide decision-making support to manufacturers through improved monitoring, analysis, modeling, and simulation that generate more and better intelligence about manufacturing systems. However, challenges and barriers have impeded the adoption of smart manufacturing technologies. To begin to address this need, this paper defines requirements for data-driven decision making in manufacturing based on a generalized description of decision making. Using these requirements, we then focus on identifying key barriers that prevent the development and use of data-driven decision making in industry as well as examples of technologies and standards that have the potential to overcome these barriers. The goal of this research is to promote a common understanding among the manufacturing community that can enable standardization efforts and innovation needed to continue adoption and use of smart manufacturing technologies.

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