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Article
Publication date: 4 February 2014

Martin McCracken

138

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Education + Training, vol. 56 no. 1
Type: Research Article
ISSN: 0040-0912

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Book part
Publication date: 24 October 2023

Rodanthi Tzanelli

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The New Spirit of Hospitality
Type: Book
ISBN: 978-1-83753-161-5

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Book part
Publication date: 20 June 2017

David Shinar

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Traffic Safety and Human Behavior
Type: Book
ISBN: 978-1-78635-222-4

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Article
Publication date: 30 March 2010

1363

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Accounting, Auditing & Accountability Journal, vol. 23 no. 3
Type: Research Article
ISSN: 0951-3574

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Article
Publication date: 28 March 2008

816

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Accounting, Auditing & Accountability Journal, vol. 21 no. 3
Type: Research Article
ISSN: 0951-3574

Available. Open Access. Open Access
Article
Publication date: 9 March 2020

Rebecca Wolf, Joseph M. Reilly and Steven M. Ross

This 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…

2568

Abstract

Purpose

This 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/approach

A 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.

Findings

Findings 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 implications

Given 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/value

This 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.

Details

Journal of Research in Innovative Teaching & Learning, vol. 14 no. 2
Type: Research Article
ISSN: 2397-7604

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