Supporting the initial work of evidence-based improvement cycles through a data-intensive partnership

IF 1.6 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE Information and Learning Sciences Pub Date : 2021-07-29 DOI:10.1108/ils-09-2020-0212
Alex J. Bowers, Andrew E. Krumm
{"title":"Supporting the initial work of evidence-based improvement cycles through a data-intensive partnership","authors":"Alex J. Bowers, Andrew E. Krumm","doi":"10.1108/ils-09-2020-0212","DOIUrl":null,"url":null,"abstract":"\nPurpose\nCurrently, in the education data use literature, there is a lack of research and examples that consider the early steps of filtering, organizing and visualizing data to inform decision-making. The purpose of this study is to describe how school leaders and researchers visualized and jointly made sense of data from a common learning management system (LMS) used by students across multiple schools and grades in a charter management organization operating in the USA. To make sense of LMS data, researchers and practitioners formed a partnership to organize complex data sets, create data visualizations and engage in joint sensemaking around data visualizations to begin to launch continuous improvement cycles.\n\n\nDesign/methodology/approach\nThe authors analyzed LMS data for n = 476 students in Algebra I using hierarchical cluster analysis heatmaps. The authors also engaged in a qualitative case study that examined the ways in which school leaders made sense of the data visualization to inform improvement efforts.\n\n\nFindings\nThe outcome of this study is a framework for informing evidence-based improvement cycles using large, complex data sets. Central to moving through the various steps in the proposed framework are collaborations between researchers and practitioners who each bring expertise that is necessary for organizing, filtering and visualizing data from digital learning environments and administrative data systems.\n\n\nOriginality/value\nThe authors propose an integrated cycle of data use in schools that builds on collaborations between researchers and school leaders to inform evidence-based improvement cycles.\n","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":"93 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2021-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Learning Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ils-09-2020-0212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 5

Abstract

Purpose Currently, in the education data use literature, there is a lack of research and examples that consider the early steps of filtering, organizing and visualizing data to inform decision-making. The purpose of this study is to describe how school leaders and researchers visualized and jointly made sense of data from a common learning management system (LMS) used by students across multiple schools and grades in a charter management organization operating in the USA. To make sense of LMS data, researchers and practitioners formed a partnership to organize complex data sets, create data visualizations and engage in joint sensemaking around data visualizations to begin to launch continuous improvement cycles. Design/methodology/approach The authors analyzed LMS data for n = 476 students in Algebra I using hierarchical cluster analysis heatmaps. The authors also engaged in a qualitative case study that examined the ways in which school leaders made sense of the data visualization to inform improvement efforts. Findings The outcome of this study is a framework for informing evidence-based improvement cycles using large, complex data sets. Central to moving through the various steps in the proposed framework are collaborations between researchers and practitioners who each bring expertise that is necessary for organizing, filtering and visualizing data from digital learning environments and administrative data systems. Originality/value The authors propose an integrated cycle of data use in schools that builds on collaborations between researchers and school leaders to inform evidence-based improvement cycles.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过数据密集型伙伴关系支持循证改进周期的初始工作
目前,在教育数据使用文献中,缺乏考虑过滤、组织和可视化数据以告知决策的早期步骤的研究和实例。本研究的目的是描述学校领导和研究人员如何可视化并共同理解来自美国特许管理组织中多个学校和年级的学生使用的共同学习管理系统(LMS)的数据。为了理解LMS数据,研究人员和从业人员建立了合作伙伴关系,组织复杂的数据集,创建数据可视化,并围绕数据可视化进行联合意义分析,开始启动持续改进周期。设计/方法/方法作者使用分层聚类分析热图分析了n = 476名代数I学生的LMS数据。作者还参与了一个定性案例研究,研究了学校领导如何理解数据可视化,从而为改进工作提供信息。研究结果本研究的结果是一个框架,为使用大型复杂数据集的循证改进周期提供信息。研究人员和实践者之间的合作是完成拟议框架中各个步骤的核心,他们每个人都带来了从数字学习环境和管理数据系统中组织、过滤和可视化数据所必需的专业知识。原创性/价值作者提出了一个基于研究人员和学校领导之间合作的学校数据使用的综合周期,以告知基于证据的改进周期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Information and Learning Sciences
Information and Learning Sciences INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
9.50
自引率
2.90%
发文量
30
期刊介绍: Information and Learning Sciences advances inter-disciplinary research that explores scholarly intersections shared within 2 key fields: information science and the learning sciences / education sciences. The journal provides a publication venue for work that strengthens our scholarly understanding of human inquiry and learning phenomena, especially as they relate to design and uses of information and e-learning systems innovations.
期刊最新文献
A critical (theory) data literacy: tales from the field Toward a new framework for teaching algorithmic literacy Promoting students’ informal inferential reasoning through arts-integrated data literacy education The data awareness framework as part of data literacies in K-12 education Learning experience network analysis for design-based research
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1