{"title":"通过数据密集型伙伴关系支持循证改进周期的初始工作","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":"{\"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}","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}
Supporting the initial work of evidence-based improvement cycles through a data-intensive partnership
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.
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.