Data-Driven School Improvement and Data-Literacy in K-12: Findings from a Swedish National Program

Robert Hegestedt, Jalal Nouri, Rebecka Rundquist, U. Fors
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Abstract

Data-driven school improvement has been proposed to improve and support educational practices, and more studies are emerging describing data-driven practices in schools and the effects of data-driven interventions. This paper reports on a study that has taken place within a national program where 15 schools from 6 different municipalities and organizations are working at classroom, school and municipality levels to improve educational practices using data-driven methods. The study aimed at understanding what educational problems teachers, principals and administrative staff in the project aimed to address through the utilization of data-driven methods and the challenges they face in doing so. Using a mixed-methods design, we identified four thematic areas that reflect the focused problem areas of the participants in the project, namely didactics, democracy, assessment and planning, and mental health. All development groups identified problems that can be solved with data-driven methods. Along with this, we also identified five challenges faced by the participants: time and resources, competence, ethics, digital systems and common language. We conclude that the main challenge faced by the participants is data literacy, and that professional development is needed to support effective and successful data-driven practices in schools.
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数据驱动的学校改进和K-12的数据素养:来自瑞典国家项目的发现
数据驱动的学校改进已被提议改善和支持教育实践,越来越多的研究正在出现,描述学校中的数据驱动实践以及数据驱动干预措施的效果。本文报道了一项在国家项目中进行的研究,来自6个不同城市和组织的15所学校正在课堂、学校和市政层面开展工作,以使用数据驱动的方法改进教育实践。这项研究旨在了解该项目中的教师、校长和行政人员希望通过使用数据驱动的方法来解决哪些教育问题,以及他们在这样做时面临的挑战。使用混合方法设计,我们确定了四个主题领域,这些领域反映了项目参与者关注的问题领域,即教学法、民主、,评估和规划以及心理健康。所有开发小组都确定了可以用数据驱动方法解决的问题。除此之外,我们还确定了参与者面临的五个挑战:时间和资源、能力、道德、数字系统和通用语言。我们得出的结论是,参与者面临的主要挑战是数据素养,需要专业发展来支持学校中有效和成功的数据驱动实践。
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来源期刊
自引率
0.00%
发文量
352
审稿时长
12 weeks
期刊介绍: This interdisciplinary journal focuses on the exchange of relevant trends and research results and presents practical experiences gained while developing and testing elements of technology enhanced learning. It bridges the gap between pure academic research journals and more practical publications. So it covers the full range from research, application development to experience reports and product descriptions. Fields of interest include, but are not limited to: -Software / Distributed Systems -Knowledge Management -Semantic Web -MashUp Technologies -Platforms and Content Authoring -New Learning Models and Applications -Pedagogical and Psychological Issues -Trust / Security -Internet Applications -Networked Tools -Mobile / wireless -Electronics -Visualisation -Bio- / Neuroinformatics -Language /Speech -Collaboration Tools / Collaborative Networks
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