There have been many reforms in schools throughout history aimed at improv- ing quality and thereby enhancing the learning of the students. In recent dec- ades, many efforts have been made to implement data-driven methods in schools to improve the quality of education. There are some studies that show that this can lead to significant improvements in students’ academic achieve- ment, while other studies show mixed results. The aim of this thesis is to in- vestigate how data can be used by teachers, principals, and district adminis- trators to improve quality in education. The aim was accomplished by study- ing how data-driven projects were planned and implemented in a number of K-12 schools in Sweden. The schools were part of a three-year-long research and development program facilitated by an independent research institute, Ifous. The planning and implementation process was investigated using a case study and mixed methods. Multiple sources of data, including interviews and project plans, were collected and analyzed using thematic analysis.

Results show that data-driven decision-making can lead to insights that could not be achieved without frequent and systematic data collection. The thesis also concludes that there are a number of factors that influence the implemen- tation process of data-driven methods, namely data collection and analysis, frequency, anonymity, involving students, and organizational changes. There are also a number of challenges that schools face in their planning process: time and resources, competence, ethics, digital systems, and common lan- guage. Among these, the main challenge was shown to be competence in data literacy among teachers and school staff, and there is a need for professional development in this area in order to create the conditions necessary for suc- cessful projects. Another conclusion is that schools should not only use data in temporary projects, as this needs to be part of their daily work in their effort to achieve continuous improvement. To accomplish this, there is a need to build a capacity for using data, which includes data systems, processes, organ- izational changes, and professional development.

The main contributions of this thesis are: 1) enabling an understanding of the necessary conditions for a successful implementation of data-driven decision- making (DDDM); 2) contributing to the previously developed framework for data literacy; 3) exploring how data-driven methods can be used to enhance democratic values among students as a part of school development; 4) inves- tigating the role of a common language in school improvement; and 5) identi- fying the lack of models for collaboration between micro, meso, and macro levels (classroom, school, and district) within the school organization.

Data-driven methods are being implemented in many schools around the world to improve education. In this study, two schools were studied to investigate how they implemented datadriven methods for the monitoring and improvement of the well-being of their students. These schools were part of a Swedish national program where 15 schools participated to use data on both classroom, school, and system levels for school improvement. We identified five factors that influenced the implementations, namely data collection and analysis, frequency, anonymity, involving students, and organizational changes. We conclude that continuous and frequent data collection provided insights on students´ well-being that cannot be achieved without systematic data collection. Since this kind of data collection can be time-consuming, dedicated digital tools can be used to automate data collection and analysis. These tools can also provide a better basis for decision-making since it is easier to connect and visualize data. We also conclude that the European Union’s (EU) General Data Protection Regulation (GDPR) is important when using student data, and there is a need for national guidelines on how to use data securely and efficiently in schools.

Data-driven school improvement has been proposed to improve and support edu-cational 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 six 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 method 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, digi-tal 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.