Licentiate seminar: Robert Hegestedt
Seminar
Date: Tuesday 22 April 2025
Time: 13.00 – 15.00
Location: Room L30, DSV, Borgarfjordsgatan 12, Kista
Welcome to a licentiate seminar at DSV! Robert Hegestedt presents his licentiate thesis on how data-driven methods can be implemented in Swedish schools.
On April 22, 2025, Robert Hegestedt will present his licentiate thesis at the Department of Computer and Systems Sciences (DSV), Stockholm University. The title of the thesis is “Data-Driven School Improvement: A study on how data-driven methods can be planned and implemented in Swedish K-12 schools”.
Respondent: Robert Hegestedt, DSV
External reviewer: Pernilla Josefsson, Södertörn University
Examiner: Rahim Rahmani, DSV
Chair: Paul Johannesson, DSV
Main supervisor: Jalal Nouri, DSV
Supervisor: Uno Fors, DSV
The defence takes place at DSV in Kista. It starts at 13:00 pm.
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Abstract
There have been many reforms in schools throughout history aimed at improving quality and thereby enhancing the learning of the students. In recent decades, 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 achievement, while other studies show mixed results.
The aim of this thesis is to investigate how data can be used by teachers, principals, and district administrators to improve quality in education. The aim was accomplished by studying 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 implementation 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 language. 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 successful 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, organizational 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. Investigating the role of a common language in school improvement
5. Identifying the lack of models for collaboration between micro, meso, and macro levels (classroom, school, and district) within the school organization.
Last updated: April 3, 2025
Source: Department of Computer and Systems Sciences, DSV