greenboard with chalks and coordinate system, x axis is time and y axis knowledge

Doctoral project: Personalized mathematics teaching – the use of data analysis as a support tool

This dissertation project intends to investigate how mathematics teaching can be supported and enhanced through data analyses of user data, which are generated in connection with the use of interactive digital teaching materials.

Project information

Doctoral student
Rebecka Rundquist
Italo Masiello and Kristina Holmberg
Aug 2019–Aug 2024
Pedagogy (Department of Pedagogy and Learning, Faculty of Social Sciences)
Educational technology (computer and information science, Department of Computer Science and Media Technology, Faculty of Technology)

More about the project

We are using more and more digital teaching materials within education. Therefore, research needs to be carried out in order to investigate what opportunities this entails. This is particularly relevant in regard to Covid-19.

When students use digital learning materials, user data is generated. This data can be analyzed, which can tell us something about the students' learning. These data analyses can, for example, generate learning progression curves which can be used to form something called learning trajectories that illustrate and visualize students' learning.

The project will primarily focus on the teacher's role in mathematics teaching in connection with the implementation of the use of data analysis in teaching, as well as the teaching opportunities that arise with implementation. A central question is whether learning trajectories can enable more personalised teaching in mathematics.

The thesis is a combined dissertation with four different types of studies, all of which will be within the discipline of pedagogy. The studies that will be carried out are the following:

  1. Review study.
  2. Survey studies of expectations, attitudes, understanding and needs regarding data analysis in mathematics teaching.
  3. Longitudinal study of learning trajectories in connection with digital teaching materials in mathematics teaching.
  4. Implementation of optimal learning trajectories in mathematics teaching to individualize teaching.

The project is part of Linnaeus University's research group EdTechLnu, which conducts research in the field where education meets technology, i.e. educational technology.