Knowledge and deep learning: an investigation of semantic structures in the Norwegian mathematics curriculum

Within broader neoliberal school reforms, the national curriculum has also changed its character, with a new emphasis on generic competencies and skills. The current national curriculum framework in Norway, LK20, presents an ambiguous view of what students should learn in school. On the one hand, students are expected to acquire generic skills and competencies, and on the other, the concept of deep learning has been introduced, with the aim that students should acquire deeper and more specialized disciplinary knowledge within the framework of subjects and subject concepts. This creates confusion about what kind of knowledge students should learn in school, and how the goal of in-depth learning can be achieved in this context. In this paper, we examine the organizational principles of the Norwegian mathematics curriculum and assess the degree to which it supports deep learning and cumulative knowledge building. Drawing on Karl Maton’s semantic dimension within Legitimation Code Theory, we analyse how the curriculum objectives distribute knowledge along semantic gravity and semantic density. Our analysis shows that most mathematics objectives rely on knowledge categories that insufficiently promote in-depth learning. We conclude by discussing the implications of these findings.