In an increasingly digitized world, lacking data literacy skills can lead to falling behind in both personal and professional spheres. We outline a modular framework for data literacy education, inspired by a qualitative analysis of 28 interviews across business, academia, and civil society: key shortcomings in the current structure of digital education come to light, such as the neglect of varied prior knowledge, lack of personalization, and disconnection from real-world problems. Our solution, as part of a forward-looking heuristic approach, is a highly flexible, modular learning resource system for creating personalized learning paths, applicable to both new and existing courses. Challenges like developing personalization algorithms and ensuring content relevance can be met with AI customization and interdisciplinary integration. In addition to existing approaches to modularization, which mainly focus on content modularization, we propose a modular learning resource system based on the metadata of the learning resources.

Keywords: Data literacy, learning resources, modularization.